This paper addresses the robust model predictive control problem for a class of networked control systems with polytopic uncertainties and hard constraints, where the controller design is complicated by the joint presence of an energy harvesting sensor in the forward channel and the round-robin protocol in the backward channel. In such a setting, stochastic transmission behavior caused by random energy availability, together with fixed communication scheduling and immeasurable states, makes it difficult to guarantee recursive feasibility of the online optimization and mean-square stability of the closed-loop system. To capture these features, the mathematical expectation of a quadratic function depending on both the sensor energy level and the transmission order over an infinite horizon is constructed to formulate the optimization problem. In order to cope with the terminal constraint set and the immeasurability of system states, an auxiliary optimization problem with guaranteed solvability is developed by employing inequality analysis and slack-matrix techniques, through which a sub-optimal solution is obtained. Furthermore, sufficient conditions are derived to ensure the recursive feasibility of the proposed algorithm and the mean-square stability of the resulting closed-loop system with and without hard constraints. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method.

High-fidelity 4D dynamic facial avatar reconstruction from monocular video is a critical yet challenging task, driven by increasing demands for immersive virtual human applications. While Neural Radiance Fields (NeRF) have advanced scene representation, their capacity to capture high-frequency facial details, such as dynamic wrinkles and subtle textures from information-constrained monocular streams, requires significant enhancement. To tackle this challenge, we propose a novel hybrid NeRF framework, called Geometry-Aware-Transformer-Enhanced NeRF (GAT-NeRF) for high-fidelity and controllable 4D facial avatar reconstruction, which integrates the Transformer mechanism into the NeRF pipeline. GAT-NeRF synergistically combines a coordinate-aligned Multilayer Perceptron (MLP) with a lightweight Transformer module, termed as Geometry-Aware Transformer (GAT) due to its processing of multi-modal inputs containing explicit geometric priors. The GAT module is enabled by fusing multi-modal input features, including 3D spatial coordinates, 3D Morphable Model (3DMM) expression parameters, and learnable latent codes to effectively learn and enhance feature representations pertinent to fine-grained geometry. The Transformer’s effective feature learning capabilities are leveraged to significantly augment the modeling of complex local facial patterns like dynamic wrinkles and acne scars. Comprehensive experiments unequivocally demonstrate GAT-NeRF’s state-of-the-art performance in visual fidelity and high-frequency detail recovery, forging new pathways for creating realistic dynamic digital humans for multimedia applications.

This article investigates the model predictive control (MPC) problem for discrete-time uncertain Markovian jump systems (MJSs) via an improved optimizing prediction dynamics (IOPD) approach. To address immeasurable system states, an observer-based output feedback controller is designed within the MPC framework, accompanied by a novel dual-mode control strategy that optimizes the tradeoff among initial feasibility, control performance, and computational efficiency. The first control mode, associated with the terminal constraint set, is derived from an off-line infinite-horizon optimization problem. The second mode, which steers the system state toward the terminal set within a prescribed time, is determined via online optimization, where dynamically structured perturbations expand the initial feasible region of system state, reduce computational burden, and improve closed-loop performance. The challenges posed by immeasurable states and nonlinear variable coupling are systematically resolved using matrix decomposition and parameter transformation techniques. Sufficient conditions are established to guarantee the recursive feasibility of the IOPD-MPC algorithm and the mean-square stability of the closed-loop system. Numerical simulations on a macroeconomic system validate the efficacy of the proposed method.

