The application of autonomous systems plays a crucial role in ensuring the safe and stable operation of industrial processes. Control methods grounded in artificial intelligence (AI) present new prospects for this application. However, the implementation of AI-based control methods typically depends on complete observation data. When unobservable states exist, it becomes challenging to achieve autonomous control. On the other hand, the process generally operates under dynamic working conditions, and the model needs to be updated in time to avoid model mismatch, which further increases the difficulty of autonomous control. To address these challenges, an autonomous control method based on a joint spatial-temporal model (AC-JSTM) is proposed to achieve autonomous control of unobservable point states of industrial processes under dynamic working conditions. Specifically, to solve the problem of model mismatch and poor control effect caused by the dynamic conditions of industrial processes, a condition identifier based on orthogonal test design (CI-OTD) is first proposed. The data set is constructed through typical condition parameter design, and the conditions are distinguished based on the correspondence between data distribution and condition labels. Then, considering the spatial distribution characteristics between unobservable and observable points and the dynamic correlation of observable points in the time dimension, a JSTM is established. The features of spatial and dynamic dimensions are integrated by a joint training method to achieve the prediction of unobservable points based on observable points. Finally, combining CI-OTD and JSTM, a dynamic working condition control (DWCC) framework is established to achieve autonomous control of unobservable points under dynamic working conditions. To verify the superiority and effectiveness of the proposed method, control experiments are designed for both catalytic rods and tubular reactors. The results show that the proposed method can achieve accurate control of unobservable points under dynamic conditions, and the control accuracy is improved by 18.69% compared with the control framework based on the spatial-temporal model.

Accurate odometry is crucial for mapping and localization of autonomous vehicles in unknown environment. Deep neural networks have shown significant promise in self-supervised odometry, enabling pose estimation from consecutive sensor inputs. However, existing self-supervised visual odometry methods face scale ambiguity issue due to the inaccuracy of relative depth estimation, and self-supervised LiDAR odometry methods suffer from sparse data and the difficulty of correspondence searching. To address these challenges, we propose Self-FO, a self-supervised multi-sensor fusion odometry framework that effectively integrates image and point cloud data and overcomes the limitations of single-sensor methods. First, we utilize the inferred depth map as the fusion medium for image and point cloud, and realize multi-layer visual-LiDAR feature extraction and fusion through a novel homogeneous and heterogeneous channel exchange strategies. Then, we introduce a feature alignment and pose refinement module to fine-tune the coarse pose. By leveraging 2D-3D correspondences and a cross-modal attention mechanism, this module guides the image and point cloud features to focus on consistent scene-level observations and aligns features adaptively, significantly improving the accuracy of correspondence searching and enhancing the robustness and consistency of multimodal feature representations. Extensive experiments on KITTI and KITTI-360 dataset demonstrate that Self-FO outperforms existing learning-based odometry methods, delivering superior performance and scalability.

Multi-modal image fusion aims to integrate complementary cues from different modalities into a single image, facilitating downstream tasks such as object detection. However, input image pairs are often misaligned due to rigid or non-rigid deformations in practical scenarios. Such misregistration introduces structural distortions and visual artifacts, reducing the reliability of the fused results and limiting their effectiveness for downstream vision applications. While existing methods demonstrate satisfactory results under specific deformation scenarios, they exhibit limited generalization to diverse and severe misregistrations. To this end, this study proposes a unified multiple-in-one flow Mamba framework for registering various image deformations and generating high-quality fused results. Specifically, a hierarchical flow Mamba is designed to model rigid and non-rigid flow fields and enhance adaptability to complex deformations by progressively refining misaligned features. To better distinguish between rigid and non-rigid types, a flow field classifier predicts rigid/non-rigid categories and provides prompts for high-level feature modulation. Furthermore, a flow-guided fusion Mamba module is developed to aggregate aligned multi-level modality features and generate fused images, while an iterative training strategy enables collaborative optimization by using fusion outputs to refine flow estimation. Experiments across three representative modality tasks demonstrate that the proposed method delivers superior fusion performance while maintaining applicability to object detection. The code will be available at: https://github.com/BOYang-pro/MOFM.

Accurate online detection or prediction of key quality variables provides critical reference information for optimizing and controlling operating variables in industrial processes. However, frequent fluctuations in raw material properties and environmental conditions often give rise to multiple data distribution modes within the same production process. Moreover, the inherent uncertainties and the energy-material coupling characteristics of industrial processes make it particularly challenging to uncover the underlying topological relationships among process variables. To address these issues, this article proposes a novel jointly shared-specific variational graph attention autoencoder (JSS-VGATE) model for spatial topological feature extraction and key quality variable prediction in multimode industrial processes. Specifically, a variational graph attention autoencoder is first constructed, which combines graph attention mechanisms with the variational inference architecture to adaptively learn the dynamic correlation strengths between adjacent nodes, thereby capturing complex variable interactions. Subsequently, a comprehensive loss function is designed to achieve high-fidelity extraction of representative latent feature distributions. Furthermore, a cross-mode jointly shared-specific learning framework is developed to simultaneously capture global shared features across modalities and preserve local specific features of each modality, while a learnable gated fusion mechanism is introduced to balance modality invariance and heterogeneity, thereby enhancing cross-mode information integration. Finally, the effectiveness and superiority of the proposed JSS-VGATE are validated on two representative real-world industrial datasets compared to other state-of-the-art methods.

In this article, we investigate the problem of adaptive safe critic control design for stochastic multiagent systems (MASs) subject to asymmetric state and input constraints. To systematically address asymmetric state constraints, a unified transformation function (UTF) is proposed to convert the constrained consensus control problem into the stability analysis of an unconstrained error system. In addition, a nonquadratic cost function is incorporated to address input limitations effectively. Building upon these developments, a time-varying Hamilton-Jacobi-Bellman equation (HJBE) is formulated by integrating the Bellman optimality principle with Itô's lemma, thereby accommodating stochastic disturbances and enhancing controller robustness. To improve data utilization and eliminate reliance on explicit drift dynamics, an integral reinforcement learning (IRL) algorithm is developed within this framework. Furthermore, a time-varying single-critic network is designed to approximate the solution to the HJBE and generate optimal control policies, thereby considerably reducing computational complexity. To further enhance learning efficiency and relax the persistent excitation (PE) condition, the experience replay (ER) technique is incorporated into the update process of the critic weight. Finally, two simulation examples are provided to verify the feasibility and effectiveness of the proposed approach.

Froth flotation is a complex industrial process involving multicell cascades. During the froth flotation process, a single-cell fault not only compromises its own functionality but may also propagate to adjacent cells, ultimately affecting the entire production line. Therefore, accurate and timely recognition of fault traces in the flotation process is critical for ensuring production stability. In this article, we propose a novel fault trace recognition framework using a dynamic edge weight-based cross-cell interaction graph Transformer and joint task learning. Initially, we propose a dynamic edge weighting method to update multicell node features, enhancing the model’s adaptability to dynamic industrial scenarios. Then, we introduce a cross-cell attention mechanism to decode the fault propagation path, explicitly capturing interaction-aware state features among multiple cells. Furthermore, we employ a two-stage joint task learning scheme, progressing from fault interval prediction to fault trace recognition, thereby significantly improving efficiency and accuracy. Finally, extensive experiments conducted on both benchmark datasets and real-world froth flotation processes demonstrate the effectiveness and robustness of the proposed method.

Image data serve as a vital information carrier in industrial systems, but closed-set recognition methods falter in dynamic online environments with continually emerging classes. Online learning adapts rapidly but suffers from catastrophic forgetting, while continual learning alleviates forgetting through regularization yet depends on offline training, limiting adaptability to rapid task shifts. Resource-constrained edge devices demand real-time model updates, where streaming few-shot novel classes introduce feature bias, reducing class separability and accelerating model degradation. To this end, this work proposes the Biscale Storage and bilevel contrast-based continual feature evolution learning (Bi2CFEL) for industrial online recognition. By integrating biscale storage and bilevel contrast, Bi2CFEL efficiently memorizes and captures intraclass and interclass information, enabling accurate and robust image recognition in industrial online scenarios. First, spatio-temporal biscale guided storage strategy is innovatively proposed, constructing a representative and discriminative historical storage through class prototype distance screening criterion and prototype calibration mechanism. Second, to address overfitting risks in limited storage, distribution-aware memory reactivation method reactivates old-class memory based on stored sample distributions, effectively suppressing feature drift and forgetting. Finally, bilevel contrastive dictionary learning framework optimizes high-dimensional sparse feature spaces via instance-prototype cross-level contrast mechanisms, enhancing intraclass cohesion and interclass separation to decouple old and new class features. Experimental results demonstrate that Bi2CFEL achieves state-of-the-art performance in generalization capability, forgetting rate, and overall process performance.

Modern industrial processes are highly complex and dynamically evolving, with new modes emerging due to variations in raw materials, production environments, and other factors. Traditional monitoring methods often suffer from catastrophic forgetting during updates, where old knowledge is overridden. Although continual learning offers an effective solution, existing methods tend to overprotect historical knowledge, limiting the adaptability to new modes. To address the above problems, we propose orthogonal decoupled continual dictionary learning (ODCDL), which orthogonally decouples the dictionary space into stability and plasticity subspaces. The stability space preserves representations of historical modes to mitigate forgetting, while the plasticity space allows flexible updates for learning new modes. To balance the tradeoff between stability and plasticity, we impose dynamic constraints with varying strengths on the two subspaces. This design enhances both retention of old knowledge and learning of new knowledge, ensuring accurate monitoring across all operating conditions. Extensive experiments have demonstrated that the proposed method outperforms several state-of-the-art methods, exhibiting superior continual learning capability and process monitoring performance.

The complex industrial environment and poor data quality inherent in electrolytic copper foil production often lead to inaccurate energy consumption predictors, and consequently unreliable energy optimization. Given the difficulty in further improving the accuracy of energy consumption predictors, this article proposes a high-reliability stochastic prediction-based ensemble optimization (SPEO) method for energy saving of copper foil electrodeposition. The approach begins by introducing stochastic prediction to establish a standard stochastic prediction optimization framework, wherein filtering operations are applied to mitigate the adverse effects of prediction errors on the optimization process. Subsequently, reliability analysis methods are employed to derive reliability metrics based on optimization results generated by energy consumption predictors of varying accuracy. Finally, these metrics are comprehensively incorporated to integrate multiple optimizers, along with their respective optimal result feedback, thereby enhancing the generalizability and stability of the method. Experimental evaluations using real-world data from an industrial electrodeposition process demonstrate the superiority of the proposed SPEO method.

Accurate camera calibration is essential for obtaining precise three-dimensional information of a target. However, in the froth flotation process, it is challenging to position the calibration board support surfaces correctly. This difficulty makes it hard to capture high-quality calibration images from various angles, ultimately impacting the accuracy of the calibration results. To address this issue, we propose a planar template camera calibration method based on convolutional neural networks and synthetic data augmentation. Initially, we establish a virtual imaging scene to map the relationship between the feature map and camera parameters. Then, we conduct coarse camera calibration and apply synthetic data augmentation within specific range constraints to create a comprehensive calibration dataset. After that, we develop a deep calibration network named FSC-McCNN to facilitate the correspondence between the calibration feature map and camera parameters. This network comprises feature spatial coding (FSC) and multichannel convolutional neural networks (McCNN). The proposed approach effectively compensates for changes in camera focal length and requires only a single calibration image, thereby reducing the calibration workload at flotation sites. Experiments conducted on synthetic and real flotation datasets demonstrate the effectiveness and robustness of the proposed method.

In industrial scenarios, noise interference and feature overlap often result in blurred classification boundaries, compromising the reliability of rolling bearing fault diagnosis. An adaptive distribution-aware discriminative loss (ADADL) is introduced, through which intraclass thresholds are dynamically adjusted and interclass boundaries are optimized, thereby enhancing compactness and separability in the feature space. By integrating it with the cross-entropy loss, ADADL yields marked gains in diagnostic accuracy on both the Case Western Reserve University benchmark and real-world datasets, particularly under conditions of class imbalance and high noise. Visualization analyses further confirm its ability to sharpen clustering boundaries, suppress feature overlap, and effectively mitigate blurred decision regions.

Industrial edge computing in time-sensitive, heterogeneous environments faces significant challenges in delivering deterministic, low-latency, and fair task offloading under mixed-criticality traffic. Existing solutions struggle with unpredictable delays, resource contention, and the joint optimization of offloading and scheduling. In this work, we propose a task queue mapping mechanism-based architecture that incorporates formal mixed-deterministic traffic modeling under time-sensitive networking, enabling unified computation and network resource management. The joint task offloading and traffic scheduling problem is rigorously formulated as a two-stage Markov decision process with embedded fairness constraints. To address the strong coupling and computational complexity, we develop a fairness-aware deterministic offloading and resource scheduling (FA-DORS) framework, a hierarchical multiagent deep deterministic policy gradient algorithm, enhanced by a contribution-based local reward mechanism to ensure adaptive resource allocation and fairness. Extensive experimental results demonstrate that in the proposed architecture, FA-DORS achieves lower task latency, higher completion rates, improved resource utilization, and enhanced transmission determinism while ensuring fairness in multitask offloading compared to baseline methods. In particular, FA-DORS achieves an 8.89%–39.15% reduction in average end-to-end delay and up to 39.73% reduction in average jitter under high load, compared with the baseline method.

In complex industrial production processes, accurate multivariate time series forecasting is essential for operational control, process optimization, and safety assurance. However, the inherent nonstationarity and multiscale characteristics of industrial time series present a significant challenge to achieving accurate predictions. To this end, this article proposes a novel method for multivariate nonstationary industrial time series forecasting, which integrates frequency-domain decomposition with season-trend bidirectional interaction. First, a learnable frequency-domain decomposition module is designed to decouple the input sequences adaptively into distinct components that possess different frequency properties. Second, a bidirectional gated interaction module is developed, which facilitates dynamic and controlled information exchange between the seasonal and trend encoding branches. Furthermore, a loss function incorporating trend consistency constraints is established to enhance the model's capability in capturing the dynamic morphologies of the sequences. Experimental results on multiple real-world industrial datasets demonstrate that our model outperforms existing state-of-the-art methods on three industrial datasets, thereby validating its effectiveness and practical value in forecasting complex industrial time series.

Aluminum fluoride ($\mathrm{AlF_{3}}$) is a vital industrial material and its production is highly dependent on the precise blending of fluorspar. Conventional blending approaches rely heavily on manual expertise and often prove inefficient in handling complex ore compositions and multiple constraints. In this work, the fluorspar blending task is formulated as a constrained multiobjective optimization problem that simultaneously balances economic efficiency and production stability. Distinct from traditional cost-centric models, our approach explicitly incorporates the effects of fluorspar impurities on downstream reagent consumption, thereby achieving global cost minimization from raw material to final product. A novel constrained multiobjective state transition algorithm with self-adaptive shift-based penalty function under a dual-population framework is proposed to effectively handle complex constraints arising from inventory limits, compositional requirements, and impurity content. A fully functional intelligent fluorspar blending optimization system is developed and deployed using real-world data from multiple suppliers. Experimental results demonstrate superior performance in blending quality, cost-effectiveness, and industrial adaptability, offering a scalable solution for intelligent fluorspar blending optimization in$\mathrm{AlF_{3}}$production.

In this paper, we consider the predefined-time formation control problems subjected to various constraints for heterogeneous autonomous systems. We propose a novel distributed predefined-time formation control framework, which consists of a multi-agent system (MAS) estimator layer and a local tracking controller layer. Firstly, the MAS estimator layer is designed to tackle predefined-time distributed formation optimization problems with multiple constraints. Compared to previous approaches, our method effectively address challenges posed by cost functions that are non-strongly convex or even non-convex, while significantly reducing computation time. Subsequently, the local tracking controller layer is designed to ensure that the sailing states of the autonomous system can track the optimal signals obtained by the distributed optimization estimator. Additionally, we design a robust predefined-time tracking controller for autonomous systems capable of effectively handling unknown but bounded external disturbances. A control term is further incorporated to enable the autonomous system to achieve internal obstacle avoidance. Finally, we provide illustrative examples to demonstrate the validity and efficiency of the proposed predefined-time distributed optimization scheme.

The sensor coverage problem is a typical combinatorial optimization problem, which is favored by researchers due to its wide practical applications. This article studies the sensor coverage problem, where each sensor has communication, sensing, and computing capabilities to collaboratively cover a certain area. First, we treat each sensor as a player, and establish a potential game for the sensor coverage problem, where the global objective is presented by the potential objective, and individual's utility is designed as a wonderful life utility. Second, we prove that under the established potential game, a Nash equilibrium can guarantee at least 50% optimality. Third, we design a synchronous game learning (SGL) distributed algorithm, where each player has a memory length$m$. Fourth, we prove that our designed SGL algorithm can guarantee that strategies of all sensors converge to a Nash equilibrium, and analyze its complexity. Finally, we demonstrate the effectiveness and superiority of our designed SGL algorithm by comparing with the existing representative optimization algorithms via numerical simulations. In addition, we also find that by employing our designed SGL algorithm, a tradeoff between solution quality and runtime could be achieved via adjusting players' memory length.

Modern industrial systems typically operate in a multimode environment due to continuous changes in raw materials and production schedules. Traditional static monitoring methods often struggle to accurately capture the intricate relationships between different modes, leading to model mismatches and a significant decline in monitoring performance. On the other hand, emergence of a new mode often prompts model updates that focus on the new data, potentially causing the model to forget historical knowledge, which is a phenomenon known as catastrophic forgetting. To address these issues, we propose a zero forgetting lifelong dictionary learning (ZFLDL) method for multimode process monitoring. ZFLDL integrates a low-rank basis matrix growth mechanism with an adaptive basis matrix selector, enabling effective representation of knowledge across both new and historical modes. Specifically, a novel low-rank decomposition method is first proposed to address feature redundancy in data representation with an overcomplete dictionary. This method utilizes a low-rank matrix, i.e. a “basis matrix”, as the fundamental representation unit, which can accurately capture the core data features and enhance the dictionary's representation capability. Then, to address the emergence of new modes, a zero forgetting continual learning framework based on low-rank basis matrix growth is proposed. By adaptively adding basis matrices for each new mode while freezing those associated with historical modes, the dictionary efficiently assimilates new knowledge while ensuring zero forgetting of historical knowledge. Additionally, since only the basis matrices related to the new mode are updated, this approach significantly reduces the number of parameters requiring training, thus enhancing the model's learning efficiency. Finally, an adaptive basis matrix selector is proposed. By evaluating the contribution of each basis matrix to different modes, it dynamically selects the basis matrices that best match the specific mode, ensuring accurate mode recognition and reliable process monitoring. Extensive experiments have demonstrated that the proposed method outperforms several state-of-the-art methods, exhibiting superior continual learning capability and enhanced process monitoring performance.

The state transition algorithm (STA), as an intelligent optimization method grounded in constructivist learning, has been demonstrated to be highly effective in solving complex optimization problems. However, the standard STA suffers from slow convergence, particularly in the later stages when dealing with flat landscapes. Additionally, users are required to set the maximum number of iterations based on intuition. To address these issues, an enhanced STA with guaranteed optimality is introduced. This improvement involves three key components. First, novel translation transformations (TTs), inspired by predictive modeling, are developed to generate a broader set of candidate solutions by leveraging historical data. Second, adaptive parameter control strategies are incorporated to accelerate convergence. Finally, a dedicated termination condition is designed to ensure that the algorithm converges at the optimal solution, analogous to the zero gradient condition in mathematical programming. The comprehensive experimental results validate the effectiveness and superiority of the proposed method. The source codes for ESTA and EXSTA will be publicly available athttps://github.com/tiezhongyu2005/ESTA

A visible light communication (VLC)-based sensing system is proposed for gait recognition and classification. Human-induced reflections are modeled using a time-varying channel representation, enabling the capture of gait dynamics without requiring wearable devices. A low-cost sensing module with embedded processing and multi-channel photodetectors is implemented. Filtered signals are transformed into spectral-spatial features and analyzed using deep learning models. Experiments involving ten participants across eight gait types demonstrate that contrastive and multi-scale models achieve over 98% accuracy, highlighting the potential of VLC-based sensing for unobtrusive, privacy-preserving, and real-time human gait recognition.

3-path vertex cover of networks is a typical optimization problem in network science, which has a wide range of applications. Toward a 3-path vertex cover of networks from distributed optimization, we first established a potential game to describe the 3-path vertex cover problem. Next, we analyze the inherent relationship between potential game and 3-path vertex cover, that is, only the solution to minimum value of potential function are minimum 3-path vertex covered solutions, and strict Nash equilibriums are intermediate solutions between minimum 3-path vertex covered solutions and 3-path vertex covered solutions. Then, we propose a bounded best response and memory-based distributed algorithm, and prove that our proposed algorithm can guarantee any initial solution converge to a strict Nash equilibrium, and further analyze the complexity of this algorithm. Finally, numerical simulations verify the effectiveness and superiority of our proposed algorithm on some representative networks and benchmark by comparing with existing representative algorithms. This work paves an effective way for distributed optimization that could be modeled as distributed potential game.

