Nonstationarity in industrial processes, guided by factors, such as equipment aging and changing upstream load demands, inherently exhibits heterogeneous characteristics. This complex overlay of homogeneous stationarity poses great difficulty in process monitoring and analysis. Therefore, this study presents a new model (Hs- ${\mathrm {H}}_{\mathrm {n}}$ ) that peels the homogeneous and heterogeneous nonstationarity, which has four components: a differentiated learning network (DL-Net), a peeling network (Pe-Net), an adaptive reweighting network (AR-Net), and a global decoder network. DL-Net obtains the differentiated representation by leveraging a new differentiated learning approach to unique inputs, which is based on the cognitive understanding and derivation of functional specialization and content learning during network training. The aim is to maximize functional diversity and minimize content overlap. Furthermore, Pe-Net extracts the stationarity and nonstationarity (S-N) components from each differentiated scale, formulated as an encoder-decoder-encoder architecture with an integrated identity subtraction skip connection. A min-max S-N constraint regulates the peeling process and controls the extracted content. AR-Net additionally refines homogeneous stationarity across each scale and reweights the individual components to adaptively adjust their contributions. Last, reweighted components are fused and input into the global decoder to facilitate unsupervised learning. Experimental results on three processes demonstrate the effectiveness of Hs-Hn.

Reversible data hiding (RDH) for JPEG images, particularly those focusing on DCT coefficient modification, has garnered significant attention in recent years. Existing methods primarily select coefficients valued$\pm 1$for expansion embedding to avoid significant file size increases caused by modifying zero-valued DCT coefficients. However, zero-valued coefficients, which constitute the majority of DCT coefficients, are more suitable for data embedding to reduce the shift distortion. To efficiently utilize zero-valued coefficients for high-capacity embedding while controlling the file size increment, this paper introduces a novel JPEG RDH method based on ternary matrix embedding, where ternary syndrome trellis codes (STC) is employed on selected zero-valued coefficients to minimize the expansion embedding distortion, and other non-zero-valued coefficients are shifted for reversibility. Furthermore, a novel DCT coefficients measurement strategy is proposed for coefficient selection to further reduce the shift distortion. Extensive experimental validations demonstrate the superiority of the proposed method in various evaluation criteria. Notably, the proposed method achieves more than twice the embedding capacity of some state-of-the-art methods at the same PSNR while maintaining file size increment within acceptable bounds.

Zoning search strategies have been utilized to solve multimodal multi-objective optimization problems (MMOPs). However, effectively transferring knowledge among subspaces and mitigating the negative transfer remain significant challenges. To this end, we propose an adaptive transfer learning-assisted zoning search (ZSATL) method to assist other multimodal multi-objective evolutionary algorithms (MMOEAs) in obtaining more equivalent Pareto optimal solutions and a high-quality Pareto front approximation. In the ZSATL, the zoning search is employed to segment the search space into many subspaces. Moreover, an adaptive transfer learning method is proposed to alleviate the negative transfer issue. If the distribution of solution sets in two subspaces is similar, the transfer learning method is employed to exchange knowledge. Otherwise, the brain storm optimization algorithm is employed to find promising regions for mining useful knowledge. The performance of the proposed algorithm is compared with that of seven advanced MMOEAs on balanced and imbalanced MMOPs. Based on the experimental results, the ZSATL can locate more equivalent Pareto optimal solutions in the decision space and find a better PF approximation when compared with other competitors.

Advances in image generation enable hyper-realistic synthetic faces but also pose risks, thus making synthetic face detection crucial. Previous research focuses on the general differences between generated images and real images, often overlooking the discrepancies among various generative techniques. In this paper, we explore the intrinsic relationship between synthetic images and their corresponding generation technologies. We find that specific images exhibit significant reconstruction discrepancies across different generative methods and that matching generation techniques provide more accurate reconstructions. Based on this insight, we propose a Multi-Reconstruction-based detector. By reversing and reconstructing images using multiple generative models, we analyze the reconstruction differences among real, GAN-generated, and DM-generated images to facilitate effective differentiation. Additionally, we introduce the Asian Synthetic Face Dataset (ASFD), containing synthetic Asian faces generated with various GANs and DMs. This dataset complements existing synthetic face datasets. Experimental results demonstrate that our detector achieves exceptional performance, with strong generalization and robustness.