Some capsule networks (CapsNets) reported lately aim to enforce capsule poses and descriptors to be equivariant and invariant respectively by adding extra loss functions as regularization but without providing rigorous proof. To address this problem, a group equivariant spatial attention mechanism (GSA) is proposed to rigidly guarantee the equivariance with mathematical proof while enhancing the spatial information in capsule poses. In addition, to alleviate the computation burden associated with the conventional routing algorithm, group poolings are developed to generate the descriptors and poses of capsules, which contribute greatly to preserving the invariance and equivariance of CapsNets. With the proposed components of GSA and group poolings, a new attentive CapsNet, namely spatial attentive group equivariant CapsNets (SAGE-CapsNets), is constructed in this paper. To validate the invariance and equivariance of SAGE-CapsNets, we conduct experiments involving classification, semantic segmentation, and visualization. The results obtained from these experiments provide empirical evidence of the effectiveness of our proposed approach. Note to Practitioners—This paper is motivated by the problem that existing affine transformations in the real world generally degrade the performance of neural networks in vision tasks like image classification, segmentation, and detection. While conventional capsule networks help to alleviate this problem by learning invariant spatial relationships between features, their robustness to affine transformations is shown through empirical results without rigorous proof. To tackle this issue, we propose a novel capsule network with equivariant components, including group spatial attention and group pooling layers. These components are rigorously proven to be equivariant and greatly contribute to the model’s robustness against affine transformations. Moreover, for practical applications, our proposed attention mechanism improves model performance without significantly increasing computation. Additionally, group pooling preserves model equivariance while reducing computation overhead. As a result, our computation-saving model can be applied to real-world vision applications that require robustness to affine transformations, such as bearing fault diagnosis and facial recognition.

Within the scope of location-based services and personalized recommendations, the challenges of recommending new and unvisited points of interest (POIs) to mobile users are compounded by the sparsity of check-in data. Traditional recommendation models often overlook user and POI attributes, which exacerbates data sparsity and cold-start problems. To address this issue, a novel multiplex hypergraph attribute-based graph collaborative filtering is proposed for POI recommendation to create a robust recommendation system capable of handling sparse data and cold-start scenarios. Specifically, a multiplex network hypergraph is first constructed to capture complex relationships between users, POIs, and attributes based on the similarities of attributes, visit frequencies, and preferences. Then, an adaptive variational graph auto-encoder adversarial network is developed to accurately infer the users’/POIs’ preference embeddings from their attribute distributions, which reflect complex attribute dependencies and latent structures within the data. Moreover, a dual graph neural network variant based on both Graphsage K-nearest neighbor networks and gated recurrent units are created to effectively capture various attributes of different modalities in a neighborhood, including temporal dependencies in user preferences and spatial attributes of POIs. Finally, experiments conducted on Foursquare and Yelp datasets reveal the superiority and robustness of the developed model compared to some typical state-of-the-art approaches and adequately illustrate the effectiveness of the issues with cold-start users and POIs.

The generative adversarial network (GAN) is increasingly used to address data imbalanced. However, GAN struggle with minority data that have few instances and lack accurate statistical characteristics. To mitigate this problem, a hierarchical oversampling based on Cohen’s criterion (HiOC) is proposed for extremely imbalanced data with missing information. The main idea of HiOC is to use Cohen’s criterion to design a primary rebalancing rate by considering data size, distribution, and feature information comprehensively. Then, HiOC including two stages to achieve the data balance. In the first stage, to enhance the data variety, especially for instances on the borderline as well as make a delicate imputation for missing information, a progressive oversampling method in the framework of fuzzy information decomposition (FID) is proposed. The progressive FID (PFID) introduces a small bias to expand the prediction region’s bounds and fulfills imputation step by step using newly sampled data. In the second stage, to greatly keep the original data distribution, an information granularity (IG) incorporated fuzzy c-means clustering strategy is developed. Afterward, a GAN based on Mahalanobis distance performs oversampling in each cluster to achieve ultimate data balance with unbiased evaluation. Finally, the proposed algorithm is applied to several real datasets, demonstrating higher classification accuracy compared with existing algorithms, thus proving the applicability of the research results in real-world scenarios.