Vertex cover problem is a typical nondeterministic polynomial combinatorial optimization problem with wide applications. In order to seek a near-optimal solution in finite time, this article achieves the goal from the perspective of serial potential game optimization. Specifically, we present the vertex cover problem as a potential game, where the corresponding potential function minimizers are equivalent to the minimum vertex cover state. Then, we propose a novel polynomial-based serial game distributed algorithm, and prove that the algorithm can guarantee that strategies of all vertices converge to a$\epsilon$-minimum vertex cover ($\epsilon$-MVC) state in finite time. Compared with the existing representative optimization algorithms on networks and standard benchmarks, numerical simulations demonstrate our proposed algorithm can effectively balance solution efficiency and computation time. We hope this work provides insight for decision-makers designing a reasonable algorithm when solving nondeterministic polynomial optimization problems, so as to guarantee both solution quality and computation time.Note to Practitioners—Vertex cover problem has a wide range of practical applications. Many distributed optimization algorithms for the vertex cover problem are available in the existing literatures. However, those algorithms mainly pursue high-quality solutions in infinite time. Thus, one of the main challenges is to design a distributed algorithm that guarantees both solution quality and computation time. This article design a polynomial-based serial game algorithm, and prove that it can converge to the$\epsilon$-MVC state in finite time. This article can serve as a supplement to the existing optimization algorithms for solving the vertex cover problem.

Reagent addition control performance of zinc first rougher significantly affects the quality of the final product. It is common to control visual features to satisfactory feature setpoints, in order to realize satisfactory reagent addition control. However, existing methods usually use the historical data under the desired production state to generate the feature setpoint, and the historical data under the undesired production state are ignored. Moreover, the number of feed grade categories for feature setpoint generation is settled, and thus the richness of corresponding query rules for presetting the reagent addition rate is limited. In this situation, a reagent addition control strategy with a dual frequent pattern tree (dual FP tree)-based feature setpoint generator and knowledge core-based reagent fine presetting is proposed for zinc first rougher. First, a dual FP tree-based feature setpoint generator with the complementary input is constructed to fully utilize the dataset, where the main tree is designed to construct the knowledge repository and the secondary tree is established for optimization. Second, the optimized knowledge rules are taken as cores to construct an optimal operational pattern base for reagent fine presetting. Then, a designed feedback controller based on a fuzzy logic system with a three-way decision mechanism is explored to adjust reagent addition rates and try to make visual features track prescribed feature setpoints. The ablation results show effectiveness of the dual FP tree-based feature setpoint generator, reagent fine presetting and feedback controller. The comparative experimental results demonstrate the potential of the proposed control strategy. Note to Practitioners—This article is motivated by the problem of reagent addition control in the zinc flotation process. This article focuses on making visual features track prescribed feature setpoints by adjusting reagent addition rates, aiming to achieve the goal of controlling the zinc concentrate grade within its acceptable range. However, two challenges exist for control: the historical data under the desired production state are usually used during feature setpoint generation, while latent process knowledge in the historical data under the undesired production state is ignored; the number of feed grade categories for feature setpoint generation is settled during reagent presetting, which means the richness of corresponding query rules for presetting the reagent addition rate is limited. Therefore, this article suggests a reagent addition control strategy with a dual FP tree-based feature setpoint generator and knowledge core-based reagent fine presetting to address those problems. A suitable feedback controller is designed for outputting reagent addition adjustment values to qualify the concentrate grade, which is based on feature setpoint generation and reagent fine presetting. The experiments are carried out for evaluation. The evaluation shows the potential and superiority of the proposed approach in terms of decision-making and automation control in froth flotation.

The blast furnace (BF) is the most energy-consuming and emission-intensive equipment in the steel industry. Burden surface images provide reliable and concrete information that is crucial for real-time monitoring, understanding burden distribution, and ensuring efficient and green operations. However, the high-temperature, enclosed, dusty, and low-light environment within the BF results in images captured by high-temperature industrial endoscopes that suffer from unevenly illumination, abnormal light and shadow, and blurriness, limiting their application. This paper proposes a method for high-definition reconstruction of BF surface images based on a spatiotemporal multi-domain guided generative model (STMGG). This method constructs a deep image prior (DIP) network with an encoder-decoder mechanism to decompose the original burden surface image into illumination and reflectance maps across multiple domains. For the illumination maps, a multi-frequency-attention fusion block (MFAFB) extracts extreme bright and dark features from the original image, calculates the appropriate fusion weights for the illumination maps of each domain, and synthesizes an optimal distributed total illumination map to accurately restore light and shadow. For the reflectance maps, global temporal and multi-spatial domain local structural texture information guidance based on wavelet transform is introduced, along with a structural texture entropy loss, to ensure the generated reflectance maps focus on both global contours and local details, thus reducing blur. The final high-brightness, high-definition reconstruction of the BF burden surface is achieved by merging the total illumination and reflectance maps. Experimental results demonstrate that STMGG significantly enhances the quality of BF burden surface images and exhibits strong adaptability in general scene images, highlighting its broad applicability and significant practical value.

In response to the challenge of strongly nonlinear and multimode systems control, this paper introduces a weighted deep learning based adaptive predictive control method. This approach integrates LSTM networks for different operating modes using a set of weighting coefficients. These coefficients are dynamically updated during online control via an error-guided scheduling strategy to adapt to changing operation modes. Compared to offline identification based methods, the proposed method eliminates the need for mode recognition or model switching strategies and can adapt to drifted operation modes. In contrast to online methods, it achieves rapid model convergence and reduced computational cost, requiring only minimal data to update the weighting coefficients without necessitating the retraining of the LSTM networks. Theoretical convergence and stability analysis ensure the reliability of the proposed method. Numerical simulations and industrial control experiments demonstrate that the proposed approach exhibits favorable control performance across both known and drifted operation modes. Note to Practitioners—Considering the changing operation modes in complex industrial processes and the detrimental effect of slow or unstable control during system operation, this paper proposes a weighted LSTM based predictive control method for strongly nonlinear and multimode systems. Extensive experiments demonstrate that compared to other state-of-the-art methods, this method can rapidly adapt to changes in operating modes with a small amount of data, meeting both real-time and stability requirements for online control.

Modern industrial processes typically operate under variable conditions due to fluctuations in raw materials and production loads. Consequently, accurate monitoring across multiple operational modes is essential for process optimization. Although some global modeling based methods have been proposed, they struggle to adapt to the continual emergence of new modes. On the other hand, online updating monitoring methods often suffer from “catastrophic forgetting”, i.e., they lose the ability to represent historical modes. To address these challenges, this article proposes a multimode process monitoring method based on Incremental Rank Continual Dictionary Learning (IRCDL). The method integrates the incremental learning framework based on low-rank matrix augmentation with the adaptive weight selector, allowing the dictionary to retain the knowledge of both historical and new modes. Specifically, to address the issue of information redundancy in overcomplete dictionaries, an innovative dictionary construction method based on low-rank matrices weighting is proposed. Using low-rank matrices as the fundamental units for dictionary construction, this method accurately captures the intrinsic features of different modes while significantly reducing redundant parameters. Then, to handle the continual emergence of new modes, an incremental learning framework based on low-rank matrix augmentation is developed. This framework continually adds new low-rank matrices to learn representations of new modes, while keeping the historical low-rank matrices intact to maintain “zero-forgetting” of historical modes, thereby enabling accurate monitoring of multimode processes. Finally, an adaptive weight selector is designed to automatically optimize the weights of low-rank matrices, thereby enhancing the representation of different modes and significantly improving the performance of multimode monitoring. Extensive experiments have demonstrated that the proposed method achieves state-of-the-art continual learning capabilities (i.e., “zero forgetting”) and exceptional process monitoring performance.

Industrial systems often undergo dynamic changes during operation, which presents challenges for traditional identification and control methods. These challenges arise in two aspects: variations in model structure and parameters, and differences in control objectives across diverse operating conditions. Traditional static predictive control methods face challenges in meeting the high-precision, real-time requirements in practice. In addition, control schemes with fixed parameters encounter difficulties in adapting to varying control objectives, resulting in suboptimal control performance. To address these problems, this article proposes a dynamic error-triggered adaptive control method, which can identify the operating conditions and objectives in real-time. Specifically, a dynamic error-triggered model updating mechanism is first established to detect changes in operating conditions and update the prediction model. To overcome the model mismatch during the transition process, a novel enhanced transition control (ETC) method is proposed, which designs a transition error corrector to decline prediction error and a high informative pseudo-random binary sequence (HIPRBS) input to enhance the excitation level. Considering the differences in control objectives under varying operating conditions, a fuzzy weight-adaptive method is proposed to balance heterogeneous indicators in different conditions. Two types of systems, high-speed and high-stability, are designed to validate the superiority of the proposed method. Extensive experimental results demonstrate that, compared to some state-of-the-art methods, the proposed method can efficiently and accurately identify emerging operating conditions, dynamically adjust optimization objectives, and achieve real-time control effects under varying operation conditions.Note to Practitioners—The motivation of this paper is to develop a high-precision and real-time control method for industrial systems that operate under frequently changing conditions. The proposed method can adapt to changes in model parameters and control objectives in multiple operating conditions processes. Compared with some state-of-the-art methods, this method significantly enhances the control performance in the transition process of mode switching, meeting the long-term stable operation requirements of industrial systems.

Modern industrial systems possess the capacity to accumulate substantial data, thereby enabling data-driven process monitoring. However, the dynamic industrial processes give rise to new working conditions continuously. Traditional methods require substantial new data for updates, resulting in considerable delays. Furthermore, edge devices face resource limitations, which complicates the ability to meet the increasing demands for storage and computing power. On the other hand, the frequent transmission of data between the cloud and edge introduces potential security risks. To address these challenges, this paper proposes a Secure Monitoring Method based on Self-Updating Dictionary Learning (SUDL-SM) within a cloud-edge collaboration framework. Specifically, to tackle the issue of poor model adaptability caused by limited data of new modes, this paper first proposes a dictionary learning method based on multi-task hardness evaluation. By evaluating the multi-dimensional contributions of samples, those with strong generalization are extracted, and the dictionary is updated online accordingly, ensuring adaptability to both new and historical conditions. Subsequently, due to the resource constraints inherent in edge devices, a dictionary distillation compression method has been proposed. This method aims to maximize dictionary compression while preserving the original monitoring performance, thereby ensuring efficient and accurate inference on edge devices. Finally, a hybrid encryption-based cloud and edge data transmission protocol is designed to effectively address malicious activities such as data theft and tampering by ensuring reliable interaction between the cloud and the edge. Extensive experiments verified the effectiveness and superiority of the proposed method.

The paper studies the robust stability of linear uncertain systems under periodic event-triggered impulsive control (ETIC). Based on sliding variables, some sufficient conditions are derived to ensure the robust stability of the addressed impulsive control systems, and the effects of both external disturbance and uncertainty on the stability are revealed. Interestingly, it is verified that the proposed ETIC can ensure the robustness of the addressed systems with any bounded external disturbance. While, it is shown that the uncertainty may bring destabilizing impact to the stability. Further, compared with continuous event-triggered mechanism that often requires high hardware accuracy and sensor complexity in previous results, the proposed periodic event-triggered mechanism is more relaxed and realistic. It is also shown that the interevent time generated by the proposed periodic event-triggered mechanism has a unified positive lower bound, which cannot be derived by previous continuous event-triggered mechanism. Two illustrative examples are given to show the excellence of the main results. Note to Practitioners—It is known that the industrial plant emulator setup serves as a scaled-down representation of various industrial plant mechanisms, such as conveyor belts, drives, servomechanisms, and assembly machines. This paper is motivated by the problem of impulsive control for an industrial plant emulator setup, which has been studied by using continuous-time control or intermittent control in the previous results. Note that impulsive control only needs to implement at some discrete instants, which can reduce control cost and communication pressure compared with the continuous-time control and intermittent control. In this study, periodic ETIC, which contains the advantages of both periodic event-triggered control and impulsive control, is proposed to achieve the robust stability of linear multi-input uncertain systems with external disturbances. Some novel sliding-variables-based conditions have been obtained to guarantee the robust stability of addressed systems and the impacts of uncertainty and external disturbances on the stability are revealed simultaneously. It has been verified that the uncertainty may have destabilizing impact on the stability and under some conditions, the addressed periodic ETIC systems are robust with unknown bounded external disturbances. In addition, there exists a unified minimum time between arbitrary two consecutive triggered instants. In fact, the periodic ETIC can be also applied to some practical systems such as the second-order relay system and buck converter system, as these systems can be represented as linear uncertain systems with external disturbances. Furthermore, it is an intriguing future research topic to extend the derived results to the case of nonlinear systems.

The occlusion problem has consistently posed a significant challenge in the field of segmentation. Most existing segmentation methods require additional annotations and fail to capture the contour information of occluded regions, thus not truly addressing the occlusion issue. Although segmentation tasks involving particle objects also suffer from occlusion problems, the homogeneity of particle objects offers new possibilities for overcoming this challenge. In this paper, we propose an occlusion segmentation framework for particle objects that does not require additional annotations. This framework only necessitates instance-level segmentation labels to obtain complete contour information of particle objects, including occluded regions. First, we decompose the occlusion segmentation task into a generic instance segmentation task and an occlusion repair task for occluded objects. Then, in order to train the occlusion repair model with only instance segmentation-level labels, we quantitatively analyze the occlusion phenomenon, including the mathematical descriptions of occlusion relationships, degrees, and distributions. Next, we geometrically transform and layer overlay the unobscured samples to construct occlusion samples containing labeling information of the occluded regions. These sample sets are used to train a generative model that predicts the contour information of occluded regions. Finally, we fine-tune or post-process the pre-segmentation model with the particle objects containing restored complete contour information to achieve the final occlusion segmentation. We conducted extensive ablation experiments on both the ore-particle dataset and publicly available cell-particle datasets. The experimental results validate the effectiveness, accuracy, and generalizability of our method. Note to Practitioners—Particle segmentation has been faced with the occlusion problem. In this paper, inspired by the similarity between particle objects, we propose a self-supervised occlusion segmentation framework that does not require additional annotation of occlusion layers. Our approach requires only instance segmentation level annotation without more complex additional manual annotation, which is crucial for practical applications. In addition, we decouple the complex occlusion relation modeling into a binary classification problem without knowing precisely the occlusion hierarchy between particles, which further reduces the difficulty of practical applications. Then, we also propose shading transformations to characterize the inter-particle shading distribution to construct shading sample sets from existing samples. Finally, we use these learned and constructed occlusion sample sets to pre-train the generative model for regenerating the occluded objects to complete the final occlusion segmentation. Although in a generic segmentation task, our approach may have some limitations because the segmented objects may not have an apparent similarity. However, our approach using self-supervision and the objects’ properties provides valuable ideas for solving the occlusion problem. In the future, we will solve the occlusion problem regarding the properties of each class of objects rather than just considering the similarity among particle objects.

Accurate prediction of reaction temperature in rotary kiln is essential to realize its advanced process control and operational optimizations. However, the complexity of the physical and chemical reactions in the rotary kiln makes it difficult for the traditional mechanism model to characterize the dynamic kiln process. In this study, a deep learning-based temperature prediction model is proposed to accurately track the temperature changes during the production process of rotary kiln. The proposed model integrates a temporal convolutional network (TCN) with a long short-term memory (LSTM) network. The former enables the proposed model to be aware of local context and can effectively compute local characteristics, whereas the latter with its time-memory capability can better capture the long-term dependencies of data to extract deep-level features. To enhance the prediction accuracy, the proposed model is further improved by introducing a novel feature fusion and hybrid pooling layer to merge the original input with TCN output features, which efficiently preserve the detailed information and significantly reduce model complexity. Attention mechanism is also incorporated after the LSTM network to concentrate on the key moment information and improve the model performance. Monitoring data from a zinc rotary kiln at a field site is used for model training and testing. Results demonstrate that the proposed model can achieve the best mean square error, 0.165, exhibiting a promising prediction accuracy of the rotary kiln’s temperature. It outperforms the state-of-the-art machine learning-based prediction models such as LSTM, Gated Recurrent Unit (GRU) and TCN.Note to Practitioners—This work considers high-accuracy prediction of the temperature changes during the production process of rotary kiln. It is critical to well predict kiln tail temperature by capturing long-term dependencies and extracting global and local features from time series data. Yet, this is an extremely challenging task because rotary kiln operation is a complex thermal process with multivariate, pure hysteresis, strong interference, nonlinearity and strong coupling characteristics. As a result, these issues inhibit the establishment of a model that can accurately characterize the production process of the rotary kiln and impedes the realization of automation in volatile kiln production. Current prediction methods usually adopt mechanism-based and data-driven, there are suffer from long computation time, unknown kinetic parameters and poor accuracy. Thus, they fail to effectively learn features and accurately establish model. This work proposes a temperature prediction dynamic model named enhanced TCN-LSTM, which combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, incorporating a unique feature fusion technique along with a hybrid pooling layer and attention layer. This integration enables the model to extract both global and local features, thereby preserving detailed information efficiently while significantly reducing model complexity and enhance the stability and robustness. Experimental results based on the zinc rotary kiln dataset demonstrate that the proposed model exhibits a promising prediction accuracy of the rotary kiln’s temperature. In the future, it can be readily implemented and applied in many industrial areas such as systems modeling, time series forecasting, and predictive maintenance.

During the operation of a distributed parameter system (DPS), its working conditions typically undergo dynamic changes. Although online learning methods can enable models to adapt to new working conditions to some extent, they often confront the “catastrophic forgetting” problem, where the updated model forgets historical working conditions. On the other hand, only a few new samples can be collected during online operation, and the sparse samples make it difficult to establish accurate models for new working conditions. Therefore, achieving precise control under full working conditions remains a challenging problem. To address these challenges, this paper proposes an adaptive predictive control method based on continuous learning that achieves stable control under full working conditions by continuously identifying working conditions and triggering adaptive model updates in real time. Specifically, a spatial-temporal feature-based working condition identification method is first proposed to identify changes in working conditions automatically. Then, to address the challenge of limited data for model updating, a parameter transfer method is proposed. Simultaneously, to ensure that the updated model retains the ability to characterize historical working conditions, an Elastic Weight Consolidation (EWC) constraint is incorporated into the loss function, thus overcoming the catastrophic forgetting problem, and ensuring the updated model can represent both the historical and new working conditions. Finally, by incorporating this condition identification mechanism and adaptive predictive model into the model predictive control framework, continuous precise control of DPS can be achieved. To demonstrate the superiority of the proposed method, extensive experiments are designed. Experimental results show that the proposed method can accurately identify new working conditions and learn new condition models with only a few online samples while overcoming model mismatch of historical conditions, ultimately achieving high-precision control under full working conditions.Note to Practitioners—Motivated by the fact that DPS often operates in different conditions and online learning methods confront “catastrophic forgetting” and “sparse sample training” problems, this paper proposes an adaptive learning control method. The proposed method can learn new working condition features with small online samples while retaining the ability to represent historical working conditions, and thus achieves accurate control under full working conditions.

This paper addresses the fault isolation, estimation, and fault-tolerant control scheme for the aircraft electric anti-skid braking system (EABS) in the presence of actuator and sensor faults. First, the inherently nonlinear dynamics of EABSs are represented by a Takagi-Sugeno (T-S) fuzzy model, incorporating immeasurable antecedent variables to capture the time-varying characteristics. Second, based on the output equivalence principle, a fuzzy observer with unmatched antecedent variables is proposed to achieve isolation and estimation of actuator and sensor faults. The designed observer can guarantee the sensitivity to specific faults while enhancing the robustness to disturbances. The estimated fault information is then utilized to develop a fault-tolerant control strategy, ensuring effective fault compensation and tracking performance. Subsequently, the design of separate and integrated frameworks for the estimation and control units is considered, taking their interaction into account to achieve state and fault isolation, estimation, fault compensation, and tracking control. Finally, hardware-in-the-loop experimental results verify the effectiveness and real-time performance of the proposed fault isolation and fault-tolerant control method, demonstrating the practical applicability of the proposed framework. Note to Practitioners—The aircraft anti-skid braking system (ABS) is crucial for ensuring the safety during landing, taxiing, and other ground movements. This paper focuses on developing reliable fault isolation and fault-tolerant control strategies to maintain ABS performance and efficiency in the presence of faults. The proposed approach employs a fuzzy model to analyze the effects of various faults on system outputs, enabling precise fault isolation and estimation for simultaneous multiple faults. The reconstructed fault information is then integrated to enhance the fault-tolerant control mechanism. This ensures that braking performance can be maintained, even in the presence of multiple simultaneous faults, thereby enhancing system robustness and safety. Moreover, the proposed strategy holds potential applications in other safety-critical domains, such as rail transportation and aerospace vehicles. Future research will explore the integration of historical data to further enhance the accuracy of the fault diagnostic and accommodation units.

This paper proposes a novel filter-based model-free reinforcement learning (RL) event-triggered control (ETC) method for the fully distributed robust leaderless cooperative output regulation (COR) of unknown heterogeneous multi-agent systems (MASs) with external disturbances over directed graphs. First, the fully distributed event-triggered observers are designed to generate an autonomous system for the robust leaderless COR, in which the frequency of signal transmission and computational burden are significantly reduced, and the Zeno behavior is strictly ruled out. Then, a filter-based model-free RL algorithm without integration operation is developed to obtain the solution of the internal model-based augmented algebraic Riccati equation (AARE) and to release the requirement of recording complete and continuous data. Moreover, with some adaptive parameters, the robust leaderless COR is solved in a fully distributed manner without involving any global information of directed MASs. Finally, simulation results on RLC circuits are illustrated to show the feasibility and effectiveness of the proposed control scheme.

Deep-learning-based structured light 3D reconstruction technology (SL3D) provides excellent solutions for intelligent manufacturing. However, the scarcity of real-world datasets covering full-process data and diverse objects hampers the validation of new ideas. Moreover, limited research on dataset construction strategies, such as scene backgrounds and sample distribution, reduces network performance. We investigate the impact of background stability on foreground accuracy (BS-FA) and find a whiteboard background improved foreground prediction accuracy by up to 82% over a black background. Guided by BS-FA, we develop the SL3D-BF, a background-effective SL3D dataset for industrial use, featuring approximately 2,100 scenes with diverse objects like metal/plastic workpieces, plaster sculptures, and standard parts for precise evaluation. It uniquely includes shadow and foreground masks, absent in prior datasets, and offers full-process data from gratings to 3D point clouds, totaling 100,800 gratings. We also establish an initial benchmark for future research by conducting evaluation experiments with advanced methods. Furthermore, we investigate the relationship between the spatial frequency of sample occurrence and the model predictive ability to minimize the time and resource demands of dataset construction. Most importantly, SL3D-BF is also a valuable resource for tasks like depth estimation, defect detection, and semantic segmentation. The dataset is available at: https://github.com/LiYiMingM/Dataset_SL3D_BF.