With the continuous expansion of the city scale and urbanization, urban road networks are becoming increasingly complex. Moreover, severe and extreme weather events, earthquakes, and other natural disasters occur frequently. Therefore, how to effectively and quickly evacuate urban crowd in dynamic environments is an urgent issue. To carry out the above objective, an unmanned system-guided crowd evacuation method is proposed in the current study. In the proposed method, the robot can perceive the environment in a timely and accurate manner to generate the evacuation map via advanced information technologies such as the Internet of Things or urban brain. Subsequently, an improved elliptic tangent graph approach based on global and local information (ETG-GLI) is utilized to plan a feasible and short evacuation path in large-scale scenarios. Finally, a novel crowd evacuation model based on the social force model is proposed to simulate the actual crowd evacuation process in complex and large-scale environments. To test the performance of the proposed path planning method, 25 different scenarios are proposed to simulate complex urban crowd evacuation environments. The experimental results show that the proposed algorithm outperforms other competitors in terms of path planning ability and computational time. Three actual evacuation cases with 324 pedestrians are modeled to further test the performance of the proposed algorithm. The simulation results demonstrate that the unmanned system-guided crowd evacuation method can find a shorter evacuation path for reducing the evacuation time in three complex and large-scale environments when compared with three other methods. Therefore, the proposed algorithm is a highly effective and promising approach to provide useful decision support and guidance for actual urban planning and urban emergence management.Note to Practitioners—In modern cities, the population density is high and the road network is complex. To evacuate the crowd in a timely and safe manner, planning feasible and short paths in large-scale and complex environments is a critical and challenging task. Therefore, the present study aims to provide a novel method to plan high-quality evacuation routes to guide the pedestrian flow. The performance of the proposed approach is validated in 25 test scenarios and 3 real-world instances. Experimental results demonstrate that the proposed algorithm performs well in terms of path length and computation time. Moreover, the proposed crowd evacuation model can simulate the actual process of crowd evacuation.

Multirobot task allocation (MRTA) is a challenging bi-level problem in the multirobot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are 1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments and 2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multiobjective evolutionary algorithm (MMOEA) based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved MMOEA. The traveling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multiobjective bi-level optimization problem while saving computational costs.

Accurate causal discovery is of great significance for data-driven root cause diagnosis. For multivariate complex industrial processes, traditional methods rarely conduct causal discovery of multiple causes and single effect from the perspective of quantifying causal strength. In this regard, this article proposed an anomaly tracing method based on attention-based postnonlinear (PNL) causal model. The attention mechanism is introduced into the multivariate PNL model to quantitatively calculate the causal contribution of each cause to the effect. To address the issue of distinguishing inherent causal relationships from anomaly propagation paths, a comparative causal diagram analysis method is proposed. It analyzes the changes in attention weights of the cause variables and effect variable under normal and abnormal conditions to determine the anomaly propagation paths. To tackle the problem of multiple root nodes in causal diagram, a root cause scoring method is proposed. The feasibility of the proposed method is demonstrated through simulation and real industrial case study. Compared with existing and ablation methods, the proposed method can provide the root cause more promptly and accurately, as well as identify anomaly propagation path that align with mechanism analysis.

Fault diagnosis is crucial in ensuring the safety and efficient operation of industry processes. However, achieving excellent diagnosis performance in traditional fault diagnosis tasks becomes challenging when the training process lacks test fault information. To address this challenge, a novel model for zero-shot industrial process fault diagnosis based on deep semantic canonical correlation embedding (DSCCE) is proposed in this study. The DSCCE model utilizes an embedding reconstruction model to establish the relationship between fault and their semantic attributes, which is accomplished the task through the transfer of fault semantic attribute descriptions. Initially, DSCCE encodes fault samples and semantic attribute descriptions into an embedding space through two embedding networks. Then, the model employs canonical correlation analysis with two embedding features to assess the consistency between semantic attribute vectors and fault samples. Simultaneously, correlation constraints are applied between fault embedding feature subsets to enhance differential information. Subsequently, the classifier is trained using embedding features to complete the task of zero-shot fault diagnosis. Finally, the effectiveness and superiority of the proposed DSCCE model are verified using two case studies.