Most existing techniques to handle imbalanced data might be invalid in the presence of data missing since they are based on the assumption that the data is complete. To shorten such a gap, a novel synthetic minority oversampling technique (SMOTE), i.e., Non-negative latent factor analysis-incorporated and Switching triple-weight-SMOTE (NSS), is proposed. The main idea of NSS is 4-fold: 1) a Lagrange non-negative matrix factorization (LNMF) method is put forward to impute the missing values with a guaranteed non-negativity according to the original distribution owing to the consideration of the global feature information; 2) by mapping the complete data after imputation into an empirical feature space (EFS), a more separable dataset is achieved, which rigidly maintains the same geometrical structure as the original data while efficiently reducing the redundant features to enhance model generalization and operational efficiency; 3) after fulfilling the fuzzy$c$-means (FCM) clustering, the inter-cluster distance, the capacity of each minority cluster and its sparsity are comprehensively taken into account to develop a triple-weight assignment strategy, which contributes to allocate the number of synthetic samples to each cluster appropriately; 4) a switching oversampling strategy is provided in response to the clusters with different distributions (i.e., either Gaussian or uniform distribution). Moreover, a posterior is used to check the correctness of the synthetic samples. Finally, experiments on a real dataset and 12 public datasets show that the proposed NSS outperforms other 11 state-of-art methods. Note to Practitioners—Data classification is an important computer task, which has been successfully used in many domains, including but not limited to, the medical domain, finance domain, and manufacturing domain. However, there are two big challenges when a classifier is to handle real-world data in the presence of imbalanced data and missing values. More specifically, the model performance is very likely to be degraded due to the missing information and the inclination of classifiers to the majority class. To surmount this problem, a natural idea is to use the LNMF model to obtain the desired recovery. Then, for the complete data after imputation, the empirical-feature-space-based switching triple-weight-SMOTE is applied to synthesize safe and correct data (i.e., lie solidly on the region of minority class) to achieve the balance. Such a working principle generates a novel NSS strategy. The proposed NSS strategy has the following obvious merits: 1) the imputation guarantees the similarity to the original dataset; and 2) new synthetic data are safely generated by taking adequate consideration of the information and distribution of datasets. Thus, the proposed NSS can greatly help improve the classification accuracy of real-world datasets.

Unsupervised anomaly detection methods based on knowledge distillation have exhibited promising results. However, there is still room for improvement in the differential characterization of anomalous samples. In this paper, a novel anomaly detection and localization model based on reverse knowledge distillation is proposed, where an enhanced multi-scale feature mutual mapping feature fusion module is proposed to greatly extract discrepant features at different scales. This module helps enhance the difference in anomaly region representation in the teacher-student structure by inhomogeneously fusing features at different levels. Then, the coordinate attention mechanism is introduced in the reverse distillation structure to pay special attention to dominant issues, facilitating nice direction guidance and position encoding. Furthermore, an innovative single-category embedding memory bank, inspired by human memory mechanisms, is developed to normalize single-category embedding to encourage high-quality model reconstruction. Finally, in several categories of the well-known MVTec dataset, our model achieves better results than state-of-the-art models in terms of AUROC and PRO, with an overall average of 98.1%, 98.3%, and 95.0% for detection AUROC scores, localization AUROC scores, and localization PRO scores, respectively, across 15 categories. Extensive experiments are conducted on the ablation study to validate the contribution of each component of the model.

Symmetric, high-dimensional and sparse (SHiDS) networks usually contain rich knowledge regarding various patterns. To adequately extract useful information from SHiDS networks, a novel biased triple factorization-based (TF) symmetric and non-negative latent factor (SNLF) model is put forward by utilizing the transfer learning (TL) method, namely biased TL-incorporated TF-SNLF (BT$^{2}$-SNLF) model. The proposed BT$^{2}$-SNLF model mainly includes the following four ideas: 1) the implicit knowledge of the auxiliary matrix in the ternary rating domain is transferred to the target matrix in the numerical rating domain, facilitating the feature extraction; 2) two linear bias vectors are considered into the objective function to discover the knowledge describing the individual entity-oriented effect; 3) an improved momentum-incorporated additive gradient descent algorithm is developed to speed up the model convergence as well as guarantee the non-negativity of target SHiDS networks; and 4) a rigorous proof is provided to show that, under the assumption that the objective function is$L$-smooth and$\mu$-convex, when$t\geq t_{0}$, the algorithm begins to descend and it can find an$\epsilon$-solution within$O(ln((1+\frac{\mu L}{L(1+\mu )+8\mu })/\epsilon ))$. Experimental results on six datasets from real applications demonstrate the effectiveness of our proposed T$^{2}$-SNLF and BT$^{2}$-SNLF models.