Accurate estimation of burden surface depth plays a crucial role in constructing the temperature field and optimizing reaction control in volatile kilns. However, most image-based depth estimation techniques require high-quality input images and achieve limited accuracy, which restrict their applications in actual harsh working conditions such as high temperature, heavy dust and dense smoke. In this study, a deep learning-based monocular depth estimation model is proposed to measure the burden surface depth in the volatile kiln head zone. The proposed model integrates an encoder-decoder network with an attention module. The encoder-decoder network outputs a set of deep semantic features, while the attention module intelligently fuses multi-level features to predict a probability distribution over depth intervals for each pixel. A volatile kiln prototype is designed and constructed to generate image datasets of the kiln head zone which approximate real data collected from industrial production sites. Results demonstrate that the proposed model has a depth prediction error of RMSE = 11.008 mm for the burden surface region, outperforming state-of-the-art neural networks and the traditional depth-from-defocus method. Code and datasets are available athttps://github.com/LLLcong/Attention-MonoDepth.

Complex industrial processes exhibit typical nonstationarity due to frequently fluctuating material flows and complex control loops. This poses three challenges for trustworthy process monitoring, including data drift, coordination of old and new knowledge, and interpretability. In this study, the adaptive and interpretable process monitoring problem is formulated as an online updating strategy and the spatial topology structure representation learning process monitoring problem. An incremental variational graph attention autoencoder with probabilistic inference framework is proposed, which aims to effectively learn continuously from dynamically changing industrial data to make interpretable monitoring results. First, an incremental learning strategy based on the Bayesian regularized self-organizing map is presented, which can distinguish between real faults and time-varying changes. Once normal samples are encountered, the itself and downstream model are elegantly updated with a dynamic down-sampling replay strategy without leading to catastrophic forgetting. Subsequently, a variational graph attention autoencoder with probabilistic inference is proposed, which endows interpretable spatial structural relationships through priors and effectively captures the variability of spatial latent representations suitable for nonstationary processes. Then, an incremental variational Bayesian inference is introduced to calculate the adaptive thresholds to adapt the system. In addition, an anomaly-aware graph attention localization mechanism is provided to localize fault root causes and propagation paths. Finally, the effectiveness of the proposed method is validated through two industrial applications. The results demonstrate that the proposed method can significantly enhance the performance of process monitoring, especially for reducing the false alarm rate (FAR) in process monitoring schemes. Moreover, it offers interpretable causal relationships among faults.

This article considers the optimal consensus control for the multiagent systems problem. By developing the multiagent multistep Q-learning (MaMsQL), the methodology achieves enhanced efficiency while addressing the issue of the complex interaction dynamics between agents, environmental uncertainty, thus ultimately meeting demand of balancing exploration and exploitation. First, associated with the performance index, the Q-function is established to prove that all optimal Q-functions form a Nash equilibrium outcome, thereby the consensus problem is converted to finding the optimal Q-functions. Then, the MaMsQL method is developed with theoretical proof of its convergence. Finally, the method is implemented through a specially designed Actor-Critic network. By virtue of the comparison with multiagent single step Q-learning, the effectiveness and superiority of this method are verified through simulation examples.

In industrial soft-sensor applications, labeled samples are often scarce and unable to fully represent the dynamic changes in industrial processes. Although semi-supervised methods offer a potential solution to this issue, existing feature-construction-based methods cannot ensure the effectiveness of the feature, and pseudo-label-based methods lack an established confidence evaluation standard. To address these challenges, this article first proposes a novel performance-driven distillation strategy, which designs an innovative siameseLSTM structure for training multiple teacher models. By assigning higher weights to high-performance teacher models and simultaneously leveraging the guidance of the soft sensing task, the student model is guided to learn more effective feature representations. Additionally, a new pseudo label confidence evaluation strategy is introduced, which aims to enhance the generalization of the base soft-sensor model by selecting samples with high-confidence pseudo labels. Finally, By combining the above two strategies, a semi-supervised soft-sensor framework is proposed for the soft sensing of industrial quality variables. The effectiveness of the proposed framework is validated through two real-world datasets from different stages of the alumina production process. Compared with some existing advanced soft sensor frameworks, the prediction results on different datasets show that the root-mean-square error (RMSE) and mean absolute error (MAE) are reduced by an average of 10.76% and 11.18%, respectively, while the correlation coefficient (R2) is averagely increased by 0.1203.

Key performance indicator (KPI) reflects the quality and efficiency of manufacturing operations, and KPI forecasting enables proper operations or controls in many industrial processes. However, existing KPI forecasting methods are inadequate for managing the advance prediction of KPI across multiple cycles effectively, which impedes precise and timely control in complex industrial processes. Therefore, we propose an adaptive encoder-decoder framework with partial teacher forcing strategy (PTF-ED) to enable flexible multihorizon KPI forecasting. First, we employ an encoder that processes the input time series and an attention layer to generate the context vectors. Then, we divide the measured KPIs into delayed and current time series, and design a delayed decoder and a current decoder in series to make the delayed and current time series correspond to the input time series. Especially, we propose a partial teacher forcing strategy to utilize the measured KPI efficiently and tackle the challenge of exposure bias in the current time series between training and inference phases. Moreover, we introduce a weighted multihorizon forecasting constraint in the model training loss to constrain the input-output correspondence across different sample intervals. The effectiveness of the proposed model has been validated through both a numerical simulation study and a case study in a real-world zinc flotation process.

Complex systems are frequently influenced by uncertain factors, making it difficult for traditional fixed-model control schemes to achieve high-precision control. Data-driven control methods offer a solution, but they face challenges in constructing accurate models due to insufficient excitation in operational data. Moreover, mismatches between historical models and new conditions coupled with limited data accumulation under new conditions reduces the operational performance throughout the entire process. To address these issues, this paper proposes a generalized integrated fuzzy model predictive control (GIF-MPC) framework. It combines the generalization capability of fuzzy control with the precision of model predictive control to ensure highprecision control under all conditions. Specifically, a strategy switching mechanism, triggered by a mismatch characteristic parameter is first proposed, which transitions the original strategy to a fuzzy-driven excitation control method, thereby mitigating the control performance degradation caused by the mismatch between control strategies and complex systems. Then, a fuzzy control feature extraction method is proposed to balance fuzzy set activation and improve adaptability to unknown conditions. Additionally, an optimal input excitation design method is proposed to tackle insufficient data excitation, enabling effective control. Once sufficient data is accumulated, the model switches to model predictive control. The dual decision mechanism guided by the data information and triggered by the mismatch characteristic parameter effectively ensures high precision control under uncertainties. Numerical experiments demonstrate that the GIF-MPC method ensures high-precision control throughout disturbances and condition changes. The solution is also successfully deployed in an industrial setting, validating its excellent control performance under full operation conditions.

This study combines the fuzzy logic and the data-driven technology to solve the reinforcement learning-based sliding mode control problem of interconnected nonlinear systems with unmodeled dynamics. By assigning cost functions associated with the sliding-mode function for all auxiliary subsystems, the original control problem is equivalently converted into designing a group of optimal control policies updating in a self-triggered manner. To derive the optimal policies, a single-critic network architecture under the framework of reinforcement learning is constructed. Meanwhile, a fuzzy logic-based control policy is designed to handle the dynamical uncertainty issue aroused from the unmodeled dynamics. Furthermore, a data-driven method is used to reconstruct the unknown system dynamic through a three-layer neural network. Eventually, effectiveness and superiority of the proposed control strategy are demonstrated via experiments on optimal control of an interconnected two-stage chemical reactor system.

Accurate and reliable power generation forecasts of offshore wind farm clusters are crucial for the low-carbon operation of multienergy power systems. In practice, measurement data may not always be complete due to various failure issues in data acquisition systems or communication interruptions in harsh marine environments, and missing essential data may significantly reduce the credible prediction accuracy of probabilistic models. To address this problem, this article proposes a novel missing-data tolerant model based on confidence-triggered fuzzy clustering quantile-enhanced transformer (CFCQET). First, a quantile-enhanced transformer-based multistep wind power probabilistic forecasting method is developed, where the predicted values are iteratively updated by conditional confidence expectations. Then, based on the spatio-temporal characteristics of wind farms, a FCM clustering model for offshore wind farms is constructed to divide wind farms with similar power curve attributes for joint modeling. Next, a confidence-triggered strategy is designed for probabilistic power forecasting with missing data under wind farm clusters, where the output interpolated predicted values are used to fill in unobserved input data. Finally, probabilistic prediction tests for twelve offshore wind farms at a time resolution of half an hour. The test results demonstrate that the CFCQET achieves a lower negative form of the continuous ranking probability score (CRPS*), as well as superior sharpness and comparable reliability of the prediction intervals with respect to the benchmarks.

Reliable interval forecasting of uncertain load with cyberattacks is critical for grid resilience and decision-making. Nevertheless, various constraints on the predictive distribution assumptions are introduced into existing probability prediction models, limiting the model's capability to approximate the actual distribution. Moreover, load forecasting involving cyberattacks may lead to inappropriate decision-making for power transmissions. To this end, a novel cybersecure distribution-free learning model is devised for load forecasting under cyberattacks. First, an isolation forest is implemented to counter load anomalies with cyberattacks automatically by unsupervised learning. Second, an attention-assisted gated recurrent unit embedded in the developed model is employed to extract long- and short-term temporal dependencies of load time series. Then, an optimal prediction interval (PI) construction strategy based on conformal inference is put forward to eliminate the quantile crossing rates, which approximates the actual cumulative distribution function of load data and attains effective marginal coverage. Finally, numerical experiments have demonstrated that the model surpasses existing models in both PI width and cybersecurity.

Accurate and stable feed-grade monitoring is essential for flotation and reagent control. Although some monitoring models for feed grade have been developed in recent years, they always use a group of time-series data with a fixed time step as input, and neglect the roles of models at different training steps. Therefore, to enhance the stability and accuracy of the feed-grade monitoring model, we propose a feature-ensemble model with an adaptive self-ensemble module. First, we use multiple groups of input vectors with different time steps as inputs to the monitoring model. Then, we introduce an adaptive self-ensemble module (ASE module) to fully use the models at different training steps with an adaptive adjustment mechanism and a self-ensemble module. After that, we construct a feature-ensemble model (FE model) embedding the ASE module to handle the multiple time series with different time steps. Effectiveness of the proposed monitoring model is validated both on a numerical example and an industrial example in froth flotation. In the numerical example, the root mean squared error (RMSE) of the three FE models with the ASE module decreases by 0.0090 to 0.0159, and the R-squared ($R^{2}$) score increases by 0.0013 to 0.0035, compared with the three single models. In industrial application, the RMSE of the three FE models with the ASE module decreases by 7.20% to 9.70%, and the$R^{2}$increases by 6.71% to 7.34%, compared with the three single models. This shows our framework can improve the feed-grade monitoring performance.

In [1], to maintain the integrity of the publication and uphold academic standards, a correction is requested for an identified error. Fig. 12 in the article is a duplicate of Fig. 13 due to an inadvertent mistake during the final stages of manuscript preparation.

Current deep learning methods for strip steel defect detection prioritize accuracy and computational efficiency but struggle to generalize to new production environments with unseen defect types. While visual foundation models like segment anything model (SAM) offer broader generalization through pretrained visual knowledge, they face significant domain shifts when applied to industrial defect imagery. To address this, we propose AdaptedSAM, a parameter-efficient few-shot fine-tuning framework for SAM that optimizes adapter placement and feature interaction. We first demonstrate that adapter positioning within the encoder critically impacts few-shot adaptation performance. Building on this, we introduce an efficient global optimizer that dynamically allocates adapter weights via cross-block communication across transformer layers. Furthermore, we develop an adapted mask decoder to enhance detailed mask information. Our adaptedSAM achieves state-of-the-art defect detection with precise boundaries in five-shot learning, outperforming 12 state of the art (SOTA) models on the SD-saliency-900 steel dataset while ensuring computational efficiency. Cross-domain evaluations on six benchmarks (industrial defects, remote sensing, medical imaging, natural scenes) demonstrate robust generalization.

Shape shift refers to the phenomenon where by amplitude and scaling of samples of the same fault vary with the environment. How to incrementally diagnose industrial faults under shape shift samples with directly interpretability is a challenging problem. Therefore, a new convertible shapelet learning with incremental learning capability method is proposed in this study. First, incrementally learning under shape shift samples is formulated as how to learn new scaling shift and amplitude shift samples in industrial process. A shape shift detection method is proposed to detect new shape domain. Second, a new parameter initialization method for shapelet learning is proposed, which employs pretraining technology to obtain better initial shapelets. Third, the convertible shapelet-transformed representation is proposed to enhance informativeness of shapelets. An incremental convertible shapelet learning loss is proposed to alleviate catastrophic forgetting in incrementally training new shape time series. By comparing with other state-of-the-art methods, experiments on the Tennessee Eastman Process and real-world aluminum electrolysis process demonstrate the superior performance of the proposed method in accuracy, alleviating catastrophic forgetting and interpretability.

Maintaining an optimal alumina concentration distribution is crucial for ensuring stable operation and reducing energy consumption in aluminum electrolysis cells. Due to the strong coupling of multiple physical fields within the electrolysis cell, the rapid and accurate prediction of alumina concentration distributions has long been a formidable challenge. Furthermore, the harsh industrial environment only allows for the acquisition of alumina concentration data at limited locations, hindering the construction of precise predictive models for the alumina concentration distribution. Moreover, the presence of measurable disturbances, such as feeding, can exert additional influences on the alumina concentration, necessitating traditional methods to collect new data and reconstruct models. To address these issues, this article proposes a structural process knowledge-informed neural network for alumina concentration distribution prediction with desirable precision and high solving efficiency. Specifically, considering the alumina concentration distribution is primarily driven by electrolyte convection and secondarily by electrolysis consumption, pretraining modules for flow and concentration field are designed to explicitly modify the network structure, thereby strengthening the relationship between inputs and process state variables, which significantly enhance prediction efficiency and accuracy. Subsequently, a loss function incorporating process governing equations is designed, which effectively constrains the relationship between inputs and process state variables to adhere to physical principles. This enables accurate concentration prediction at any location in the field, even with a limited number of spatial training data. Finally, a training method for alumina concentration prediction under varying initial concentrations is introduced, incorporating the initial concentration and a fixed time step into the network inputs, thereby eliminating the need for traditional methods to reconstruct models for continuous predictions under different initial conditions. Extensive experiments demonstrate that our method achieves significant performance improvements compared to some state-of-the-art methods.

In the process industries, nonlinear and large time-delay systems pose significant challenges for efficient model predictive control (MPC). The advent of deep learning offers innovative techniques for precise modeling and control; however, deep neural architectures have limited application in control problems. This study introduces a deep neural networks-based model predictive control (DNNs-MPC) that can utilize various gradient-based neural network models as predictors, enhancing the predictive capabilities of MPC and improving performance for nonlinear systems with large time-delay. To achieve this, we first employ dilated convolution and recurrent neural networks to develop a dynamic system modeling predictor, effectively capturing the system's nonlinear characteristics. Concurrently, to address challenges associated with the objective function, we propose an optimization strategy that incorporates three objective functions and employs a multistage weight optimization method to improve control performance and ensure output stability. Furthermore, to derive the optimal control strategy, an adaptive gradient descent method is applied to accelerate the solution process and quickly obtain optimal control signals. Finally, the effectiveness of our method is validated through numerical simulations and a case study of an industrial rotary kiln, demonstrating significant improvements in control performance, system stability, and response accuracy.

The sintering process is crucial for the preparation of ternary cathode materials (TCMs), which need to be precisely monitored to ensure the production of high-quality products. Nevertheless, the sintering process of ternary cathode materials (TCMs-SP) is marked by a prolonged duration, the high dimensionality of process variables, and spatio-temporal correlation coupling, which poses challenges when using traditional centralized monitoring methods. To this end, a novel distributed monitoring method suitable for TCMs-SP is proposed in this article. First, the sintering process is divided into different subsystems based on the material manufacturing process. Then, during the establishment of the local monitoring model, using the low-dimensional strong correlation information transmitted by other subsystems, the Gaussianity preserved deep neural network-aided canonical correlation analysis (CCA) is utilized to extract the Gaussianized nonlinear dynamic features. On this basis, the monitoring statistics of the input and output observation space are developed via CCA, and a monitoring strategy is specially designed for TCMs-SP. Finally, the industrial application verifies that our proposed approach offers superior monitoring compared to existing methods. It allows for rapid localization of faults and enhances troubleshooting efficiency in the sintering process.

Temporal feature representation is critical for soft sensor modeling in industrial time sequences. Deep learning networks like long short-term memory are often used to model the temporal dynamics of data sequences. However, the data collected from industrial plants are usually sampled with irregular frequency, making it challenging for traditional methods to handle these temporally changeable relationships. Therefore, a Gaussian-based interval-aware transformer (GIA-Trans) with interval embedding is proposed in this article to model industrial data with irregular sampling frequency. In GIA-Trans, positional and temporal embedding layers are established to take positional distances and time intervals of samples into account. Then, Gaussian-based time-aware attention is proposed to tackle the changeable time intervals with adaptive weights. In this way, the temporal correlations between samples can be adaptively captured. The GIA-Trans is applied to an industrial hydrocracking process to predict the C5 and C6 content of light naphtha.

Accurate feature extraction and quality variable prediction are critical problems for time sequences in industrial processes. However, industrial samples often exhibit strong temporal correlations with each other that have different positional distances, making it challenging for conventional data-driven models like long short-term memory (LSTM) and Vanilla transformer to capture these underlying features. In this article, a difference metric attention with position distance-based weighting is proposed for transformer (DMA-trans) in industrial time series modeling. First, the DMA is established to calculate the difference of query-key vector pair in transformer to measure the spatial similarity. In this fashion, the difference can accurately represent the spatial similarity of vectors, compared with the original dot product directly on two vectors. Then, positional distance-based weights are designed to capture the sample relevance that has different positional distances. This may help to extract more potential features because the closer samples tend to have higher relevance while there may be weak correlations if two samples are far in positional distance. The effectiveness of the DMA-trans model is validated in industrial hydrocracking processes for C5 content of the light naphtha and the final boiling point of the jet fuel.

This study focuses on the problem of multiunmanned aerial vehicle cooperative pursuit in complex obstacle environments and proposes a lightweight cooperative pursuit method with safety guarantees. By incorporating obstacle motion dynamics, an enhanced control barrier function constraint framework is constructed, effectively overcoming the limitations of traditional approaches that consider only geometric collision avoidance in dynamic environments. To enable the simultaneous pursuit of multiple evading targets, a hybrid task allocation strategy is designed without requiring complex optimization solvers. Building upon this strategy, a distributed quadratic optimization problem is formulated, aiming to minimize control input variations under safety constraints. A neurodynamic approach is employed to solve this optimization problem in real time, thereby achieving efficient and safe control during the cooperative pursuit process. Subsequently, the stability and safety of the closed-loop system are theoretically analyzed. Finally, extensive simulations and physical experiments demonstrate the effectiveness and practicality of the proposed method in multitarget cooperative pursuit tasks.

Accurate grade monitoring in froth flotation is crucial for improving process efficiency and mineral recovery. Current grade monitoring models typically use mean video features and fail to capture feature variations within individual videos. Furthermore, they usually embed the most recently available feed grade into the visual feature vector without providing a refined embedding of feed grade. Therefore, we propose a flotation grade monitoring model with feed grade refined embedding (FGRE) and nonoverlapping patch encoding (NOPE). First, we develop an FGRE module to generate fine-grained feed grade representations. This module extracts the time series of critical feed grade elements by physical topology of the flotation process, and it employs a grouped weight-sharing time-lagged-recurrent neural network (RNN) to fuse multisource temporal data. Then, we introduce an RNN with NOPE to encode video patches and extract dynamic information from each froth video. This network enables the capture of subtle visual feature variations within each individual video. After that, we implement a multivideo temporal extraction strategy to capture dynamic temporal features across multiple videos for grade monitoring. Experiments are conducted on a real-world zinc flotation process. Our proposed approach can be integrated into most existing flotation grade monitoring models, significantly enhancing their monitoring performance.

In fringe projection profilometry systems, accurately reconstructing 3D objects with varying surface reflectivity requires high dynamic range (HDR) imaging. However, the limited dynamic range of single-exposure cameras poses challenges for capturing HDR fringe patterns efficiently. This paper introduces a deep learning-based HDR structured light 3D reconstruction pipeline, comprising an HDR Fringe Generation Module and a Phase Calculation Module. The HDR Fringe Generation Module employs an end-to-end network with attention guidance and feature distillation to reconstruct HDR fringe images from short- and long-exposure low dynamic range (LDR) inputs. The Phase Calculation Module processes the phase information from HDR fringes to enable 3D reconstruction. On a metallic HDR dataset, the method achieved a phase error of 0.105, comparable to the 4-exposure 6-step Phase Shifting Profilometry (PSP) method (0.069), with only 8.3% of the projection time. Experimental results demonstrate the robustness of our approach under diverse object geometries, exposure levels, and challenging global illumination environments. In quantitative measurements, our method achieved accuracies of sub-50 $\mu $ m on ceramic spheres, flat plates and metal step object. Ablation experiments confirmed that feature distillation and attention module effectively enhance the HDR Fringe Generation Module, producing high-quality HDR fringe patterns critical for reconstructing objects with HDR surface reflectivity. Furthermore, we constructed an HDR imaging metal dataset comprising 1,700 samples of machined metal parts with diverse shapes, sizes, and materials, making it a benchmark in the field of HDR structured light measurement. Our method offers a general HDR imaging-based structured light 3D reconstruction approach, integrating the two modules into an efficient, end-to-end solution for objects with HDR reflective surfaces.