Deep learning for medical image classification needs large amounts of carefully labeled data with the aid of domain experts. However, data labeling is vulnerable to noises, which may degrade the accuracy of classifiers. Given the cost of medical data collection and annotation, it is highly desirable for methods that can effectively utilize noisy labeled data. In addition, efficiency and universality are essential for noisy label training, which requires further research.To address the lack of high-quality labeled medical data and meet algorithm efficiency requirements for clinical application, we propose a simple yet effective approach for multi-field medical images to utilize noisy data, named Pseudo-T correction. Specifically, we design a noisy label filter to divide the training data into clean and noisy samples. Then, we estimate a transition matrix that corrects model predictions based on the partitions of clean and noisy data samples. However, if the model overfits noisy data, noisy samples become more difficult to detect in the filtering step, resulting in inaccurate transition matrix estimation. Therefore, we employ gradient disparity as an effective criterion to decide whether or not to refine the transition matrix in the model's further training steps. The novel design enables us to build more accurate machine-learning models by leveraging noisy labels. We demonstrate that our method outperforms the state-of-the-art methods on three public medical datasets and achieves superior computational efficiency over the alternatives.

Process monitoring and fault root cause analysis of industrial processes play a critical role to inform timely maintenance and ensure safe production. Existing distributed monitoring frameworks are able to determine the fault occurrence units, but often fall short on fault causality analysis for deeper insights. Therefore, it is necessary to conduct further analysis on variable correlations to locate the fault variable. Conventional fault root cause analysis methods, such as Granger causality and transfer entropy, ignore the distinction between indirect and direct causations between variables, resulting in unsatisfactory results of fault root cause analysis. Therefore, this article proposes a distributed process monitoring and fault root cause analysis framework via direct causality analysis based on partial cross mapping (PCM). First, fault units are located through distributed process monitoring. Then, PCM is used to locate the root cause variables hierarchically. This framework makes full use of the fault unit information obtained by a distributed monitoring method so that the number of variables for causality analysis is reduced, which increases computational efficiency as well as accuracy of the PCM method. The validity of the proposed framework is verified on the Tennessee–Eastman process and a wastewater treatment process.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling$T^{2}$statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Plant-wide multiunit processes generally contain numerous variables, complex relations, and strong nonlinearity, making the monitoring of such processes challenging. This work proposes a new Gaussian-process-based probabilistic latent variable (GPPLV) modeling framework for distributed monitoring of multiunit nonlinear processes. A Gaussian-process latent variable model is first established to extract the dominant features of a local unit. Using the extracted features, a correlation between the local unit and its neighboring units are then modeled through a Gaussian-process regression (GPR) model. The genetic algorithm is used to determine the ideal independent variables from the neighboring units and optimize the hyperparameters of the GPR model simultaneously. Residuals are generated and monitoring statistics are constructed using an established GPPLV model. Experimental studies on three processes: 1) a numerical example; 2) the Tennessee Eastman benchmark process; and 3) a laboratory distillation process show that compared to some common distributed process monitoring models, the proposed method performs better in showing the nature of different faults and shows higher fault detection rate for large-scale multiunit processes.

This study develops a novel data-driven latent variable correlation analysis (LVCA) framework to achieve communication efficient distributed monitoring for industrial plant-wide processes. Process data of a local unit are first projected into a dominant latent variable subspace and a residual subspace to characterize the correlation within the local unit. Then, least absolute shrinkage and selection operator is used to determine communication variables from neighboring units that are beneficial for monitoring the local unit. Thereafter, canonical correlation analysis is performed between the dominant subspace and communication variables to characterize the correlation between units. Finally, a distributed monitor is established for each unit, which considers the correlation within the local unit and the correlation between different operation units. The proposed LVCA-based distributed monitoring scheme is applied on a numerical example, the Tennessee Eastman benchmark process, and a lab-scale distillation process. Comparison results with some state-of-the-art methods verify the effectiveness.Note to Practitioners—In the monitoring of a local operation unit, it is important to characterize the relationship among variables within the local unit and the relationship between the local unit and its neighboring units. However, not all variables from neighboring units are beneficial for the monitoring. Including nonbeneficial variables may cause considerable communication cost and model interpretation difficulty. Here a novel latent variable correlation analysis (LVCA)-based distributed local monitoring method, which considers simultaneous correlation within the local unit and between units, is proposed. The LVCA-based distributed monitoring preserves the fault detection ability and is more computationally efficient than the existing stochastic optimization-based methods, and therefore is more suitable for practical application. The superiority and characteristics are theoretically discussed and experimentally studied. MATLAB code is available upon request.