In this article, an improved double factorization-based symmetric and non-negative latent factor (Im-DF-SNLF) model is proposed to make the estimation for missing data in symmetric, high-dimensional, and sparse (SHiDS) matrices. The main idea of the Im-DF-SNLF model is fourfold: 1) considering the data variety in the practical engineering, non-negative latent factors (NLFs) in different cases are considered to better reflect the latent relationships between entries; 2) the$l_{2}$-norm regularization and the Lagrangian multiplier technique are simultaneously adopted to handle the overfitting and satisfy the non-negative constraint for latent factors (LFs); 3) the extragradient-based alternating direction (EGAD) method is utilized to accelerate the model training and rigidly guarantee the non-negativity of LFS; and 4) a rigorous proof is provided to show that, under the given assumption that the objective function is smooth and has a Lipschitz continuous gradient, the designed algorithm can find an$\epsilon $-optimal solution within$O(1/\epsilon )$, and the upper bound of the learning rate is given by 1/2. Finally, experimental results on public datasets are given to demonstrate the effectiveness of our proposed Im-DF-SNLF model with EGAD.

It is always a challenging task to model the trajectory and make an efficient damage estimation of debris clouds produced by hypervelocity impact (HVI) on thin-plates due to the difficulty in obtaining high-quality fragment images from experiments. To improve the damage estimation accuracy of HVIs on a typical double-plate Whipple shield configuration, we investigate the distributive characteristic of debris clouds in successive shadowgraphs using image processing techniques and traditional numerical methods. The aim is to extract the target movement parameters of a debris cloud from the acquired shadowgraphs using image processing techniques and construct a trajectory model to estimate the damage with desirable performance. In HVI experiments, eight successive frames of fragment shadowgraphs are derived from a hypervelocity sequence laser shadowgraph imager, and four representative frames are selected to facilitate the subsequent feature analysis. Then, using image processing techniques, such as denoising and segmentation techniques, special fragment features are extracted from successive images. Based on the extracted information, image matching of debris is conducted and the trajectory of debris clouds is modeled according to the matched debris. A comparison of the results obtained using our method and traditional numerical methods shows that the method of obtaining hypervelocity impact experimental data through image processing will provide critical information for improving numerical simulations. Finally, an improved estimation of damage to the rear wall is presented based on the constructed model. The proposed model is validated by comparing the estimated damage to the actual damage to the rear wall.

This article is concerned with the efficient model-predictive control (EMPC) problem for interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy systems with hard constraints. By applying the round-robin (RR) protocol, all controller nodes are activated in a pregiven order such that the occurrence of network congestion or data collisions can be effectively reduced. The aim of the proposed problem is to design a fuzzy controller in the framework of EMPC so as to obtain a good balance among the computation burden, the initial feasible region, and the control performance. With respect to the RR protocol and IT2 T-S fuzzy nonlinearities, a unified representation is modeled for the underlying system, and then, an augmentation state comprising of the system state, the token-dependent perturbation, and the previous input under the RR protocol is put forward; correspondingly, objective functions are constructed for the controller design. Subsequently, by using the min–max strategy, some token-dependent optimizations are established to facilitate the formation of the EMPC algorithm, where the feedback gain is designed offline, and the perturbation is calculated by solving an online optimization dependent of the token. Moreover, with the help of the matrix partition technique, the feasibility of the proposed EMPC algorithm and the stability of the underlying IT2 T-S fuzzy system are rigidly guaranteed. Finally, two numerical examples are utilized to illustrate the validity of the proposed EMPC strategy.