This article concerns the investigation on the consensus problem for the joint state-uncertainty estimation of a class of parabolic partial differential equation (PDE) systems with parametric and nonparametric uncertainties. We propose a two-layer network consisting of informed and uninformed boundary observers where novel adaptation laws are developed for the identification of uncertainties. Particularly, all observer agents in the network transmit their information with each other across the entire network. The proposed adaptation laws include a penalty term of the mismatch between the parameter estimates generated by the other observer agents. Moreover, for the nonparametric uncertainties, radial basis function (RBF) neural networks are employed for the universal approximation of unknown nonlinear functions. Given the persistently exciting condition, it is shown that the proposed network of adaptive observers can achieve exponential joint state-uncertainty estimation in the presence of parametric uncertainties and ultimate bounded estimation in the presence of nonparametric uncertainties based on the Lyapunov stability theory. The effects of the proposed consensus method are demonstrated through a typical reaction-diffusion system example, which implies convincing numerical findings.

As a well-known NP-hard problem, the vertex cover problem has broad applications, which has aroused the concern of many researchers. In recent years, its related optimization problems, including the weighted vertex cover problem, the $\ell \geq 3$ path vertex cover problem, and the connected vertex cover problem, and other related optimization problems have came into the view of researchers, who have designed various optimization algorithms to solve those related optimization problems. First, based on the existing works, we give detailed descriptions of the vertex cover problem and its related optimization problems and then review the current research progress. Then, we present some main representative optimization algorithms and provide numerical results and corresponding analysis. Finally, we summarize the existing works and present the future research directions.

Real industrial process data from various domains often exhibit divergent distributions, may occupy distinct feature spaces, and are occasionally unlabeled, which limits the effectiveness of conventional process monitoring methods. To address these challenges, we propose an adaptive discriminative transfer dictionary pair learning (ADTDPL) method with attribute embedding for generalized cross-domain industrial process monitoring. Specifically, this method aligns the feature spaces of source and target domains by the aligned transfer reconstruction, enabling the transfer of knowledge through a common synthetical dictionary. Concurrently, semantic attributes relevant to process knowledge are seamlessly fused into data information via attribute embedding, enhancing the transferability and interpretability of dictionary pairs. Considering the relative significance of marginal and conditional distributions, an adaptive distribution consistency function is designed to better reduce the distributional discrepancies. And the discriminative structure regularization is developed to ensure the discrimination of the dictionary pairs and their corresponding coding coefficients. Furthermore, in the absence of target domain labels, a novel selective pseudo-labeling strategy is advanced to adaptively update pseudo-labels. The superior performance of our method for cross-domain process monitoring is verified on the Tennessee Eastman platform and in practical aluminum electrolysis processes (AEPs).

Confidence calibration in classification models is a vital technique for accurately estimating the posterior probabilities of predicted results, which is crucial for assessing the likelihood of correct decisions in real-world applications. Class imbalance data, which biases the model's learning and subsequently skews predicted posterior probabilities, makes confidence calibration more challenging. Especially for underrepresented classes, which are often more important and tend to have higher uncertainty, confidence calibration is more complex and essential. Unlike previous surveys that typically separately investigate confidence calibration or class imbalance, this article comprehensively investigates confidence calibration methods for deep learning-based classification models under class imbalance. First, the problem of confidence calibration under class imbalance data is outlined. Second, this article explores the impact of class imbalance data on confidence calibration in theory, providing some explanations for empirical findings in existing studies. Third, this article reviews 60 state-of-the-art confidence calibration methods under class imbalance data, divides these methods into six groups according to method differences, and systematically compares seven properties to evaluate their superiority. Then, some commonly used and emerging evaluation methodology are summarized, including public datasets and evaluation metrics. Subsequently, this article performs necessary comparative experiments to provide better guidelines and insights to the readership. Finally, we discuss several application fields and promising research directions that serve as a guideline for future studies.

With the rapid development of modern industry and the increasing prominence of artificial intelligence, data-driven process monitoring methods have gained significant popularity in industrial systems. Traditional static monitoring models struggle to represent the new modes that arise in industrial production processes due to changes in production environments and operating conditions. Retraining these models to address the changes often leads to high computational complexity. To address this issue, we propose a multimode process monitoring method based on element-aware lifelong dictionary learning (EaLDL). This method initially treats dictionary elements as fundamental units and measures the global importance of dictionary elements from the perspective of the multimode global learning process. Subsequently, to ensure that the dictionary can represent new modes without losing the representation capability of historical modes during the updating process, we construct a novel surrogate loss to impose constraints on the update of dictionary elements. This constraint enables the continuous updating of the dictionary learning (DL) method to accommodate new modes without compromising the representation of previous modes. Finally, to evaluate the effectiveness of the proposed method, we perform comprehensive experiments on numerical simulations as well as an industrial process. A comparison is made with several advanced process monitoring methods to assess its performance. Experimental results demonstrate that our proposed method achieves a favorable balance between learning new modes and retaining the memory of historical modes. Moreover, the proposed method exhibits insensitivity to initial points, delivering satisfactory results under various initial conditions.

Modern industrial systems often operate under complex dynamics and strict reliability constraints, demanding a timely and precise fault diagnosis with efficient root cause analysis to ensure operational safety and minimize downtime. However, the inherent uncertainties and complexities of industrial processes present significant challenges for conventional diagnostic approaches. Specifically, even minor anomalies can escalate into critical incidents, while process uncertainties frequently induce distribution shifts, leading to novel fault types that complicate fault detection and diagnosis. To address these challenges, this article proposes a novel industrial flow topology-induced semi-heterogeneous graph perception network (IFT-SHGPN) model for class-incremental fault diagnosis of complex industrial processes. By embedding the physical topology of industrial processes into a semi-heterogeneous graph perception network (SHGPN) and incorporating gradient-weighted class activation mapping (Grad-CAM), the proposed approach demonstrates strong capability in effective class-incremental fault recognition and interpretable root cause analysis. Rigorous experiments on the Tennessee Eastman process (TEP) and a multiphase flow facility process under various operation conditions showcase the superiority of IFT-SHGPN over existing methods. The proposed approach achieves high diagnostic accuracy for both historical and emerging fault categories while enabling efficient root cause identification with low computational overhead, making it particularly suitable for deployment in resource-constrained industrial environments.

For industrial processes, it is significant to carry out the dynamic modeling of data series for quality prediction. However, there are often different sampling rates between the input and output sequences. For the most traditional data series models, they have to carefully select the labeled sample sequence to build the dynamic prediction model, while the massive unlabeled input sequences between labeled samples are directly discarded. Moreover, the interactions of the variables and samples are usually not fully considered for quality prediction at each labeled step. To handle these problems, a hierarchical self-attention network (HSAN) is designed for adaptive dynamic modeling. In HSAN, a dynamic data augmentation is first designed for each labeled step to include the unlabeled input sequences. Then, a self-attention layer of variable level is proposed to learn the variable interactions and short-interval temporal dependencies. After that, a self-attention layer of sample level is further developed to model the long-interval temporal dependencies. Finally, a long short-term memory network (LSTM) network is constructed to model the new sequence that contains abundant interactions for quality prediction. The experiment on an industrial hydrocracking process shows the effectiveness of HSAN.

As a typical combinatorial optimization problem, the 3-path vertex cover problem has wide applications in practice. To solve the 3-path vertex cover problem from the perspective of distributed optimization, we treat each vertex as an agent (i.e., player) with computation, and decision-making capabilities. First, we establish a 3-player symmetric game model to describe the 3-path vertex cover problem, and design the corresponding cost function for each player. Then, we prove that under the established game model, strict Nash equilibriums (SNEs) act as the basis of the connection between 3-path vertex cover states and minimum 3-path vertex cover states. Next, we propose a novel memory-based synchronous learning (MSL) algorithm, where the initial profile strategy generation of players relies on the designed degree preference rule, and each player has a memory length for recording strategies and independently update their strategies concurrently based on the accessed local information. After that, we prove that our proposed MSL algorithm can guarantee that any strategy profile converges to an SNE, and provide a theoretical analysis of the algorithm’s complexity. Finally, we present numerous numerical simulations to demonstrate the performance of our proposed algorithm on various networks. Moreover, we find that increasing the memory length and adopting the degree preference initialization can yield a better SNE.

Multirate sampling data are common in industrial systems, and traditional diagnostic models face challenges in directly handling such data due to its incomplete characteristics. Recently, some data reorganization methods have been proposed, which divide the multirate sampling data into several data subsets and learn diagnostic models based on each subset separately. However, they always treat different tasks independently and ignore the correlation between data subsets. In addition, training individual models for each task will decrease the data available and increase computational resources. In this article, a novel attention-based mask network fault diagnosis model for multirate sampling data is proposed, which converts the multirate sampling fault diagnosis problem into a continual multitask learning problem and realizes it with one model. Moreover, an attention mechanism and cumulative gradients are integrated during multitask learning to alleviate the catastrophic forgetting problem, thus further guaranteeing the performance of fault diagnosis. Notably, the proposed method is the first to approach multirate sampling data from a multitask continual learning perspective, allowing it to fully leverage raw data. Extensive experiments, including a numerical simulation and an industrial three-phase flow case, indicate the proposed method effectively tackles multirate sampling data fault diagnosis within a multitask continual learning framework. Since, it takes the correlation between such data and avoids the detrimental effects of catastrophic forgetting, it achieves higher accuracy in fault diagnosis.

This article introduces an integrated guidance and control method for morphing flight vehicles, addressing model uncertainties and external disturbances through a robust deep reinforcement learning framework built on sliding-mode control (SMC). The method development begins with the establishment of a longitudinal guidance and control model and a detailed introduction to the necessary theoretical foundations. The proposed approach incorporates robust observation strategies enabled by a novel fixed-time SMC design. The agent’s actions, rewards, neural network structure, and training process are meticulously crafted to tackle practical guidance and control challenges effectively. Trained offline to achieve seamless integration of position and attitude control, the agent generates end-to-end control commands in real time during online operation. Extensive testing, including robustness evaluations, generalization assessments, and comparative performance analyses, demonstrates the superiority and reliability of the proposed method.

Complex industrial processes often run under varying operating conditions. Learning-based control methods are difficult to adapt to these unknown variations. Therefore, it is necessary to update the model and control strategy adaptively. However, in traditional control frameworks, due to the limitation of computational and storage resources of edge devices, control strategies are difficult to update once deployed, which leads to model mismatch after operating condition change and seriously reduces the control performance. To solve this problem, this article proposes a novel cloud-edge collaborative control method. Specifically, a cloud-assisted parallel subspace identification method is proposed, which fully utilizes the powerful computational capability of the distributed cluster in the cloud to achieve fast and accurate model identification. Then, an explicit control strategy is proposed, which solves the control law as a piece-wise affine function offline. The process model and explicit control law are sent down to the edge, enabling fast and precise control under limited resource constraints. An operating condition change detection method based on the process model is proposed, and the edge detects the emergence of new operating conditions by the prediction error. Meanwhile, to fully excite new operating condition characteristics, a joint control and excitation signal generator (JCESG) is designed. JCESG ensures accurate identification of new operating condition model under limited data, which in turn greatly shortens the operation condition switching process and ensures fast modeling and precise control in new operating conditions. Notably, considering that the proposed method can adaptively realize model identification and control law update, it is capable of adapting to the continuous change of operating conditions, and the sufficient excitation of JCESG greatly reduces the data volume requirement for model update, which further ensures that the method adapts to the full range of operating conditions. Finally, extensive experiments verified the superiority of the proposed method.

A stochastic Boolean network (SBN) emerges as a more realistic model for gene regulatory networks than a deterministic Boolean network (BN). In order to reduce output sampling while ensuring a given estimation accuracy, this article proposes an event-triggered sampling strategy for the state estimation of SBNs. Under this strategy, the output is sampled when the one-step prediction mean error exceeds a prespecified threshold. An iterative algorithm for the state probability distribution is proposed based on the algebraic form of SBNs, which determines the optimal state estimation. A matrix inequality method is proposed to calculate the worst-case mean estimation error based on its monotonicity with time. Then, the range of sampling triggering thresholds that minimize the worst-case mean estimation error is obtained. This article demonstrates that the event-triggered sampling strategy can make a tradeoff between estimation error and sampling rate. It explains that the full sampling estimator is a special event-triggered sampling estimator. Finally, the proposed method is applied to BN models of the lac operon in Escherichia coli to analyze the relationship among the sampling triggering threshold, the sampling rate, and the estimation error.

As human-computer interaction (HCI) advances, gesture recognition has emerged as a transformative technology for human-computer interaction. Traditional methods, often camera or glove-based, are restricted by various environmental conditions and user-specific demands, highlighting the need for more universal, non-intrusive, and sustainable solutions. Addressing this, we present PowerGest, a self-powered gesture recognition system based on a solar cell array. This innovative system leverages the dual functionalities of solar cells: energy harvesting and gesture sensing, providing an alternative to conventional methods. It integrates a designed low-powered data acquisition chip with a wireless transmission module and a user-friendly interface. PowerGest employs a series of signal processing methods and utilizes several machine learning algorithms, achieving over 97% accuracy for both numeric input and activity control gesture recognition tasks. With its broad applications in robotic control, text input, and more, PowerGest contributes to a more sustainable and intuitive HCI experience. Project demo: https://drive.google.com/drive/folders/10KEul8PAfvTUomi0JvZ8u411JyScCXQI?usp=sharing.

With the rapid development of industrial network, Industrial Internet of Things (IIoT) has become an indispensable part of industrial network development. However, due to the vulnerability of Industrial Internet of Things to network intrusion attacks. Therefore, anomaly detection in IIoT is particularly important. In this paper, an effective intrusion detection approach ROSE-BOX (Random fOrest, SmotE, BO-Xgboost) is proposed to detect multi-class cyberattacks based on Random Forest, SMOTE and BO-XGBoost in IIoT. It is worth mentioning that BO-XGBoost is obtained by optimizing the parameters of XGBoost using Bayesian optimization. Finally, compared with other existing methods, the proposed approach has better detection performance, with an accuracy rate of over 99.85%.

The demand for accurate and real-time indoor localization and human sensing is rising with the development of smart environments, with applications in security and smart homes. Effective systems enhance safety, energy efficiency, and user experience by leveraging existing infrastructure, reducing deployment costs, and integrating seamlessly. Traditional methods rely on dedicated hardware, while communication or lighting infrastructure can provide dual-purpose solutions. This research focuses on Visible Light Communication (VLC) technology, which uses visible light for data transmission. Our VLC system utilizes existing lighting infrastructure to transmit data, providing localization and human sensing functionalities. The system design strategically incorporates specific VLC transmitters and receivers to enhance sensing performance. The collected data is processed using advanced algorithms and machine learning models, ensuring robust, real-time, and cost-effective localization with an accuracy of 96.6% and human activity recognition with an accuracy of 98.3%. This multi-functionality system demonstrates the potential for VLC in healthcare monitoring and home automation applications.

SolarSense is a self-powered sensing system for gesture recognition using solar cell arrays, thereby offering a sustainable approach to human-computer interaction (HCI) within industrial settings. The system effectively employed the sensing and energy harvesting capabilities of solar cells, achieving over 97.0% accuracy in recognizing diverse gestures. The design incorporates a low-power wireless data acquisition chip, a signal processing framework, and a user interface to realize robotic control and text input applications. SolarSense enhances HCI with its eco-friendly and user-centric approach, which is suitable for Internet of things (IoT) scenarios. Demo: https://youtu.be/RmPolChw_c4.

With the development of information technologies, a large number of sensors have been deployed to obtain industrial data. As a result, data-driven approaches have become a crucial means for modeling of distributed parameter systems (DPS). However, due to harsh environments and unreliable sensors, data is often of low quality in practice, which in turn poses a challenge for data-driven modeling approaches. In order to address the challenge of inaccurate modeling of DPS induced by outliers, this paper proposes a physical-informed sparse learning method to overcome the adverse effects of outliers and achieve robust modeling of DPS by fully exploring the spatiotemporal dynamic of DPS. Specifically, this paper proposes an innovative method for robust modeling of DPS. The method incorporates the statistical features of contaminated data to restore the dynamic evolution structure of DPS, which weakens the adverse effects of outliers and address the problem of low modeling accuracy caused by contaminated observation data. Furthermore, the underlying partial differential equation (PDE) of DPS is incorporated into the constraint on the temporal deviation data, which leads to a physical-informed optimization objective and improves the reliability of outlier extraction and DPS modeling. Finally, an optimization algorithm based on the alternating direction method of multipliers (ADMM) with an adaptive penalty factor is proposed. This ensures the convergence of multivariate optimization problem and superior performance of the DPS modeling framework. Extensive experimental results have verified that the proposed method is effective in overcoming the adverse effects of outliers and achieving robust modeling of DPS.Note to Practitioners—The motivation of this paper is to develop an interpretable and robust modeling method for DPS. Considering the negative impact of outliers, the proposed method first restores the dynamic properties of the data based on the characteristics of the outliers. Then, the embedding of physical knowledge ensures the reliability of outlier removal and robustness of modeling. Extensive experimental results have verified that the proposed method can effectively overcome the adverse effects of outliers and outperform some state-of-the-art methods. Therefore, it is more suitable for real industrial systems.

Nowadays, the intelligent fault diagnosis problem of modern industrial systems has received increasing attention. However, with the increasing scale of industrial systems, the same category of faulty data often contains multiple working conditions, which leads to the multi-granularity of faulty data and the increasing complexity of data distribution. The possible existence of fine-granularity inter-class similarities in multi-granularity data can interfere with coarse-granularity recognition tasks, which brings challenges to data-driven fault diagnosis tasks. To solve the above issue, a fault diagnosis method based on multi-granularity dictionary learning is proposed in this paper. Specifically, the proposed method integrates prior knowledge into the dictionary learning framework by introducing the concept of “centroid atom”, which embeds the multi-granularity structure in the dictionary and improves the discrimination of dictionary atoms. In order to validate the proposed approach, experiments are conducted on a numerical simulation case and a zinc smelting roasting process. Compared with some state-of-the-art methods, the proposed approach performs better in the classification accuracy which shows that it can make full use of the knowledge provided by the multi-granularity structure while it is difficult to be modeled by the other methods. Therefore, it can be applied to fault diagnosis tasks more precisely and efficiently. Note to Practitioners—Motivated by the fact that industrial faults often occur in different modes, and different faults may occur in the same mode, the fault data often has multi-granularity characteristics, this paper proposes a multi-granularity dictionary learning method for complex industrial fault diagnosis. The proposed method can learn the multi-granularity structure in the data, and the obtained model contains both coarse-grained and fine-grained information, which effectively improves the fault diagnosis accuracy. Intensive experimental results show that the proposed method performs better than some state-of-the-art methods, it can make full use of the knowledge provided by the multi-granularity structure, while it is difficult to be modeled by others. Above all, it is verified as suitable for fault diagnosis of real industrial systems.

In this paper, a detection framework is proposed that combines strong light removal, working condition identification, and slagging quality evaluation. This framework is applied to a semisolid metal solution slagging operation system, which is an approach that was not available in previous work. Due to the strong time-varying nature and texture of metal solutions, it is difficult to identify different oxidized slag working conditions. The Gabor transform is used to model images of semi-solid solutions. The t location-scale distribution and extreme value distribution are used to fit the statistical characteristics of Gabor responses. A least squares support vector machine (LS-SVM) classifier is trained to recognize the model based on the statistical feature vectors. This enables working condition identification and quality evaluation. To solve the strong light interference on the semisolid metal solution, color space conversion and a parallel generative adversarial network (PGAN) is proposed to restore the missing texture details in the light area and improve the image quality of metal solutions. In the experiments, the accuracy of working condition identification and quality evaluation reached 95%, and the product quality met the production requirements.Note to Practitioners—This article is motivated by the problem of ensuring the quality of slag removal by robots in order to completely remove oxidized slag from the surface of each metal ingot.To guarantee the quality of each metal ingot, the production line uses two robots to remove oxide slag. This is accomplished in two stages: removal of coarse and fine slag. Before coarse slag removal, a visual inspection framework is used to recognize work conditions and calculate the shovel depth for the coarse slag robot. After the coarse slag removal, the ingot surface is assessed for quality. If it is not qualified, fine slag removal is performed to remove the residual oxidized slag. Additionally, a light removal algorithm based on an improved generative adversarial network is proposed to improve the quality of the metal solution image. This detection framework has been applied in actual production, and future research will focus on the visual servo control of the slag-picking robot.

The intelligent goal of process manufacturing is to achieve high efficiency and greening of the entire production. Whereas the information system it used is functionally independent, resulting to knowledge gaps between each level. Decision-making still requires lots of knowledge workers making manually. The industrial metaverse is a necessary means to bridge the knowledge gaps by sharing and collaborative decision-making. Considering the safety and stability requirements of the process manufacturing, this article conducts a thorough survey on the process manufacturing intelligence empowered by industrial metaverse. First, it analyzes the current status and challenges of process manufacturing intelligence, and then summarizes the latest developments about key enabling technologies of industrial metaverse, such as interconnection technologies, artificial intelligence, cloud-edge computing, digital twin (DT), immersive interaction, and blockchain technology. On this basis, taking into account the characteristics of process manufacturing, a construction approach and architecture for the process industrial metaverse is proposed: a virtual-real fused industrial metaverse construction method that combines DTs with physical avatar, which can effectively ensure the safety of metaverse's application in industrial scenarios. Finally, we conducted preliminary exploration and research, to prove the feasibility of proposed method.