Synthetic minority oversampling technique (SMOTE) has been widely used in dealing with the imbalance classification problem in the machine learning field. However, classical SMOTE implements the oversampling by linear interpolation between adjacent minority class samples, which may fail to consider the uneven distribution of the samples. This article proposes a minority clustering SMOTE (MC-SMOTE) method that involves the clustering of minority class samples to improve the imbalance classification performance. First, samples from the minority class are clustered into several clusters. Second, oversampling is performed by linear interpolation between adjacent clusters to create new samples from different clusters that contain additional information of the entire minority class. Then classical classification techniques can be employed to achieve efficient classification. The superiority of the MC-SMOTE is first verified by experiments on some benchmark datasets from various application domains. The proposed method is then applied to the real industrial SCADA data of wind turbine blade icing. Classification results indicate that the MC-SMOTE exhibits a better performance than that of the classical SMOTE.

Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for nonlinear plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.

Nonlinear process fault detection remains a challenge, with representation learning being a key step. In this article, a deep neural network (DNN)-based discriminative representation learning approach is proposed to achieve efficient fault detection for nonlinear plant-wide processes. An early-stage fault rarely affects several independent variables concurrently; hence, mutual information-based block division and randomized fault construction are performed to generate faulty validation data. By using the training data from the normal operation training data and the constructed validation data, a DNN with stacked autoencoders and a softmax classifier is trained to generate discriminative representations that maximize the capability of discriminating normal and abnormal statuses. Finally, on the basis of the learned deep discriminative representations, support vector data description is employed to discriminate the normal and abnormal process statuses. The proposed monitoring approach is tested on a numerical example and an industrial tail-gas treatment process, through which the efficiency is verified.

Dynamics and nonlinearity may exist in the time and batch directions for batch processes, thereby complicating the monitoring of these processes. In this article, we propose a two-dimensional deep correlated representation learning (2D-DCRL) method to achieve the efficient fault detection and isolation of the nonlinear batch processes. Three-way historical data are first unfolded as two-way time-slice data. Second, a stacked autoencoder based deep neural network is constructed to characterize the correlation among the process variables. Considering that the time and batch directions may be dynamic, for each time-slice measurement, a constructed 2-D measurement containing samples from the previous time instants and batches is then obtained. Subsequently, DCRL is performed between the current running-batch measurements and the constructed 2-D measurements to characterize the 2-D dynamics and nonlinearity. The 2D-DCRL-based monitoring examines the status of a sample by considering the 2-D nonlinear and dynamic information, providing improved monitoring performance. Applications on two typical batch processes demonstrate the effectiveness of the proposed 2D-DCRL monitoring scheme.

Industrial processes generally have various operation modes, and fault detection for such processes is important. This paper proposes a method that integrates a variational Bayesian Gaussian mixture model with canonical correlation analysis (VBGMM-CCA) for efficient multimode process monitoring. The proposed VBGMM-CCA method maximizes the advantage of VBGMM in automatic mode identification and the superiority of CCA in local fault detection. First, VBGMM is applied to unlabeled historical process data to determine the number of operation modes and cluster the data in each mode. Second, local CCA models that explore input and output relationships are established. Fault detection residuals are generated in each local CCA model, and monitoring statistics are derived. Finally, a Bayesian inference probability index that integrates monitoring results from all local models is developed to increase the monitoring robustness. The effectiveness of the proposed monitoring scheme is verified through experimental studies on a numerical example and the multiphase batch-fed penicillin fermentation process.

Deep neural network (DNN) extracts hierarchical representations from process data and is promising for nonlinear process monitoring. Obtaining meaningful representations and generating efficient fault detection residual are the main challenges in DNN-based monitoring. This study proposes a regularized deep correlated representation (RDCR) method that incorporates deep belief networks (DBNs) and canonical correlation analysis (CCA) for nonlinear process monitoring. Hierarchical representations are initially extracted using DBN to process input and output variables. Second, hierarchical representations from process input and output are modeled through CCA to characterize the relationship between them. Efficient fault detection residuals are then generated, and monitoring statistics are established. CCA-based monitoring relies on the most correlated representations; thus, a multiobjective evolutionary optimization-based regularization is performed to select the most correlated representations and eliminate the influence of unrelated representations. The advantages of the RDCR monitoring are verified through experimental studies on a numerical example and the Tennessee Eastman process.