Fuzzy$c$-means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs. To this aim, a novel fuzzy clustering framework is put forward to perform highly accurate clustering on incomplete data. It adopts twofold ideas: 1) Utilizing a nonnegative latent factor model to prefill the missing data in the inputs by rigidly extracting involved entities’ latent features, where the principle of a minibatch gradient descent algorithm is incorporated into a single latent factor-dependent, nonnegative and multiplicative update algorithm to accelerate the convergence rate; and 2) integrating the distribution of inputs and the weights of local features into the objective function through sparse self-representation and weighting allocation to focus on crucial features. In this way, a NLF analysis-incorporated and feature-weighted fuzzy double$c$-means clustering (NF$^2$D) method is achieved, where the data distribution and instance correlation are simultaneously considered with care. Experiments on 12 real-world datasets including both data and images with different missing rates show that the proposed NF$^2$D method has a significant superiority over state-of-the-art fuzzy clustering methods.

Routing algorithm in most existing Capsule Networks (CapsNets) is always a challenging problem due to its heavy computational burden. To address this issue, we propose a “fast routing” algorithm, where the high-level capsules are activated by the statistical characteristic of votes from low-level capsule vectors. In this way, the votes and their distribution are both considered, and iterations in the traditional “dynamic routing” algorithm are eliminated. The comparison experiment on MNIST dataset reveals that CapsNet with fast routing promotes time efficiency of CapsNet with dynamic routing (DR) by 71.2%, as well as improves classification accuracy by 6%. Moreover, improved dense blocks (IDB) are developed to make a powerful feature extraction, where layer position is utilized to calculate its filters to encourage feature reuse, while reducing redundant features. Finally, the proposed CapsNet with “fast routing” and IDB, namely fast routing CapsNet (FR-CapsNet), outperforms state-of-the-art capsule models in multiple benchmark datasets.

Undirected, high-dimension and sparse (SHiDS) networks which are often encountered in industrial applications usually contain numerous useful information; thus, it is practically important to develop effective collaborative filtering methods to rigidly extract the latent information. In order to improve the performance of nonnegative latent factor (NLF) models based on the double factorization while reducing the computation burden of NLF models in terms of the triple factorization (TF) technique, a novel symmetric NLF (SNLF) model, i.e., TF-like SNLF model, is proposed in this article. A latent factor (LF) matrix that reflects the relationship of items is introduced into the objective function so as to offer more freedom for solutions. Then, a fixed weighting matrix designed by sensitivity experiments is used to replace the aforementioned LF matrix dependent on the update, which greatly helps save the time cost without sacrificing any accuracy. Furthermore, the alteration direction (ADI) method is adopted to design the algorithm, which effectively helps reduce the occurrence of the local minimum while guaranteeing convergence. Finally, experimental results on four real industrial data sets demonstrate that the proposed TF-like SNLF model makes a good compromise between the model performance and the time efficiency.

This article is concerned with the security-based resilient robust model predictive control (RMPC) problem for a class of discrete-time polytopic uncertain systems with deception attacks and the round-robin (RR) protocol. The well-known RR protocol is adopted with the hope to reduce the communication burden, where the control signal is transmitted only at the given transmission instant. By means of the concept of bounded energy and the independent Bernoulli-distributed (BD) white sequences, a representative attack model is given. A definition of mean-square (MS) security inH2-sense is employed to reasonably explain the dynamics process of the controlled systems. We aim at designing a set of resilient RMPC controllers so as to make the closed-loop system state under consideration of deception attacks and the RR protocol enter and stay in a certain bounded region. Furthermore, with the aid of stochastic analysis methods and inequality analysis techniques, sufficient conditions are obtained to satisfy the desirable security requirements. To tackle the nonconvex obstacles resulting from attacks and the RR protocol, the cone complementary linearization (CCL) method is exploited to cast them into a convex optimization problem (OP) for its solvability. Then, an online OP regarding a certain upper bound of the concerned objective is provided for the solvability and resilient RMPC-based controller gains are obtained. Finally, two examples, including a high-purity distillation one and a numerical one, are used to demonstrate the effectiveness and the validity of the proposed techniques.