Early classification predicts the class of the incoming sequences before it is completely observed. How to quickly classify streaming time series without losing interpretability through early classification method is a challenging problem. A novel memory shapelet learning framework for early classification is proposed in this article. First, a memory distance matrix is introduced to store the historical characteristics of streaming time series, which can alleviate repetitive calculations caused by the growing length of time series. Second, early interpretable shapelets are extracted in the proposed method by optimizing both accuracy objective and earliness objective simultaneously. The proposed method employs end-to-end learning, which allows the model to directly learn early shapelets without the necessity of searching for numerous candidate shapelets. Third, an objective function of memory shapelet learning is proposed by overall considering accuracy and earliness, which can be optimized by gradient descent algorithm. Finally, experiments are conducted on benchmark dataset UCR, Tennessee Eastman process, and real-world aluminum electrolysis process in China. Comparable results with other state-of-the-art methods demonstrate the superior performance of the proposed method in interpretability, accuracy, earliness, and time complexity.

This article studies the performance monitoring problem for the potassium chloride flotation process, which is a critical component of potassium fertilizer processing. To address its froth image segmentation problem, this article proposes a multiscale feature extraction and fusion network (MsFEFNet) to overcome the multiscale and weak edge characteristics of potassium chloride flotation froth images. MsFEFNet performs simultaneous feature extraction at multiple image scales and automatically learns spatial information of interest at each scale to achieve efficient multiscale information fusion. In addition, the potassium chloride flotation process is a multistage dynamic process with massive unlabeled data. To overcome its dynamic time-varying and working condition spatial similarity characteristics, a semi-supervised froth-grade prediction model based on a temporal-spatial neighborhood learning network combined with Mean Teacher (MT-TSNLNet) is proposed. MT-TSNLNet designs a new objective function for learning the temporal-spatial neighborhood structure of data. The introduction of Mean Teacher can further utilize unlabeled data to promote the proposed prediction model to better track the concentrate grade. To verify the effectiveness of the proposed MsFEFNet and MT-TSNLNet, froth image segmentation and grade prediction experiments are performed on a real-world potassium chloride flotation process dataset.

In gene regulatory networks (GRNs), it is important to model gene regulation based on a priori information and experimental data. As a useful mathematical model, probabilistic Boolean networks (PBNs) have been widely applied in GRNs. This article addresses the optimal reconstruction problem of PBNs based on several priori Boolean functions and sampled data. When all candidate Boolean functions are known in advance, the optimal reconstruction problem is reformulated into an optimization problem. This problem can be well solved by a recurrent neural network approach which decreases the computational cost. When parts of candidate Boolean functions are known in advance, necessary and sufficient conditions are provided for the reconstruction of PBNs. In this case, two types of reconstruction problems are further proposed: one is aimed at minimizing the number of reconstructed Boolean functions, and the other one is aimed at maximizing the selection probability of the main dynamics under noises. At last, examples in GRNs are elaborated to demonstrate the effectiveness of the main results.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

Reagent addition control level of the zinc first rougher has a significant impact on quality of the zinc concentrate. As froth visual features are important indicators of working states in froth flotation, they are usually controlled to optimal setpoints by reagent addition for the desired concentrate. Existing methods usually construct a rule base for feature setpoint generation and design an error-driven feedback controller for reagent addition. However, there are still some issues: 1) a portion of data is used for rule base establishment and knowledge in remaining data is ignored; 2) as the rule base is sparse, it may lead to match failures caused by insufficient knowledge; 3) existing controllers only consider the error at the current time, while ignore the temporal information in error sequences. Therefore, we propose a reagent addition control strategy with the dual rule-based weighted fuzzy interpolative reasoning module and temporal encoder-decoder Bayesian network. Firstly, we design a dual rule base with complementary data to fully mine knowledge. Then, we explore a weighted fuzzy interpolative reasoning module for feature setpoint generation. This module selects several nearest neighbor rules in a positive rule base as interpolative candidates and determines rule weights via the nearest neighbor rules in the negative rule base. After that, we introduce a temporal encoder-decoder network by taking temporal error sequences as the input to recognize working states, and use a Bayesian network to realize reagent adjustment. Ablations and comparative experiments on industrial data show the effectiveness of the proposed control strategy.

Modern industrial processes often exhibit complex and uncertain operating state fluctuations due to the diversification of production materials, the complexity of production processes, and the harsh production environment. To cope with the operating state fluctuations of industrial processes, fuzzy neural networks, as a form of human-inspired computing, have been introduced and widely applied. In contrast, model predictive control (MPC) typically employs a multi-model control strategy for the multimode process. However, ensuring control performance during the switching phase and meeting real-time control requirements poses challenges. To address the real-time control challenge in multimode processes, this article proposes a learning framework for fuzzy neural network explicit control for full operation conditions. The proposed framework includes two modules: the fuzzy neural network-based explicit control law (FNNECL) and the neural network-based predictive model (NNPM). By adjusting the structure and parameters of FNNECL, precise control of full operation conditions is achieved. Specifically, a novel truncated radial basis activation function is first presented, and the operation condition change measurement index of data coverage is proposed to identify the mismatch degree between current and historical operation conditions accurately. Then, for small-scale operation condition changes, an elastic weight consolidation (EWC) mechanism is introduced to ensure that FNNECL can learn control strategies for new operation conditions while maintaining control performance for historical operation conditions. Finally, to address large-scale operation condition changes, a radial basis function (RBF) neuron growth mechanism based on data coverage is proposed. This mechanism calculates the data coverage of fuzzy rules and selectively adds new neurons to the fixed structure FNNECL. This enables the explicit control law to fit large-scale changes and effectively learn control strategies for new operation conditions. It is worth noting that during the application of this method, there is no need to know the current operation conditions in advance. Based on a single FNNECL model, accurate control sequences can be rapidly obtained, overcoming the traditional online optimization problem of multiple models. This greatly expands the application scope and achieves precise control under full operation conditions. Extensive experiments including numerical simulation and industrial roasting process verified the feasibility and effectiveness of the proposed method.

Deep neural networks (DNNs) as one of the key enabling technologies have been widely used in industrial artificial intelligence (IAI). However, recent research has revealed that they are quite vulnerable to adversarial attacks, arousing serious concerns about DNNs' robustness in many IAI-driven applications such as industrial video analysis tasks. Considering the attack efficiency and effectiveness, it is essential to study the sparse adversarial attack examples. Nevertheless, current methods' performance is limited by insufficient sparsity and lacks a unified framework. To solve these problems, in this article, we focus on sparse adversarial video attacks and propose a dual-branch neural network-based model to generate sparse adversarial video examples in an end-to-end fashion. We conduct extensive experiments with mainstream video models on public datasets and industrial case. Experimental results demonstrate that compared with state-of-the-art methods, our method can achieve a faster and better attacking performance with less than 1% perturbed pixels in the video.

Online assessment of the molten iron quality of a blast furnace ironmaking process strongly depends on reliable measurement of silicon (Si) content. In this article, we propose a novel cooperative strategy to train the data-driven prediction model and estimate variable time delay (VTD) values. First, a dynamic deep network based on stacked denoising autoencoders (D-SDAE) with the ability to describe process nonlinearity and time-varying is designed for Si estimation. Then, time delay between the process variables and Si, which may disrupt the data distribution pattern (i.e., the real input–output relationship), is recovered in the modeling process. It is an overlooked data feature that occurs due to material transportation times, installation location, and the analysis period of sampling equipment. The VTD values are considered to be the parameters of the D-SDAE model and obtained in the training process by the proposed double-scale collaborative search particle swarm optimization algorithm. The effectiveness of the proposed VTD-based D-SDAE model is validated in a numerical simulation and an industrial ironmaking plant, and a marked improvement in the prediction performance is achieved when VTD information is considered.

The real-time recognition of operating conditions is always critical to ensuring the efficient and stable operation of industrial flotation processes. Although the widespread use of smart devices enables the availability of multimodal data in flotation processes, recognizing operating conditions using cross-modal data information is still challenging due to the modality gap. To address this issue, this article first proposes an innovative bimodal manifold autoencoder model to predict interested quality variables from the perspective of visual modality and auditory modality. Specifically, the well-designed intra- and intermodal manifold regularization constraints are introduced to fully learn the intrinsic manifold features within each modality and the interdependencies across modalities, thereby enhancing the cross-modal data representation ability of the developed prediction models. Then, based on the prediction values of quality variables, an adaptable multimodal fuzzy decision inference module is designed to recognize the operating conditions while counteracting the influence of fluctuations in feedstock properties. Finally, extensive experiments are conducted on two industrial flotation process datasets at distinct periods to validate the superiority of the proposed methods in terms of quality prediction and operation condition recognition tasks.

Zinc rotary kiln is an important equipment in the nonferrous metallurgical industry. Due to unclear internal working conditions, its operation based on experience is random. Digital twin (DT) with virtual–real integration and synchronization ability is a necessary method to realize real-time and accurate monitoring for key variables, while its nowadays practical application face challenge about accuracy and consistency. Therefore, this article proposes a DT driven soft sensing method for key variables in rotary kiln. First, a thermodynamics and chemical reactions coupling model is built after analyzing the mechanism in kiln. Second, key parameters of DT were identified through analysis of limited and multisource data to ensure its consistency. Finally, in DT deployment stage, to realize the DT soft-sensing real-timely, a model reduction and DT distributed computing method was proposed to improve simulation timeliness. Practical application proved that soft sensing method based on DT can accurately and effectively obtain real-time monitoring results of key variables in the rotary kiln.

Accurate and timely foam grade prediction plays an important role in the flotation foam industry process. However, the information between foam characteristic series and foam grade series at different sampling times often does not match, making the prediction result lagging behind. A multistep fusion transformer (MSFT) model is designed in this article. First, we extract multiple froth time series as input to correlate feature information and grade information under multiple time series, then, a self-attention structure is designed to fuse at multiple scales, which enhances the degree of information correlation under different time series, finally, the information matrix is passed through the fully connected layer to obtain the final prediction result. Compared with the existing froth grade network recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit, Transformer, Enc–Dec (RNN), feature reconstruction–regression, Siamese time series and difference (LSTM), and FlotationNet models, the MSFT model has reduced the baseline by 30.3%, 30.3%, 30%, 66.9%, 30%, 45.8%, 55.2%, and 52.5%, respectively, among all indicators.

To prevent industrial fault propagation, it is important to clarify the relationship between industrial system components and identify the fault propagation path efficiently and timely, aiming at the problems in fault propagation path identification, this article presents an equipment fault propagation analysis approach based on unstable points identification to solve such issues. First, the fault propagation diagram is created by analyzing the industrial complex system components. Furthermore, to address the issues of traditional interpretative structural modeling (ISM), the bilateral rotation interpretative structural modeling (BRISM) method is proposed to stratify the components and detect the unstable points. Finally, the fault propagation graph is analyzed using the PageRank algorithm to update the unstable points and edge weights between different nodes to identify the fault propagation paths. The results indicate that the proposed method can effectively identify fault propagation paths between components and can be applied to various industrial systems.

In contemporary industries, diagnosing bearing faults is crucial, yet the complexity and diversity of these faults pose challenges to traditional methods. Existing algorithms typically treat compound faults as independent events, overlooking the interrelations among different faults, which constrains the performance in diagnosing the faults with diverse semantic complexities. Also, the research on leveraging multimodal data to enhance fault diagnosis accuracy is limited. To overcome the weakness mentioned above, a multimodal knowledge graph (MKG) construction method based on multimodel data, including time series vibration signals, spectrum, and description text of datasets, is proposed. Subsequently, a fault diagnosis method utilizing a MKG completion model based on a relation cascade graph attention network is designed to capture the relationship between various faults. Experimental results on an MKG constructed from seven bearing datasets demonstrate the robustness of the proposed method.

Modern industrial processes generate many interassociated variables, which are more likely to implicit associations knowledge for describing irregular changes at different times to accurately describe behavior changes. Motivated by this issue, a novel knowledge-data-based synchronization states analysis method is proposed in this article for process monitoring. Its advantage mainly refers to integrating physical–chemical mechanism knowledge to handle the representation of associated relationships between numerous monitor variables. Furthermore, this method utilizes the trend distributions of variable changes to observe the differences between operation states and their parents online, which can maintain the simple, practical, and efficient advantage of data-driven process monitoring. Specifically, global process monitoring can be achieved by the synchronization status exceeding its corresponding threshold ($\chi ^{2}$distribution). At the same time, the local cause of backtracking can also be identified by whether the weighting of eigenvector components of each variable exceeds their corresponding thresholds ($\chi ^{2}$distribution). This novel proposed process monitoring method is applied to one practical hydrometallurgical zinc purification process consisting of copper and cobalt removal processes. The application's comparable performance shows the applicability and effectiveness of this proposed method.

In-process hot-rolled strip steel is suffering from some complicated yet unavoidable surface defects due to its harsh production environment. The automated visual inspection on defects consistently faces challenges of interclass similarity, intraclass difference, low contrast, and overlapping issue, which tend to trigger false or missed detections. This article proposes a prior-modulated and semantic-aligned dynamic transformer, called PMSA-DyTr. In this framework, a long short-term self-attention embedded with local convolution is designed for assisting an encoder to eliminate noise ambiguity between defects and backgrounds. Then, a semantic aligner is cleverly bridged between the encoder and the decoder to align the sematic for speeding up the convergence, and prior-modulated cross attention is proposed to alleviate the deficiency of samples for a data-driven transformer. Furthermore, a gate controller is innovatively constructed to dynamically select the minimal number of encoder blocks while preserving detection accuracy. The proposed PMSA-DyTr outperforms 19 state-of-the-art models on mean average precision with an inference time of 54.67 ms and visually performs best in detecting low-contrast and multiple small defects.

Few-shot new faults are constantly emerging due to the dynamic environments and operations in the industrial process. It is a challenge for existing fault diagnosis methods to diagnose few-shot new faults without forgetting old faults by fine tuning the base model. This article defines this challenge as few-shot fault incremental learning problem, and proposes a multiview shapelet prototypical network to solve this problem. First, a multiview metalearning framework that simulates true incremental tasks and combines multiview information is proposed in this article to build a generalizable feature space for unseen classes. Second, a multiview shapelet prototypical classifier is proposed to enhance the generalization ability of shapelets in adapting new faults with few samples. Third, multiview metacalibration modules based on transformers are proposed to fuse multiview information and calibrate prototypes and embedded features into a distinguishable space. Finally, experiments are conducted on the benchmark Tennessee Eastman process and the real-world aluminum electrolysis process. Experimental results illustrate that the proposed method is better than the existing methods in terms of interpretability, alleviating catastrophic forgetting, and reducing time complexity.

In complex process industries, multivariate time sequences are omnipresent, whose nonlinearities and dynamics present two major challenges for soft sensing of important quality variables. Consequently, due to the potent representational capabilities, nonlinear dynamic models like gated recurrent unit (GRU) and long short-term memory (LSTM) networks have been used for data sequence modeling. Though it is a common occurrence in many industrial plants, data series with heterogeneous sample intervals and missing values cannot be directly handled by these dynamic algorithms. To this end, attention-based interval-aided networks (AIA-Net) are proposed in this article to adaptively model the temporal information for heterogeneous sampling sequences with missing values in the processes industry. It includes two main mechanisms, which are named attention-based time-aware dynamic imputation and interval-aided time-aware network, respectively. The reduction rate is introduced by the attention-based time-aware dynamic imputation to apply the effects of time intervals and is used in the imputation of missing data. The interval-aided time-aware network includes time intervals in the model structure and uses a sampling interval gate to correct the temporal correlations in time series. The proposed AIA-Net is successfully applied to a real hydrocracking process to predict the C5 and C6 content in the light naphtha.

Charging operation is an essential regulatory measure at the top of blast furnaces. Accurately predicting the burden surface shape (BSS) and reasonably adjusting the charging matrix (CM) are vital for achieving precise charging operation. However, the existing methods neglect the influence of the burden motion state, which greatly limits prediction accuracy of BSS, and complex constraints of CM further cause the lack of effective CM optimization strategies. To address these challenges, a BSS prediction model and a CM optimization strategy are innovatively proposed in this article. First, the burden motion state under different accumulation behaviors is introduced into the BSS modeling, and the iterative solution of BSS is realized with the volume conservation constraint. Then, a hybrid constraint-handling mechanism consisting of the infeasible solution rejection method and the penalty function method is proposed, which is used to develop a CM optimization model. Finally, an improved algorithm, named dynamic heterogeneous multiswarm particle swarm optimization, is designed to solve the optimal CM (i.e., the best particle in the population). The prediction results of BSS model are highly similar to those of the model based on discrete element method, and the effectiveness of CM optimization strategy is validated in two simulation cases.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Industrial system monitoring includes fault diagnosis and anomaly detection, which have received extensive attention, since they can recognize the fault types and detect unknown anomalies. However, a separate fault diagnosis method or anomaly detection method cannot identify unknown faults and distinguish between different fault types simultaneously; thus, it is difficult to meet the increasing demand for safety and reliability of industrial systems. Besides, the actual system often operates in varying working conditions and is disturbed by the noise, which results in the intraclass variance of the raw data and degrades the performance of industrial system monitoring. To solve these problems, a metric learning-based fault diagnosis and anomaly detection method is proposed. Fault diagnosis and anomaly detection are adaptively fused in the proposed end-to-end model, where anomaly detection can prevent the model from misjudging the unknown anomaly as the known type, while fault diagnosis can identify the specific type of system fault. In addition, a novel multicenter loss is introduced to restrain the intraclass variance. Compared with manual feature extraction that can only extract suboptimal features, it can learn discriminant features automatically for both fault diagnosis and anomaly detection tasks. Experiments on three-phase flow (TPF) facility and Case Western Reserve University (CWRU) bearing have demonstrated that the proposed method can avoid the interference of intraclass variances and learn features that are effective for identifying tasks. Moreover, it achieves the best performance in both fault diagnosis and anomaly detection.

Making proper decision online in complex environment during the blast furnace (BF) operation is a key factor in achieving long-term success and profitability in the steel manufacturing industry. Regulatory lags, ore source uncertainty, and continuous decision requirement make it a challenging task. Recently, reinforcement learning (RL) has demonstrated state-of-the-art performance in various sequential decision-making problems. However, the strict safety requirements make it impossible to explore optimal decisions through online trial and error. Therefore, this article proposes a novel offline RL approach designed to ensure safety, maximize return, and address issues of partially observed states. Specifically, it utilizes an off-policy actor-critic framework to infer the optimal decision from expert operation trajectories. The "actor" in this framework is jointly trained by the supervision and evaluation signals to make decision with low risk and high return. Furthermore, we investigate a recurrent version of the actor and critic networks to better capture the complete observations, which solves the partially observed Markov decision process (POMDP) arising from sensor limitations. Verification within the BF smelting process demonstrates the improvements of the proposed algorithm in performance, i.e., safety and return.

The precise control of the spatiotemporal process in a roller kiln is crucial in the production of Ni-Co-Mn layered cathode material of lithium-ion batteries. Since the product is extremely sensitive to temperature distribution, temperature field control is of great significance. In this article, an event-triggered optimal control (ETOC) method with input constraints for the temperature field is proposed, which takes up an important position in reducing the communication and computation costs. A nonquadratic cost function is adopted to describe the system performance with input constraints. First, we present the problem description of the temperature field event-triggered control, where this field is described by a partial differential equation (PDE). Then, the event-triggered condition is designed according to the information of system states and control inputs. On this basis, a framework of the event-triggered adaptive dynamic programming (ETADP) method that is based on the model reduction technology is proposed for the PDE system. A critic network is used to approach the optimal performance index by a neural network (NN) together with that an actor network is used to optimize the control strategy. Furthermore, an upper bound of the performance index and a lower bound of interexecution times, as well as the stabilities of the impulsive dynamic system and the closed-loop PDE system, are also proved. Simulation verification demonstrates the effectiveness of the proposed method.

Modern industry processes are typically composed of multiple operating units with reaction interaction and energy-mass coupling, which result in a mixed time-varying and spatial-temporal coupling of process variables. It is challenging to develop a comprehensive and precise fault detection model for the multiple interconnected units by simple superposition of the individual unit models. In this study, the fault detection problem is formulated as a spatial-temporal fault detection problem utilizing process data of multiple interconnected unit processes. A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected unit processes. First, slow feature analysis (SFA) is implemented to extract temporal information that reveals the dynamic relevance of the process data. Then, an integration method of metric learning and prior knowledge is proposed to construct coupled spatial relationships based on temporal information. In addition, a variational graph attention autoencoder (VGATE) is suggested to extract temporal and spatial information for fault detection, which incorporates the dominances of variational inference and graph attention mechanisms. The proposed method can automatically extract and deeply mine spatial-temporal interactive feature information to boost detection performance. Finally, three industrial process experiments are performed to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed method dramatically increases the fault detection rate (FDR) and reduces the false alarm rate (FAR).

Latent variable-based process monitoring (PM) models have been generously developed by shallow learning approaches, such as multivariate statistical analysis and kernel techniques. Owing to their explicit projection objectives, the extracted latent variables are usually meaningful and easily interpretable in mathematical terms. Recently, deep learning (DL) has been introduced to PM and has exhibited excellent performance because of its powerful presentation capability. However, its complex nonlinearity prevents it from being interpreted as human-friendly. It is a mystery how to design a proper network structure to achieve satisfactory PM performance for DL-based latent variable models (LVMs). In this article, a variational autoencoder-based interpretable LVM (VAE-ILVM) is developed for PM. Based on Taylor expansions, two propositions are proposed to guide the design of appropriate activation functions for VAE-ILVM, allowing nondisappearing fault impact terms contained in the generated monitoring metrics (MMs). During threshold learning, the sequence of counting that test statistics exceed the threshold is considered a martingale, a representative of weakly dependent stochastic processes. A de la Peña inequality is then adopted to learn a suitable threshold. Finally, two chemical examples verify the effectiveness of the proposed method. The use of de la Peña inequality significantly reduces the minimum required sample size for modeling.

Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.

Multimode process monitoring plays a significant role in ensuring the stable operation of industrial processes under changing conditions. Due to the continuous emergence of new modes, some adaptive model updating methods are proposed. However, the updated model may forget important features learned in previous modes, thus reducing the monitoring performance. To address this problem, this article proposes a global information-based lifelong dictionary learning (GI-LDL) method for multimode process monitoring. Specifically, this article adopts dictionary atoms as the fundamental units for representing process data and proposes a method for measuring the importance of dictionary atoms based on global information. Then, to ensure that the dictionary retains its representational ability for both new and historical mode data during the mode updating process, a surrogate quadratic loss considering the importance is further proposed to penalize changes of important atoms. Compared with dictionary constraints, finer-grained atomic constraints ensure that the dictionary preserves important features of previous modes while learning features of new modes. Finally, considering that the number of modes in multimode industrial processes is often unknown in advance, this article explicitly derives analytical solutions for dictionary updating, thus it is capable of accommodating ever-increasing modes in real industrial processes. To verify the effectiveness and advancement of the proposed method, extensive experiments are elaborately designed, and experimental results indicate that the proposed method has precise monitoring capabilities for both historical and new modes.

The control of industrial processes which can be generally described by nonlinear time-delay interconnected systems is very important. A decentralized control method based on adaptive dynamic programming (ADP) is proposed in this article, which can solve the control and stability problem for nonlinear time-delay interconnected systems using past state data and interconnection information. First, the form of nonlinear interconnected systems with state delay is given. Based on such systems, the subsystems are reconstructed by adding the upper bound of the interconnection part to the cost function. New cost functions are constructed which include a series of Hamilton–Jacobi–Bellman (HJB) equations with Lyapunov–Krasovskii (L–K) function, where the past state data and interconnection information are employed. Then, the control problem can be dealt with by solving the HJB equations. The HJB equations can be solved by the critic learning method based on ADP, where the critic neural networks (NNs) are applied to estimate the optimal cost function. The weights can be calculated by the gradient descent method, and the control law can be obtained. Subsequently, the stability of the closed-loop system is proved in the sense of uniformly ultimately bounded (UUB). Finally, simulation results verify the effectiveness of the proposed method by a nonlinear time-delay interconnected system and two-stage chemical reactors.

Modern industrial process control suffers from various difficulties, such as multivariable, multiconstrained, multiobjective, and strong nonlinearity. Model predictive control (MPC) is an effective solution and is widely used in industrial processes. However, one limitation of MPC is that sufficient data are required to build accurate predictive models. To this end, this article proposes a knowledge-informed neural network MPC solution. First, a Hammerstein system structure knowledge extraction method based on sparse representation is proposed, which is able to extract system structure knowledge from a small amount of system operation data. Then, a knowledge-informed neural network model is designed, which combines the system structure knowledge to construct a neural network with a special structure, thus overcoming the problem of insufficient data during the model training. Finally, the knowledge-informed neural network model is embedded in the MPC framework, which can reduce the computational cost of rolling optimization while ensuring prediction performance. A numerical simulation and a pH neutralization process experiment are conducted to verify the feasibility and effectiveness of the proposed method.

This article proposes a distributed capture strategy optimization method for the pursuit-evasion game involving multiple unmanned surface vehicles. Considering the limited perception range of each pursuer, a multiagent proximal policy optimization method combined with a novel velocity control mechanism is utilized to guide the pursuers in approaching the evader and form a dynamic encirclement. Moreover, to facilitate deep reinforcement learning (DRL) training, a bidirectional gated recurrent unit feature network is constructed to extract the fixed-length vector representations from the variable-length observation sequences. In terms of the policy training, by employing virtual barriers and curriculum learning techniques during the training process, the generalization capabilities and convergence speed of the policy have been further improved. Finally, our method is compared with the other DRL methods through the comparative simulation experiments and virtual reality scene testing based on the gazebo three dimensional physics engine, verifying its significant advantages in the policy convergence speed, capture efficiency, and generalization capabilities.

Accurately measuring the slag-iron ratio (SIR) of the molten iron flow is of great significance for efficient slag-iron emission and safe production in the ironmaking process. Slag-iron recognition is challenging due to the high temperature, intense radiation, and heavy dust in the casting field. Efficient and intelligent online slag-iron recognition is urgently needed in ironmaking enterprises. To this end, this article innovatively presents a vision-driven slag-iron recognition method based on multiscale neighborhood adaptive clustering (MNAC) to achieve continuous high-precision SIR measurement of molten iron flow. First, a shortwave infrared high-speed imaging (SWIHI) system is designed and deployed to capture high-definition molten iron flow images. Then, a multialgorithm fusion key frame and region of interest (ROI) acquisition strategy is proposed to achieve stable acquisition of molten iron flow boundaries and prevent the ROI from being occluded by dust and objects. Finally, a novel multiscale neighborhood slag-iron recognition strategy and an adaptive density peak clustering algorithm are presented to achieve high-precision slag-iron recognition through the kernel density estimation of the membership of a single pixel in multiple neighborhoods. Industrial experiments and applications indicate that the proposed method can achieve slag-iron recognition continuously and accurately to provide reliable SIR data for blast furnace experts.

In order to address the simulation requirements arising from limited computational resources, non-reusable complex models, and low-latency demands for edge devices within the realm of the Industrial Internet of Things (IIoT), we have successfully defined and developed a Lightweight Simulation System(LSS) specifically designed for industrial applications. This system draws inspiration from the architectural principles of edge and fog computing. LSS leverages an innovative architectural paradigm known as Function as a Service (FaaS), which demonstrates an even finer granularity compared to the traditional microservices architecture. Within the framework of LSS, we have discretely compartmentalized distinct operational elements, such as data acquisition and storage, model computation, and model visualization, into Docker microservices. This strategic shift in design effectively alleviates the need for developers to be overly concerned with the intricacies of server management and domain-specific industrial knowledge. Consequently, this enables them to shift their focus towards determining "what needs to be accomplished," thereby transforming the granularity of work from being server-centric to a more task-centric perspective. In comparison to simulation platforms within other frameworks, LSS distinguishes itself through its remarkable attributes, which include a lightweight structure, minimal coupling, and superior real-time performance. These unique qualities substantially enhance the collaborative, shared, and reusable capabilities of simulation systems across various dimensions. To substantiate the viability of LSS, this paper provides empirical evidence by applying it to Electrolysis and partial cascade algorithms in the context of hydrometallurgy. The results underscore the fact that the LSS system significantly improves modeling efficiency, fosters the reusability of models and algorithms, and elevates the level of industrial automation across diverse processes.

Process modeling and intelligent decision-making play crucial roles in optimizing and controlling sintering processes. To tackle these tasks, we propose a novel approach that leverages a temporal convolutional network guided by working condition knowledge (WKGA-TCN) to predict the sintering condition. By incorporating sequential features, the model effectively captures the varying importance of different features. Subsequently, we simulate the state of the sintering process using our proposed method. To further enhance operational optimization, we introduce an intelligent decision-making strategy that combines dynamic matching of operating modes and a genetic algorithm. This strategy leverages historical data to learn from excellent operation cases while considering the inherent uncertainty in process data. Simultaneously, a model update strategy employing an event-triggered mechanism is introduced to guarantee the process model's comprehensive simulation of the current sintering process dynamics. Finally, we assess the effectiveness of the proposed method by applying it to actual industrial process data.

Smart manufacturing has become mainstream in the development of manufacturing industry, where Industrial Internet of Things plays a critical role. In this article, a systematic intelligent technique for procurement supply chain (PSC) optimization is proposed. In this technique, an integrated approach based on variational mode decomposition and long short-term memory network is used to predict the market price. Considering the factors, such as production plan and market fluctuation, a multiperiod dynamic purchasing model is built. A stacked autoencoder under bootstrap aggregation is then trained to evaluate suppliers automatically end-to-end based on various data. Finally, a multiobjective order allocation model is established considering the procurement costs and supplier scores, and solved by particle swarm optimization. The extensive experiments are performed using a realistic industrial application in a zinc smelter company. The experimental results demonstrate that the proposed technique greatly reduces labor costs, improves the efficiency of PSC, and reduces the procurement costs of the company.

In this paper, we address matrix-valued distributed stochastic optimization with inequality and equality constraints, where the objective function is a sum of multiple matrix-valued functions with stochastic variables and the considered problems are solved in a distributed manner. A penalty method is derived to deal with the constraints, and a selection principle is proposed for choosing feasible penalty functions and penalty gains. A distributed optimization algorithm based on the gossip model is developed for solving the stochastic optimization problem, and its convergence to the optimal solution is analyzed rigorously. Two numerical examples are given to demonstrate the viability of the main results.

The primary goal of multimodal process monitoring is to detect abnormalities or occurrence of faults. However, the profound challenge in the multimodal monitoring problem is that it is difficult to quickly distinguish the fault occurrence on a process mode from other operating modes. In this work, a Gaussian mixture model based variational Bayesian principal component analysis (GMM-VBPCA) is proposed. GMM is used to capture the global multimodal information where each Gaussian component of GMM represents a corresponding normal operating mode. VBPCA is employed to construct a probabilistic model for each operating mode. Using the weights of posterior probabilities from global GMM, local VBPCA models can then be fused to characterize the normal multimodal processes. In order to detect the occurrence of faults, Kullback-Leibler (KL) divergence of latents and model residuals of the multimodal process are used as the monitoring statistics that measure the deviation from the normal multimodal distribution. Owing to the variational local model, the posterior distribution of latents and model residuals of the GMM-VBPCA can characterize the process behavior for every test sample. Finally, GMM-VBPCA based monitoring statistics are compared with existing process monitoring methods through a simulated numerical example and an industrial hydrocracking process. Note to Practitioners—In this paper, a novel process monitoring statistics has been proposed that can aid the practitioners in accurately identifying the process faults in near real-time with minimal false alarm. Also, the sensitivity to small bias faults is higher that the traditional methods, thus enabling higher fault detection rate. Based on the proposed statistics, an online monitoring scheme has been proposed. Hence, it is useful for practitioners in quickly taking preventive measures to avoid catastrophe, and also taking corrective measures to bring the plant to normal operating range or scheduling maintenance in case of early detection of sensor or equipment failures.

Unlike general image segmentation, highly complex particle images have significant challenges in labeling and segmentation due to the information occlusion and texture disturbance. Aiming at the highly under-segmentation problem caused by complex particle image segmentation, this paper proposes a Semi-supervised Hybrid-training Particle Segmentation framework (SHPS) based on skeleton-guided pseudo-labels. First, a pre-trained model is obtained by training a popular segmentation algorithm on partially labeled data. Then, a Background Skeleton-guided Pseudo-label generation algorithm (BSP) is proposed to generate pseudo-labels closer to the ground truth in terms of structural integrity based on coarse segmentation. The final segmentation model is obtained by training a mixed dataset consisting of labeled data and pseudo-labels from another partition on the pre-trained model. The skeleton differences of pseudo-labels and coarse segmentation are added to the loss function. Experimental results show that our method achieves 84.4% accuracy on mIoU with uniform label data distribution, which is 2.1% higher than the accuracy of UNet and reduces the degree of under-segmentation.

3D froth information is a significant indicator of working condition recognition in froth flotation. To extract it accurately, the first and key step is camera calibration. Although there have been many camera calibration methods proposed in these years, they cannot handle well for binocular camera calibration in froth flotation. The main reason is that there is an open environment without support surface or special geometric shapes for camera calibration in froth flotation. Therefore, we propose a novel binocular camera calibration method based on key frame sequences and weighted normalized tilt difference. First, we extract a high-quality frame sequence by filtering out incomplete and out-of-focus images from the froth video. Then, we propose a method to measure the tilt degree of the planar plate and extract the key frame sequences of the planar plate based on the tilt difference. After that, we calculate the camera parameters by weighted normalized tilt difference under various poses. The proposed binocular camera calibration method uses the froth video to obtain accurate and robust camera parameters, and does not require auxiliary equipment. Experiments have demonstrated the effectiveness and flexibility of the proposed method in froth flotation.

Temperature field control is crucial for the comprehensive performance of Ni-Co-Mn layered cathode material that is the most important part of lithium-ion batteries. Starting from the aspect of a class of distributed parameter systems described by highly dissipative partial differential equations (PDEs), an event-triggered optimal control (ETOC) method based on adaptive dynamic programming (ADP) for the roller kiln temperature field is proposed. First, we formulate the event-triggered control problem of the temperature field under the general framework of PDE systems. Then, an event-triggered condition is designed based on the stability of the closed-loop PDE system, which also guarantees the upper bound of the performance index. Subsequently, ADP technology is adopted to realize the ETOC, where the critic network is employed to approximate the optimal value function. Since the studied system can be regarded as an impulsive dynamic system with flow dynamics and jump dynamics simultaneously, the stability of the impulsive dynamic system combined with the ADP-based closed-loop PDE system is proved. Finally, simulation results on the temperature field verify the effectiveness of the proposed method.

In real industrial processes, factors, such as the change in manufacturing strategy and production technology lead to the creation of multimode industrial processes and the continuous emergence of new modes. Although the industrial SCADA system has accumulated a large amount of historical data, which can be used for modeling and monitoring multimode processes to a certain extent, it is difficult for the model learned from historical data to adapt to emerging modes, resulting in the model mismatch. On the other hand, updating the model with data from new modes allows the model to continuously match the new modes, but it may cause the model to lose the ability to represent the historical modes, resulting in "catastrophic forgetting." To address these problems, this article proposed a jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updated the model by learning the data of new modes, so that the model can adaptively match the newly emerged modes. At the same time, a similarity metric was put forward to guarantee the representation ability of the proposed method for historical data. A numerical simulation experiment, the CSTH process experiment, and an industrial roasting process experiment indicated that the proposed JMSDL method can match new modes while maintaining its performance on the historical modes accurately. In addition, the proposed method significantly outperforms the state-of-the-art methods in terms of fault detection and false alarm rate.

This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE)-Softmax, for the fault identification of complex industrial processes (CIPs). Specifically, sparse denoising autoencoder (SDAE) is established by integrating a sparse AE (SAE) with a denoising AE (DAE) for the low-dimensional but intrinsic feature representation of the CIP monitoring data (CIPMD) with possible noise contamination. SSDAE-Softmax is established by stacking multiple SDAEs with a layerwise pretraining procedure, and a Softmax classifier with a global fine-tuning strategy. Furthermore, SSDAE-Softmax hyperparameters are optimized by a relatively new global optimization algorithm, referred to as the state transition algorithm (STA). Benefiting from the deep learning-based feature representation scheme with the STA-based hyperparameter optimization, the underlying intrinsic characteristics of CIPMD can be learned automatically and adaptively for accurate fault identification. A numeric simulation system, the benchmark Tennessee Eastman process (TEP), and a real industrial process, that is, the continuous casting process (CCP) from a top steel plant of China, are used to validate the performance of the proposed method. Experimental results show that the proposed SSDAE-Softmax model can effectively identify various process faults, and has stronger robustness and adaptability against the noise interference in CIPMD for the process monitoring of CIPs.

Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete data obstruct the effective use of data and degrade the performance of data-driven models. Numerous imputation algorithms have been proposed to deal with missing values, primarily based on supervised learning, that is, imputing the missing values by constructing a prediction model with the remaining complete data. They have limited performance when the amount of incomplete data is overwhelming. Moreover, many methods have not considered the autocorrelation of time-series data. Thus, an adaptive-learned median-filled deep autoencoder (AM-DAE) is proposed in this study, aiming to impute missing values of industrial time-series data in an unsupervised manner. It continuously replaces the missing values by the median of the input data and its reconstruction, which allows the imputation information to be transmitted with the training process. In addition, an adaptive learning strategy is adopted to guide the AM-DAE paying more attention to the reconstruction learning of nonmissing values or missing values in different iteration periods. Finally, two industrial examples are used to verify the superior performance of the proposed method compared with other advanced techniques.

Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it is often difficult for IIoT to collect enough tagged and highly reliable data, which further degrades the process monitoring performance and makes the monitoring results not trustworthy. In order to reduce the negative impact of these unreliable factors, a self-weighted dictionary learning process monitoring method is proposed. In particular, a label propagation classifier is implemented from the labeled data to unlabeled data to obtain a credible label prediction. Subsequently, considering the interference of low-quality data and label information, we reweight the classification loss and label-consistency constraints to enhance the trustworthiness of feature extraction. Finally, a novel iterative optimization algorithm that combines the block coordinate descent method with the alternating direction multiplier method is developed to ensure the convergence speed of the learned classifier and dictionary. Extensive experiments indicate that the proposed method can guarantee the trustworthiness of the process monitoring results.

Accurate prediction of quality variables that are difficult to measure is crucial for industrial process control and optimization. However, the fluctuations in raw material quality and production conditions may cause industrial process data to be distributed in multiple working conditions. The data under the same working condition show similar characteristics, which are often defined as one data mode. Hence, the overall process data exhibit multimode characteristics, which brings great challenges in developing a uniform prediction model. Besides, the noninterpretability of the existing data-driven prediction models brings great resistance to their practical application. To address these issues, this article proposes a novel data mode related interpretable transformer network (DMRI-Former) for predictive modeling and key sample analysis in industrial processes. In DMRI-Former, a novel data mode related interpretable self-attention mechanism is designed to enhance the homomode perceptual ability of each individual mode while also capturing cross-mode features of different modes. Moreover, the key samples under different modes can be discovered using DMRI-Former, which further improves the interpretability of the modeling process. Finally, the superiority of the proposed DMRI-Former is verified in two real-world industrial processes compared to other state-of-the-art methods.

Concerning the problems of harrowing extraction and poor classification accuracy of fault features in rolling bearing vibration signals, a fault feature selection and classification method based on multi-indicator fusion is proposed. First, the original signal is decomposed through the improved complementary ensemble local mean decomposition method into several physically meaningful product functions (PF) and single residual components; then, the three indicators of kurtosis, correlation coefficient, and Kulback–Leibler divergence are combined to extract the most suitable PF components for signal reconstruction. Ultimately, the reconstructed signal's multidomain characteristics and entropy value features are retrieved and fed into the LightGBM classifier for classification in order to achieve an intelligent diagnosis of rolling bearing problems. The statistical results demonstrate that the proposed method can efficiently identify the functional PF components and has notable benefits in extracting features from diverse experimental datasets and detecting faults.

In this brief, stabilization of Boolean networks (BNs) by flipping a subset of nodes is considered, here we call such action state-flipped control. The state-flipped control implies that the logical variables of certain nodes are flipped from 1 to 0 or 0 to 1 as time flows. Under state-flipped control on certain nodes, a state-flipped-transition matrix is defined to describe the impact on the state transition space. Weak stabilization is first defined and then some criteria are presented to judge the same. An algorithm is proposed to find a stabilizing kernel such that BNs can achieve weak stabilization to the desired state with in-degree more than 0. By defining a reachable set, another approach is proposed to verify weak stabilization, and an algorithm is given to obtain a flip sequence steering an initial state to a given target state. Subsequently, the issue of finding flip sequences to steer BNs from weak stabilization to global stabilization is addressed. In addition, a model-free reinforcement algorithm, namely the Q -learning ( [Formula: see text]) algorithm, is developed to find flip sequences to achieve global stabilization. Finally, several numerical examples are given to illustrate the obtained theoretical results.

This article investigates the design of pinning controllers for state feedback stabilization of probabilistic Boolean control networks (PBCNs), based on the condensation digraph method. First, two effective algorithms are presented to achieve state feedback stabilization of the considered system from the perspective of condensation digraph. One is to find the desired matrix, and the other is to search for the minimum number of pinned nodes and specific pinned nodes. Then, all the mode-independent pinning controllers can be designed based on the desired matrix and pinned nodes. Several examples are delineated to illustrate the validity of the main results.

This article addresses an improved co-design method of dynamical controller and asynchronous integral-type event-triggered mechanisms (ETMs) for a class of linear systems with external disturbances and measurement noises. First, a dynamical controller is designed for a linear disturbed plant, and two independent integral-type ETMs are synthesized to be embedded in the plant output and control input channels. Then, an augmented hybrid system is constructed, in which the integral-type ETMs in the two channels are not required to be activated synchronously and both channels are affected by their measurement noises. The proposed asynchronous integral-type event-triggered control (IT-ETC) scheme for two-fold signal transmissions can not only avoid the Zeno behavior strictly, but save more communication resources than the static event-triggered control (S-ETC) strategy. Moreover, a criterion is provided to guarantee that the hybrid system is${\mathcal {L}}_{2}$stable, and an improved co-design method is further synthesized to simultaneously obtain the design parameters of ETMs and feasible solutions of the dynamical controller. As a result, a tradeoff can be achieved between the robustness of the control system and the occupancy rate of communication resources. Compared with the S-ETC strategy, the simulation results have illustrated the effectiveness and superiority of the proposed asynchronous IT-ETC scheme.

With the development of information and communication technologies, industrial cyber–physical systems (ICPSs) have accumulated a large amount of data, which enables us to convert data into industrial insight. However, since the industrial process of ICPS is always complicated and large scale, the raw data only contain a few operation condition information, which brings challenges to process monitoring and control. Thus, an efficient operation condition division method for ICPS is necessary. Although many operation condition division methods have been proposed, they were mainly relying on the static characteristics and ignored how the industrial process varies dynamically. Meanwhile, with the industrial process running, there may exist some new operation conditions that make the operation condition division task even more difficult. In order to grasp the static and dynamic features simultaneously of the industrial process and divide operation conditions accurately, we proposed an operation condition division method based on joint static and dynamic analysis with incremental learning. In detail, the slow feature analysis (SFA) and self-organizing map (SOM) network were proposed to extract the static and dynamic features jointly. Then, a division strategy was proposed to distinguish the operation condition changing points. For the new operating condition, we designed an incremental learning method based on the SOM network, which can update the operation condition model in real time. Extensive experiments, including a numerical simulation, two benchmark processes, and an industrial roasting process demonstrate that the proposed method can identify the operation conditions of the raw data in ICPS accurately and efficiently.

Multiagent dynamic task assignment of forest fires is a complicated optimization problem because it requires the consideration of multiple factors, such as the spread speed of fires, firefighting speed of agents, the movement speed of agents, and the number of deployed agents. In this article, we investigate multiagent dynamic task assignment based on a forest fire point model, the objective of which is to minimize task completion time. First, we establish a model for the spread of fire and dynamic task assignments. Second, we prove that the optimal static task assignment always makes all task completion times the same under certain assumptions. Furthermore, we calculate the optimal solution to the static task assignment problem assuming no travel time for the agents, which provides the theoretical basis for the initial deployment and dynamic deployment. Third, we propose a dynamic task assignment scheme based on the global information, which ensures that every reassignment reduces the task completion time and makes all task completion times close to each other. Finally, the simulation is carried out on the MATLAB platform to verify the performance of the proposed dynamic task assignment scheme by comparing with a multistage global auction algorithm. We hope that this work provides insight for decision-makers designing reasonable assignment strategies based on the model and solving assignment optimization problem in different situations.Note to practitioners—The forest firefighting problem considered in this article is a typical multitask and multistage optimization problem. Many searching algorithms for multistage optimization problem are available in the existing literature. However, one of the main challenges is that the time of searching increases exponentially with the number of stages. This work first proves that the tasks are completed in the minimum amount of time, under the constraint of one-shot assignment. This finding helps us to evaluate the gap between the searching algorithm and the optimal solution. In addition, in practice, if the underlying dynamic process can be modeled or partially modeled, then we can predict the behavior of future stages and reduce the searching domain. If a model is available, then we can also adjust the assignment scheme dynamically based on the principle that each adjustment would reduce the total time of tasks completion. In this article, we establish a dynamical fire-spreading model and propose a model-based solution to the multistage optimization problems. The findings in this work can serve as a supplement to the existing optimization algorithms.

The development of industrial cyber-physical system (ICPS) provides a tight connection between the digital model and the industrial physical plant, which enables to use the data-driven methods to reflect the state of process running in the real world. However, due to the noisy and harsh industrial environment, the collected data are often corrupted to some extent. If the corrupted data are not detected in time, the data-driven model will inevitably degenerate and induce a poor process monitoring performance. In addition, the nonlinear characteristics between process variables due to the high complexities in physical plant bring challenges to the data-driven methods. In this article, a robust kernel dictionary learning method, which can overcome the negative influence of outliers and simultaneously extracts the nonlinear characteristics of industrial process, is proposed to address the above problems in ICPS. Our extensive experiments demonstrate that the proposed method has achieved significantly better and stable performance to deal with outlier detection and process monitoring in ICPS.Note to Practitioners—In order to mitigate the impacts due to process noise and outliers, a robust kernel dictionary learning method is proposed to improve the accuracy and stability of the process monitoring of industrial cyber-physical systems. This method considers the process noise, sparse outlier, as well as the nonlinear characteristic of industrial systems for improving the accuracy and stability of monitoring. Compared with many state-of-the-art methods, the proposed method can detect the outliers in the training dataset adaptively, which is more applicable to the real industrial system.

Continuous and accurate depth information of blast furnace burden surface is important for optimizing charging operations, thereby reducing its energy consumption and CO2emissions. However, depth estimation for a single image is challenging, especially when estimating the depth of burden surface images in the harsh internal environment of the blast furnace. In this paper, a novel method that is based on edge defocus tracking is proposed to estimate the depth of burden surface images with different morphological characteristics. First, an endoscopic video acquisition system is designed, key frames of burden surface video in stable state are extracted based on feature point optical flow method, and the sparse depth is estimated by using the defocus-based method. Next, the burden surface image is divided into four subregions according to the distribution characteristics of the burden surface, the edge line trajectories and an eight-direction depth gradient template are designed to develop depth propagation rules. Finally, the depth is propagated from edge to the entire image based on edge line tracking method. The experimental results show that the proposed method can accurately and efficiently estimate the depth of the burden surface and provide key data support for optimizing the operation of blast furnace.

The admissible consensus tracking problem of nonlinear singular multiagent systems (SMASs) with time-varying delay, uncertainties, and external disturbances under jointly connected topologies is investigated in this article. First, the sliding-mode control (SMC) is applied to effectively reduce the adverse effects of uncertainties and nonlinearities of systems. Then, by the combination of admissible analysis, the Cauchy convergence criterion, and SMC, the sufficient conditions for the admissible consensus tracking and disturbance rejection of SMASs under jointly connected topologies are provided. Furthermore, a distributed SMC law is designed such that the sliding-mode dynamics trajectories reach the sliding surface in finite time. Finally, the simulation results are utilized to indicate the effectiveness of the presented methods.

The development of cloud manufacturing enables data-driven process monitoring methods to reflect the real industrial process states accurately and timely. However, traditional process monitoring methods cannot update learned models once they are deployed to edge devices, which leads to model mismatch when confronted time-varying data. In addition, limited resources on the edge prevent it from deploying complex models. Therefore, this article proposes a novel cloud-edge collaborative process monitoring method. First, historical data of industrial processes are collected to establish a dictionary learning model and train the dictionary and classifier in the cloud. Then, the model is simplified and deployed to the edge. The edge layer monitors the process states, including fault detection and working condition recognition, and determines whether a model mismatch has occurred based on an error-triggered strategy. Both numerical simulation and industrial roasting process results verify the superiority of the proposed method.

Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.

An industrial endoscope can monitor the abnormal conditions of the blast furnace burden layer by detecting the burden surface. Aiming at the problem of a small area of burden surface detected by industrial endoscopes due to the harsh environment of high temperature in blast furnaces, a novel optimization method of the installation direction of industrial endoscopes is proposed. First, a modeling method based on the three-dimensional coordinate transformation was proposed to quantitatively describe the relationship between the installation direction and imaged burden surface area. Then, we designed the optimization program based on particle swarm optimization to maximize the imaged burden surface area. Finally, the problems existing in field applications were considered, and the corresponding model of imaged burden surface area was established for the application. Experimental results show that the proposed method can effectively increase the imaged burden surface area, which provides a guarantee for the stable operation of blast furnaces.

Concrete cracks are one of the most apparent indicators for possible structural deterioration and need to be periodically inspected. However, for current image-based automated crack inspection techniques, accurate and detailed crack quantification and assessment remain a challenging task. Most of these techniques require high-quality input images, which may be difficult to ensure in practice. Besides, simply merging crack detections from multiple images to generate a large crack map may result in an inaccurate outcome for crack severity assessment. In this article, a novel crack quantification framework is proposed to identify complete crack geometric properties utilizing a set of unordered inspection images. To realize this, cracks in images are detected by an instance segmentation convolutional neural network. Subsequently, the crack segmentations from multiple separate images are systematically aggregated through voxel-based reconstruction and Bayesian data fusion. This framework outputs a crack model that can retrieve accurate geometric properties of each crack segment by recognizing the crack's inherent branching patterns. The capability and performance of the proposed crack quantification framework are validated on cracked concrete specimens in a laboratory setting. Also, a field test on a cracked concrete wall was carried out using images captured by a UAV to demonstrate the efficacy of the proposed framework in practical conditions.

Accurate and in-time performance monitoring plays a great role in industrial processes. However, since the labeled performances are usually measured by some special devices with a relatively long-time interval, the current deep neural networks for the performance monitoring always treat them only as an output. But actually, in the industrial process like froth flotation, labeled performance at the previous moment could also offer valuable information for performance monitoring at the current moment, especially under unstable working conditions. Therefore, we propose a Siamese time series and difference network (STS-D net), which integrates input features at different time steps and labeled performance at the previous moment effectively. In this article, the proposed STS-D net includes two sub-networks. One is the Siamese time series network, which aims to extract effective and uniform feature representations for the input time series at the current and previous moments; the other is the difference network, which integrates the feature representations of the two input time series with labeled performance at the previous moment to predict the performance at the current moment in an incremental way. Effectiveness of the proposed STS-D net is validated in a real-world froth flotation process.

Hydraulic systems are a class of typical complex nonlinear systems, which have been widely used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent fault diagnosis problem of hydraulic systems has received increasing attention for it can increase operational safety and reliability, reduce maintenance cost, and improve productivity. However, because of the high nonlinear and strong fault concealment, the fault diagnosis of hydraulic systems is still a challenging task. Besides, the data samples collected from the hydraulic system are always in different sampling rates, and the coupling relationship between the components brings difficulties to accurate data acquisition. To solve the above issues, a deep learning model with multirate data samples is proposed in this article, which can extract features from the multirate sampling data automatically without expertise, thus it is more suitable in the industrial situation. Experiment results demonstrate that the proposed method achieves high diagnostic and fault pattern recognition accuracy even when the imbalance degree of sample data is as large as 1:100. Moreover, the proposed method can increase about 10% diagnosis accuracy when compared with some state-of-the-art methods.

Due to technical or economic limitations, timely measuring quality-relevant key performance indicators (KPIs) of complex industrial processes (CIPs), especially the chemical composition-related indexes, is intractable. Process monitoring image sequences (PMISs) usually involve significant information about the operation states and KPIs. Thus, soft sensor-based online KPI inference by incorporating process monitoring variables (TPMVs) and PMISs is more promising. However, the extremely inconsistent sampling rates with different expression forms and concerning aspects between PMISs and TPMVs lead to a great challenge in the soft sensor modeling by combining PMISs and TPMVs. In this article, a self-attention dual-channel deep network (SADCDN)-based soft sensor model for the end-to-end online KPI detection/prediction is proposed. Specifically, one channel adopts the gated recurrent unit (GRU) network to extract intrinsic time-series features in TPMVs, and simultaneously the other channel introduces a novel frame-dilated convolution fusion neural network (FDCFNN) to extract intrinsic spatiotemporal features from PMISs to address the sampling inconsistence between PMISs and TPMVs. Successively, dual-channel network features with different concerning aspects are weighted and fused based on an introduced self-attention mechanism to bridge the gap of sampling rates and concerning aspects between PMISs and TPMVs for the soft sensor modeling. Practical application results on two real industrial processes, the bauxite flotation process and the sintering process of a cement rotary kiln, have demonstrated the effectiveness and superiority of the proposed dual-channel model, laying a foundation for the process optimization of CIPs.

An optimal switching heating control strategy based on minimum margin (OS-MM) is proposed for high efficiency, low energy consumption, and high-uniformity fine control in a large-scale vertical quench furnace. This work was stimulated by the need for solving the contradiction between the heating rate and temperature overshoot to achieve a rapid rise in temperature with no overshoot. Based on a three-dimensional (3-D) transient temperature field model of the quenching furnace, the temperature field optimization control problem is transformed into a boundary optimization control problem of rapid heating, whereby it is rigorously proved that the fastest rise in temperature is attained by the full-power heating, which satisfies the bang-bang characteristics. The key parameter that determines the overshoot in the full-power heating mode is introduced and defined as the minimum margin in the temperature-rising process. The analytical expression for solving the minimum margin is calculated by the eigenfunction expansion method and using the strong metal thermal inertia of the internal temperature field, the OS-MM method is designed to stop the heating in advance at a calculated switching point to optimize full-power heating, thus conserving energy by reducing its consumption. The results of simulation and industrial experiments show that this strategy greatly shortens the temperature adjusting time, significantly reduces energy consumption, and effectively improves the control performance indices, such as overshoot and static error in the temperature holding period of the thermal treatment process, as compared to the existing heating methods.

The control reliability of model predictive control is largely determined by the accuracy of the process model. The Hammerstein–Wiener (HW) model is an important nonlinear process modeling technique that has obtained great success in some process industries. Disturbances result in model mismatch and steady-state deviation, but little effort has been devoted to the coupling effects and inertial information in measured disturbances. In addition, few studies try to construct a disturbance observer (DO) to alleviate unmeasured disturbances. The present work proposes prompt disturbance rejection. First, a spatial–temporal long short-term memory-based measurable disturbance encoder is devised to analyze time-series information from measured disturbances and their coupling effects. The encoder can further clarify the status of inertial interference components and the disturbance intensity. Second, a DO is designed to estimate and attenuate unmeasured disturbances. Third, to create the new HW network, which is improved by integrating the disturbance encoder and observer differential, neural networks are used as nonlinear parts. Finally, a model predictive controller based on this improved model is constructed for real-time industrial process control. Simulation comparison experiments have demonstrated the superiority of the proposed methods. Real industry application in the country’s largest lead-zinc froth flotation plant in China validated the proposed model’s effectiveness in controlling chemical reagents.

We investigate the stability of Boolean networks (BNs) with impulses triggered by both states and random factors. A hybrid index model is used to describe impulsive BNs. First, several necessary and sufficient conditions for forward completeness are obtained. Second, based on the stability criterion of probabilistic BNs and the forward completeness criterion, the necessary and sufficient conditions for the finite-time stability with probability one and the asymptotical stability in distribution are presented. The relationship between these two kinds of stability is discussed. Last, examples and time-domain simulations are provided to illustrate the obtained results.

Froth color can be referred to as a direct and instant indicator to the key flotation production index, for example, concentrate grade. However, it is intractable to measure the froth color robustly due to the adverse interference of time-varying and uncontrollable multisource illuminations in the flotation process monitoring. In this article, we proposed an illumination-invariant froth color measuring method by solving a structure-preserved image-to-image color translation task via an introduced Wasserstein distance-based structure-preserving CycleGAN, called WDSPCGAN. WDSPCGAN is comprised of two generative adversarial networks (GANs), which have their own discriminators but share two generators, using an improved U-net-like full convolution network to conduct the spatial structure-preserved color translation. By an adversarial game training of the two GANs, WDSPCGAN can map the color domain of froth images under any illumination to that of the referencing illumination, while maintaining the structure and texture invariance. The proposed method is validated on two public benchmark color constancy datasets and applied to an industrial bauxite flotation process. The experimental results show that WDSPCGAN can achieve illumination-invariant color features of froth images under various unknown lighting conditions while keeping their structures and textures unchanged. In addition, WDSPCGAN can be updated online to ensure its adaptability to any operational conditions. Hence, it has the potential for being popularized to the online monitoring of the flotation concentrate grade.

In this paper, the containment problem with nonconvex velocity constraints is studied for second-order discrete-time multiagent systems. A distributed projection-based algorithm is proposed for all followers to be gathered in the convex area formed by multiple stationary leaders. It is revealed that the algorithm can solve the velocity-constrained containment problem with communication delays and switching networks provided that each follower has at least a directed path from some leaders to itself in the union of graphs. The main analysis approaches include model transformation techniques, Lyapunov function, and convexity analysis. A simulation example is also included to elucidate the obtained result.

In modern large buildings, the chilled water system regulates the indoor temperature through a series of heat exchanges. This article studies the optimal control problem of the chilled water system and proposes an ensemble learning method for the cooling load prediction under different operation conditions with imbalance sample distribution. A new control strategy is afterwards developed for optimal selection of the process control inputs that guarantee the demand for cooling load with a lower energy consumption. The optimal control strategy is also learned in real time using a cloud edge terminal form, which can be used for big data modeling and increase the effectiveness of the system response. The proposed method is applied to a real high-rise building, and the results show a significant improvement in the proposed prediction model and the optimal control strategy, compared to the state-of-the-art methods. Regarding manual operation, the control strategy decreased the energy consumption by 5.59%, and, on average, 35 645 kWh of electric energy per month was saved.

Data-driven process monitoring methods have attracted many attentions and gained wide applications. However, the real industrial process data are much more complex which is characterized by multimode, high dimensional, corrupted, and less labeled data. In order to eliminate these unfavourable factors simultaneously, a semisupervised robust projective and discriminative dictionary learning method is proposed. First, a semisupervised strategy is introduced to label unsupervised training data. Then, by utilizing low-rank and sparse features of raw data and outliers, a robust decomposition method is used to obtain clean data. After that, a simultaneously projective and discriminative model is proposed to extracting the feature of the low-rank clean data. Finally, the projection matrix and global dictionary, as well as the threshold are obtained through iterative dictionary learning. This hybrid framework provides a robust model for process monitoring and mode identification, and its efficiency is demonstrated by both synthetic examples and real industrial process cases.

This article presents a simple yet powerful online flotation process working condition (FPWC) discrimination approach based on the sparse representation of froth images. It learns a local Gabor pattern-based discriminative dictionary with a linear classification model simultaneously for the FPWC identification by solving a sparsity-constrained optimization problem. The proposed method tends to achieve similar and distinct sparse codes of froth images for the same and different FPWCs, respectively, facilitating the accurate FPWC identification. To ensure the adaptability of the FPWC discrimination model, an incremental learning-based online model updating procedure is further derived to monitor the dynamically changing characteristics of FPWCs based on an introduced sparsity discrimination index. The proposed method was validated on an industrial preferential lead-flotation subcircuit process. The prototype monitoring system with extensive confirmatory and comparative experiments shows the effectiveness and superiority of the proposed method, which lays a foundation for the optimal control of industrial flotation processes.

Industrial processes often operate in the complex dynamic environment. They challenge the cost-effective and reliable operation of industrial processes. This article proposes a multiobjective optimization and control strategy for the iron removal process to be optimally operated in the dynamic environment. In the optimization layer, multiobjective optimization and local optimization problems, minimizing the process cost and maximizing economic index, are developed to obtain the optimal set-points of the outlet ferrous ion concentrations. In the control layer, model predictive control based on the system dynamic model is constructed to control the outlet ferrous ion concentrations to track the set-points. To reduce the influences from dynamic disturbances and improve the tracking performance, we use least squares support vector machine (LSSVM) to establish the correction model. Fuzzy-logic-based compensation is proposed to compensate the set-points according to the priori and posteriori information. Finally, simulations using real-world plant data are carried out to verify the effectiveness of the proposed control strategy. The simulation results demonstrate that the proposed control strategy achieves a satisfactory tracking performance with less process consumption. It improves the iron content in the goethite precipitate, which has more commercial value. The proposed control strategy can make the plant more profitable.

With the rapid development of deep neural network (DNN), many DNN-based models for performance monitoring have been developed recently. However, some challenges still exist in the industrial performance monitoring: 1) different sample rates and time delays between the inputs and labeled performance; 2) a light-weight DNN architecture. Under this circumstance, we design a DNN named feature reconstruction-regression network (FR-R net) in this article. First, we extract the feature vector series as the input feature in order to capture the dynamic temporal information of the input data. Then, we design a feature reconstruction network with a weight-shared kernel network and fixed positional encoding to generate a reconstructed feature vector. Finally, we send the reconstructed feature vector into fully connected layers as a regression network to link the labeled performance. The effectiveness of the proposed FR-R net is validated on both a simulation case and an industrial froth flotation process.

In industrial processes, inferential sensors have been extensively applied for prediction of quality variables that are difficult to measure online directly by hard sensors. Deep learning is a recently developed technique for feature representation of complex data, which has great potentials in soft sensor modeling. However, it often needs a large number of representative data to train and obtain a good deep network. Moreover, layer-wise pretraining often causes information loss and generalization degradation of high hidden layers. This greatly limits the implementation and application of deep learning networks in industrial processes. In this article, a layer-wise data augmentation (LWDA) strategy is proposed for the pretraining of deep learning networks and soft sensor modeling. In particular, the LWDA-based stacked autoencoder (LWDA-SAE) is developed in detail. Finally, the proposed LWDA-SAE model is applied to predict the 10% and 50% boiling points of the aviation kerosene in an industrial hydrocracking process. The results show that the LWDA-SAE-based soft sensor is superior to multilayer perceptron, traditional SAE, and the SAE with data augmentation only for its input layer (IDA-SAE). Moreover, LWDA-SAE can converge at a faster speed with a lower learning error than the other methods.

Analysis and design of steady states representing cell types, such as cell death or unregulated growth, are of significant interest in modeling genetic regulatory networks. In this article, the steady-state design of large-dimensional Boolean networks (BNs) is studied via model reduction and pinning control. Compared with existing literature, the pinning control design in this article is based on the original node's connection, but not on the state-transition matrix of BNs. Hence, the computational complexity is dramatically reduced in this article from O(2n× 2n) to O(2 × 2'), where n is the number of nodes in the large-dimensional BN and r <; n is the largest number of in-neighbors of the reduced BN. Finally, the proposed method is well demonstrated by a T-LGL survival signaling network with 18 nodes and a model of survival signaling in large granular lymphocyte leukemia with 29 nodes. Just as shown in the simulations, the model reduction method reduces 99.98% redundant states for the network with 18 nodes, and 99.99% redundant states for the network with 29 nodes.

This article addresses the design and implementation of observer-based sliding mode controller with H∞performance for underactuated rendezvous system under the constraints of fault signals, maximum input, and disturbances. The nonlinear constrained model considered in this article is an underactuated system since the number of control input variables is less than that of state variables, besides, owing to the full state information is unavailable or not so accurate for the controller design, an observer is first constructed to estimate the full state of system, and then the state of observer is adopted to synthesize the integral-type sliding surface function. Considering three fault signals, input constraint, and disturbances, the stability criteria with H∞performance are derived based on Lyapunov method and the ellipsoidal approximation algorithm. Furthermore, the sliding mode control law is formulated to guarantee the sliding mode dynamic could be driven onto the sliding surface and remain there for subsequent time. Finally, the simulation experiments are implemented to verify the effectiveness of the proposed scheme for the underactuated rendezvous system.

This paper addresses the admissible consensus problem for homogenous descriptor multiagent systems with undirected graphs and disturbances. First, to achieve the consensus objective and attenuate the effects of disturbances, the distributed controllers are formulated and the admissible consensus problems are formulated. Another improvement emphasizes that the proposed approach can simplify the complexity of the admissible consensus analysis by using the state transformation during the admissible analysis and disturbances suppression analysis. Moreover, based on Riccati inequalities and linear matrix inequalities, two necessary and sufficient conditions are presented to ensure the admissible consensus and disturbances suppression objectives. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed approaches.

In genetic regulatory networks, a stable configuration can represent the evolutionary behavior of cell death or unregulated growth in genes. We present analytical investigations on output feedback stabilizer design of Boolean networks (BNs) to achieve global stabilization via the semi-tensor product method. Based on network structure information describing coupling connections among nodes, an output feedback stabilizer is designed to achieve global stabilization. Compared with the traditional pinning control design, the output feedback stabilizer design is not based on the state transition matrix of BNs, which can efficiently determine pinning control nodes and reduce computational complexity. Our proposed method is efficient in that the calculation of the state transition matrix with dimension 2 n × 2 n is avoided; here n is the number of nodes in a BN. Finally, a signal transduction network and a D. melanogaster segmentation polarity gene network are presented to show the efficiency of the proposed method. Results are shown to be simple and concise, compared with traditional pinning control for BNs.

In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs are considered as continuous-time linear multi-input multioutput (MIMO) systems with random reference noises. The controlled plants include unstable poles and nonminimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree-of-freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises [the reference noise power distribution (RNPD) and its directions], and the characteristics of communication constraints. The characteristics of communication constraints include communication noise power distribution (CNPD); quantization noise power distribution (QNPD), and their distribution directions; transform bandwidth allocation (TBA); transform encoder-decoder allocation (TEA), and their allocation directions; and NMP zeros and MP part of bandwidth. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) can always affect the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channel. Finally, an instance is given for verifying the effectiveness of the theoretical outcomes.

This paper presents an investigation of the optimal estimation of state for Boolean control networks subject to stochastic disturbances. The disturbances are modeled as independently and identically distributed processes that are assumed to be both mutually independent and independent of the current and the historical states. An iterative algorithm is proposed to calculate the conditional probability distribution of the state given the output measurements. This algorithm is applied to the problems of minimum mismatching estimation and maximum posterior estimation of the state. An example is provided to illustrate the proposed results.

In this paper, we study the consensus problem for a class of linear multiagent systems, where the communication networks are directed. First, a dynamic event-triggering mechanism is introduced, including some existing static event-triggering mechanisms as its special cases. Second, based on the dynamic event-triggering mechanism, a distributed control protocol is developed, which ensures that all agents can reach consensus with an exponential convergence rate. Third, it is shown that, with the dynamic event-triggering mechanism, the minimum interevent time between any two consecutive triggering instants can be prolonged and no agent exhibits Zeno behavior. Finally, an algorithm is provided to avoid continuous communication when the dynamic event-triggering mechanism is implemented. The effectiveness of the results is confirmed through a numerical example.

This article investigates the partial stabilization problem of probabilistic Boolean control networks (PBCNs) under sample-data state-feedback control (SDSFC) with a control Lyapunov function (CLF) approach. First, the probability structure matrix of the considered PBCN is represented by a Boolean matrix, based on which, a new algebraic form of the system is obtained. Second, we convert the partial stabilization problem of PBCNs into the global set stabilization one. Third, we define CLF and its structural matrix under SDSFC. It is found that the existence of a CLF is equivalent to that of SDSFC. Then, a necessary and sufficient condition is obtained for the existence of CLF under SDSFC, based on which, all possible sample-data state-feedback controllers and corresponding structural matrices of CLF are designed by two different methods. Finally, examples are given to illustrate the efficiency of the obtained results.

This paper presents a froth image statistical modeling-based online flotation process operation-state identification method by introducing a biologically inspired Gabor wavelet transform in accordance with the physiological findings in the biological vision system. It derived the latent probabilistic density models of these biologically inspired Gabor filtering responses (GFRs) based on a versatile intermediate probability modeling frame, Gaussian scale mixture model. It has demonstrated that both the real and the imaginary representation of GFR obey a Laplace distribution. Accordingly, the amplitude representation of GFR obeys a Gamma distribution. Whereas the phase representation of GFR is an important yet frequently ignored aspect in Gabor-based signal analysis; it is demonstrated to be a periodic distribution and can be expressed by a von Mises-like distribution model. Successively, a local spline regression (LSR)-based classifier that the maps scattered statistical feature points of froth images directly to the operation-state labels smoothly is introduced for the operation-state recognition. Extensive confirmatory and comparative experiments on an industrial-scale bauxite flotation process demonstrate the effectiveness and superiority of the proposed method. Performance effects on different parameter settings, e.g., parameters of Gabor kernel and dimensionalities of multivariate statistical models, are further discussed.

Accurate temperature measurement of blast furnace molten iron is essential for regulating the furnace temperature. However, the dust interference at blast furnace cast field makes it difficult to measure molten iron temperature accurately using the infrared temperature measurement method. To reduce the influence of dust on the results of infrared measurement method, a compensation method using the heterogeneous features of infrared images is proposed in this article. First, the infrared image of the molten iron flow is divided into subregions, from which the subregions containing only molten iron are selected. Then, the statistic and the texture features influenced by dust are extracted from the selected subregions. Finally, the heterogeneous features are used as the compensation model input to estimate the measurement error of each subregion and compensate for the molten iron temperature. Experimental results demonstrate that the compensation method can significantly reduce the infrared measurement errors caused by dust and obtain accurate molten iron temperature.

Deep learning is a recently developed feature representation technique for data with complicated structures, which has great potential for soft sensing of industrial processes. However, most deep networks mainly focus on hierarchical feature learning for the raw observed input data. For soft sensor applications, it is important to reduce irrelevant information and extract quality-relevant features from the raw input data for quality prediction. To deal with this problem, a novel deep learning network is proposed for quality-relevant feature representation in this article, which is based on stacked quality-driven autoencoder (SQAE). First, a quality-driven autoencoder (QAE) is designed by exploiting the quality data to guide feature extraction with the constraint that the potential features should largely reconstruct the input layer data and the quality data at the output layer. In this way, quality-relevant features can be captured by QAE. Then, by stacking multiple QAEs to construct the deep SQAE network, SQAE can gradually reduce irrelevant features and learn hierarchical quality-relevant features. Finally, the high-level quality-relevant features can be directly applied for soft sensing of the quality variables. The effectiveness and flexibility of the proposed deep learning model are validated on an industrial debutanizer column process.

The burst bubble rate has been strongly linked to froth stability, and thus, it is always used for performance prediction or modeling in the froth flotation. Due to different bubble motions and intensity changes as the bubbles move, the current burst bubble recognition methods are ineffective. Therefore, in this article, a similarity-based method for burst bubble recognition is proposed. The proposed method uses the local motion correction to deal with the different motion cases, and it selects the chamfer distance and the weighted normalized cross correlation as the similarity to decrease the influence of the intensity changes by the convex shape. Furthermore, the weighted normalized cross correlation is flexibly integrated with the template mask matching and the partial template matching. Extensive experiments have validated the effectiveness and robustness of the proposed method, where the precision and F1-score have been increased by at least 7.41% and 4.36%, respectively, compared with the current methods.

The copper removal process (CRP) aims to reduce the copper ion concentration in zinc sulphate solution to a specific range by zinc addition. The satisfaction of production constraints and minimization of zinc consumption are vital but difficult to achieve. In this article, the dynamic optimization for CRP is conducted for optimal zinc control trajectory design considering constraints at least cost. First, a dynamic optimization problem with both state and control constraints is constructed for CRP. Then, a constrained dynamic optimization method is proposed, where a wavelet-based control parameterization method and a smooth penalty method are adopted. Specially, a hybrid optimization strategy is proposed to achieve a robust and efficient optimization performance. Numerical experiments are provided to illustrate the effectiveness of the proposed method. Results show that the proposed method can produce not only the optimal control trajectory with a qualified outlet ion concentration but also the less zinc consumption.

Deep learning has been recently introduced for soft sensors in industrial processes. However, most of the existing deep networks, such as stacked autoencoder, are pretrained in a layerwise unsupervised way to learn feature representations for the raw input data itself. For soft sensors, it is necessary to extract quality-relevant features for quality prediction. Thus, a deep layerwise supervised pretraining framework is proposed for quality-relevant feature extraction and soft sensor modeling in this article, which is based on stacked supervised encoder-decoder (SSED). In SSED, hierarchical quality-relevant features are successively learned by a number of supervised encoder-decoder (SED) models. For each SED, the features from the previous hidden layer are served as new inputs to generate the high-level features that are learned with the constraint of predicting the quality data as good as possible at the output layer of this SED. With this new structure, the SED can learn quality-relevant features that can largely improve the prediction performance. By stacking multiple SEDs, hierarchical quality-relevant features can be progressively learned, and irrelevant information is gradually reduced by deep SSED network. The effectiveness of the proposed model is demonstrated on a numerical example and an industrial process of the debutanizer column.

In this article, we investigate the asymptotical feedback set stabilization in distribution of probabilistic Boolean control networks (PBCNs). We prove that a PBCN is asymptotically feedback stabilizable to a given subset if and only if (iff) it constitutes asymptotically feedback stabilizable to the largest control-invariant subset (LCIS) contained in this subset. We proposed an algorithm to calculate the LCIS contained in any given subset with the necessary and sufficient condition for asymptotical set stabilizability in terms of obtaining the reachability matrix. In addition, we propose a method to design stabilizing feedback based on a state-space partition. Finally, the results were applied to solve asymptotical feedback output tracking and asymptotical feedback synchronization of PBCNs. Examples were detailed to demonstrate the feasibility of the proposed method and results.

Satisfying the process technical requirements while optimizing the control of oxygen and zinc oxide is the main task for optimal operation of the iron removal process. However, due to the complicated mechanism and varied production conditions, the process is difficult to achieve optimal operation with the simple controller. The manual control, as a result, is extensively used in practice. In this paper, we develop an optimal setting and control (OSC) system for the iron removal process to achieve its technical requirements with minimal process consumptions. The OSC system is composed of a neural network soft-sensor, an optimal setting module, an optimal controller, and a fuzzy-logic-based compensator. First, we define the oxygen reaction efficiency (ORE) to measure the difference between the theoretical oxygen amount and its actual amount. Due to the ORE cannot be measured online and it varies with the production conditions, an adaptive weight radial basis function neural network soft-sensor is designed to estimate it. The adaptive weight adjusting method contributes to improve the adaptability of the soft-sensor, and its convergence is discussed. The optimal setting module provides the set-point of outlet ferrous ion concentration for the optimal operation of every reactor. The steady-state optimal control of oxygen and zinc oxide is then established, and the compensator compensates the control inputs utilizing the feedforward and feedback information. Finally, simulations validate that the ORE is important for the optimal control of the process. Furthermore, industrial experiments are presented to verify the effectiveness and potential of the proposed strategy.

In the manufacturing process of ternary cathode material, batching, mixing, loading and sintering procedures play decisive roles on product quality, and data generated by these procedures are collected by the manufacturing process of ternary cathode material. However, data of the process are heterogeneous and have different properties, and sampling of key quality variables is difficult. On this basis, on-line evaluation on product quality is hard. In this paper, a data fusion based online product quality evaluation method for the ternary cathode material is proposed. Data is collected from an enterprise manufactures ternary cathode material. Then, to establish the model between the extracted data features and the key quality variables such as particle size and surface-free lithium content, a semi-supervised double-weighted probabilistic principal component regression is proposed. After that, a distance cost index is brought to cluster and grade the two quality variables, which are predicted by the semi-supervised model. Therefore, the on-line evaluation of product performance is achieved by setting rule table in accordance with production experience. Finally, based on data collected from an enterprise manufactures ternary cathode material, the proposed method is verified to be effective and accurate.

This paper studies a containment problem with communication delays and switching topologies. A nonlinear projection containment control algorithm for followers with single-integrator discrete-time dynamics is proposed. The main approach is to use the convexity of the convex hull spanned by multiple stationary leaders to show the nonincreasing monotonicity of the largest distance from the agents to the convex hull. It is shown that the nonlinear projection containment control algorithm is robust to arbitrarily bounded communication delays as long as each follower jointly has a path from some leaders to itself. Finally, a numerical example is implemented to show the obtained theoretical results.

The state transition algorithm (STA) has been emerging as a novel metaheuristic method for global optimization over the past few years. In our previous study, the parameter of transformation operator in continuous STA is kept constant or decreasing itself in a periodical way. In this paper, the optimal parameter selection of operators in continuous STA is taken into consideration. First, a statistical study with four benchmark 2-D functions is conducted to show how these parameters affect the search ability of the STA. Based on the experience gained from the statistical study, then, a new continuous STA with optimal parameter strategy is proposed to accelerate its search process. The proposed STA is successfully applied to 12 benchmarks with 20-D, 30-D, and 50-D space. A comparison with other metaheuristics has also demonstrated the effectiveness of the proposed method.

In this paper, a new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem. This paper focuses on the distributed plant-wide processes. The core of the proposed method is to reduce uncertainties using correlation information from the neighboring nodes. Furthermore, the cost of the data transmission between network nodes is also reduced by the D-CCA algorithm. When the proposed method and the existing methods are compared using the Tennessee Eastman benchmark process, the false alarm rate, fault detection rate, and the detection delay are comparable. This suggests that the proposed method is feasible.

A novel asynchronous control for a class of Markovian jump systems (MJSs) via output feedback sliding mode approach with an artificial time-delay is proposed. The asynchronous control strategy is adopted owing to the nonsynchronization between the controlled system and the controller. In some practical applications, the state variables are often difficult to be measured directly from the outside of the system, which makes the implementation of state feedback technique more complex. However, the output information is always accessible to the system. Therefore, an asynchronous output feedback sliding controller, where an artificial time-delay is introduced in the synthesis of the sliding surface, for MJSs is designed to guarantee the sliding mode dynamics satisfying the reaching condition, and a sufficient condition is derived to ensure the resultant system exponentially stable. Besides, a program of optimization is given to optimize the artificial delay-time. Finally, a numerical simulation and a practical application are given to validate the effectiveness of the proposed technique.

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users' interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%-65.6%, and the contents can be disseminated faster by about 24.6%-57.8%.

In the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process. This paper proposes a coordinated optimization strategy for setting the descent gradient of OFIC. First, an optimal setting module is established to determine the initial set-points of the descent gradient. The oxygen utilization ratio (OUR), an important parameter in this module, cannot be measured online. Therefore, a self-adjusting RBF (SARBF) neural network with an adaptive learning rate is developed to estimate the OUR. The convergence of the SARBF neural network is discussed. Then, a coordinated optimization strategy is proposed to adjust the set-points of the descent gradient when the measured OFICs drift away from their desired set-pints. If the final OFIC does not satisfy the process requirements, a compensation mechanism is developed to provide a compensation for the set-points of the descent gradient. Finally, industrial experiments in the largest zinc hydrometallurgy plant validate the effectiveness of the proposed coordinated optimization strategy. Our strategy improves the qualified ratio of the OFIC and the quality of the goethite precipitate. More profit is created to the iron removal process after our strategy is applied.

In copper removal process control, the commonly used technique is the so-called rule-based control, which is largely dependent upon the operators' experience, likely leading to unstable process production due to each individual's characters and favors. In this paper, to enhance the effectiveness of process control, a controllable-domain-based fuzzy rule extraction strategy is proposed. New definitions of representative controlled samples are introduced, by which the input variable space is divided into several controllable domains by applying positive and unlabeled learning algorithm. Also, the unreasonable removed and the controllable domains are accordingly determined. Then, support vector machine method is employed to extract fuzzy control rules for different domains. Finally, an industrial experiment is presented to demonstrate the effectiveness and advantages of the developed new design scheme.

As part of the zinc hydrometallurgy plant, the iron removal process is a complex system with four cascaded reactors. Tighter process-index control is difficult to achieve due to the complicated, long, and time-varying removal process. The control performance is also affected by the quality of the ore source and external disturbances. Little research is documented in the literature to address these difficulties and manual control is widely used. An innovative hybrid control strategy is developed to control the iron removal process' indices within narrow ranges with minimum cost of additive regents, including oxygen and zinc oxide. This strategy is composed of an optimal setting model, a model-based optimal controller, an integrated prediction model, a fuzzy-logic-based feedforward compensator, and a model feedback adjustor. The optimal setting model automatically optimizes the set-points of the process indices under different production conditions. To achieve the process requirements with minimal cost, the model-based optimal controller is designed. The integrated prediction model is established to provide a more accurate on-line prediction of the process indices by integrating the mechanism prediction model and an error compensation model based on the least-square support vector machine. Based on the predicted process indices, the compensator is developed for the optimal controller. The adjustor provides a parameter adjustment mechanism. Four-week-long industrial experiments in the largest zinc hydrometallurgy plant in China show that the control strategy can not only improve the process-indexes control performance, but also save 6.55% oxygen and 4.61% zinc oxide consumptions, which translates to 222 858 m3oxygen and 1236 t zinc oxide per year (a saving of about $570 000). The hybrid control strategy can be extended to cover other similar processes in the zinc hydrometallurgy and other industries.

In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.

This paper investigates the exponential stability analysis issue for a class of delayed recurrent neural networks (RNNs) with semi-Markovian parameters. By constructing a stochastic Lyapunov functional and using some zoom techniques to estimate its weak infinitesimal operator, the exponential mean square stability criteria have been proposed for the Markovian neural networks with certain transition probabilities. We then generalize the homogeneous polynomial approach for the delayed Markovian RNNs with uncertain transition probabilities during the stability analysis. Theoretical results have obtained by introducing an appropriate technique for dealing with a large number of complex homogeneous polynomial matrix inequalities. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed technique.

To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase of manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines (MARS) and smooth thin plate spline regression (TPSR). Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the comparison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for interactive natural image segmentation.

This paper studies the design of optimal location of buffer area in a movement-assisted data gathering scheme. In [7], the author only considered a network scenario where rLtR and the transmission power of sensor was fixed. This paper extends the network to a general network where the relationship between r and R is arbitrary and the transmission power of sensor is variable. In the network, the maximum transmission radius is k times of the minimum transmission radius. In order to minimize the total energy consumption of network and maximize the network lifetime, this paper employs a more accurate analytical method to select parameters of the transmission power level and location of buffer area. Finally, a conclusion is obtained that the scheme using optimized location of buffer area and transmission power excels greatly the one that employs fixed transmission power in the scenario of improving the network lifetime. Therefore, our study on the parameter selection of network can present a better guidance on the optimization of wireless sensor networks.

For the load-dependent nonlinear properties of the nitrogen oxide (NOx) decomposition process in thermal power plants, a local-linearization modeling approach based on a kind of global Nonlinear AutoRegressive Moving Average with eXogeneous input (NARMAX) model, named the exponential ARMAX (ExpARMAX) model, is presented. The ExpARMAX model has exponential-type coefficients that depend on the load of power plants and are estimated offline. In order to take advantage of existing conventional controllers and to reduce the cost of the industrial identification experiment, we propose a model structure that makes it possible for the ExpARMAX model to be identified using commercial operation data. On the basis of the model, a long-range predictive control strategy, without resorting to parameter estimation online, is investigated. The influence of some intermediate variables treated as process disturbances is studied, and the scheme using a set of multi-step-ahead predictors of the intermediate variables to improve control performance is also presented. A simulation study shows that the ExpARMAX model can give satisfactory modeling accuracy for the NOx decomposition (de-NOx) process in a large operating range, and the control algorithm proposed significantly improves the control performance

In this paper, the problem of the stability and stabilization analysis for linear neutral time-delay systems is investigated. The time-delays considered here are assumed bounded but no information to be available. Using Lyapunov functional method, both delay-dependent and delay-independent conditions for the stability and stabilization of the systems are presented in terms of linear matrix inequality (LMI). Examples are given to illustrate the main result of this paper and compare with the results presented in literatures.

The problems of robust stability and robust H/sub /spl infin// control for a class of uncertain neutral systems with time-varying delay are investigated. The class describes linear state models with norm-bounded uncertain system parameters and time-varying delay. First, a sufficient condition for robust stability independent of time-varying delay is developed. Then, a sufficient condition for designing a memoryless state-feedback controller which stabilizes the uncertain neutral system under consideration and guarantees an H/sub /spl infin//-norm bound constraint on the disturbance attenuation for all admissible uncertainties is derived. In both problems, the results are expressed in the form of LMI.

Depending on varying prices of electricity, an optimal power-dispatching system (OPDS) is developed to minimize the cost of power consumption in the electrochemical process of zinc (EPZ). Due to the complexity of the EPZ, the main factors influencing the power consumption are determined by qualitative analysis, and a series of conditional experiments is conducted to acquire sufficient data, then two backpropagation neural networks are used to describe these relationships quantitatively. An equivalent Hopfield neural network is constructed to solve the optimization problem where a penalty function is introduced into the network energy function so as to meet the equality constraints, and inequality constraints are removed by alteration of the Sigmoid function. This OPDS was put into service in a smeltery in 1998. The cost of power consumption has decreased significantly, the total electrical energy consumption is reduced, and it is also beneficial to balancing the load of the power grid. The actual results show the effectiveness of the OPDS. This paper introduces a successful industrial application and mainly presents how to utilize neural networks to solve particular problems for the real world.