As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost.

In this article, the efficient model-predictive control (EMPC) problem of a class of nonlinear systems in the framework of interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy is investigated. In order to improve the reliability of the data transmission while reducing the network communication burden, a so-called stochastic communication protocol (SCP) governed by a Markov chain is adopted to orchestrate the data transmission order from the controller to the actuator. The purpose of the addressed problem is to design a set of desired EMPC controllers so as to guarantee the mean-square system stability and obtain a good balance among the computation burden, initial feasible region, and the control performance. A novel control model is established for the SCP and IT2 T-S fuzzy nonlinearities in a unified representation by using a fuzzy periodic switching related to the transmission token and the membership function. Then, the system state, the SCP-based control perturbation, and the previous input under the SCP are fully taken into consideration for constructing the objective function. By virtue of the “min-max” strategy, a few optimizations are formulated, and the corresponding EMPC algorithm is provided, where the feedback gain is designed offline, while the control perturbation is obtained online. Furthermore, by means of the matrix partition technique, sufficient conditions are presented to rigidly guarantee the feasibility of the proposed EMPC algorithm and the mean-square stability of the underlying IT2 T-S fuzzy system. Finally, two illustrative examples are utilized to demonstrate the validity of the proposed EMPC strategy.

Feature tracking plays a vital role in a monocular visual-inertial system (VINS) or a visual task based on feature points. However, in terms of feature points tracking, most of the existing VINS solutions adopt the classical method where the feature extraction and matching are carried out independently. Due to such nonintegrated working manner, the matching performs traversal operation globally rather than in a reasonable search space, which increases the probability of false matches, and reduces the accuracy. In this article, a novel feature points tracking algorithm in terms of inertial measurement unit (IMU)-aided information fusion is presented, which can reduce the search space to improve accuracy, and boost efficiency. This method starts with a preintegration-based predictor which can predict the position of the feature points in the current frame according to the feature points that need to be matched in the previous frame, and the measurements of IMU between two frames. Then, a variable-sized search window, in which the feature extraction and matching are locally carried out, is built at the predicted location. Furthermore, to solve the convergence and overlap problem of feature points in the tracking process, a feature update module of the bionic population is attached to the local matcher. Finally, the comparison experiments are performed on the public datasets to show the effectiveness and superiority of our method. It should be emphasized that the proposed algorithm is a universal framework of solution, which can meet various task requirements by choosing different feature points extraction algorithms, and improve the efficiency of matching in an integrated way.

In this paper, robust model predictive control (RMPC) problem is investigated for a class of Markovian jump systems with unstable modes under polytopic uncertainties and hard constraints. The transition probability matrix and a mode-dependent control strategy in the framework of RMPC are co-designed. Moreover, in order to design a switching rule for the mean-square stability of the jump system, an off-line design scheme is first proposed to guarantee the on-line mode-dependent model predictive controller design. For the “on-line” part, a set of mode-dependent state feedback controllers is designed to minimize an upper bound of the worst-case infinite horizon cost function in terms of the solutions to a series of linear matrix inequalities. Finally, an simulation example regarding the economic system is implemented to verify the effectiveness of the proposed design scheme.

Summary Feature selection is a useful method for fulfilling the data classification since the inherent heterogeneity of data and the redundancy of features are often encountered in the current data exploding era. Some commonly used feature selection algorithms, which include but are not limited to Pearson, maximal information coefficient, and ReliefF, are well‐posed under the assumption that instances are distributed homogenously in datasets. However, such an assumption might be not true in the practice. As such, in the presence of data imbalance, these traditional feature selection algorithms might be invalid due to their prejudices to the minority class, which includes few samples. The purpose of the addressed problem in this article is to develop an effective feature selection algorithm for imbalanced judicial datasets, which is capable of extracting essential features while deleting negligible ones according to the practical feature requirements. To achieve this goal, the number and the distribution of samples in each class are fully taken into consideration for the correlation analysis. Compared with the traditional feature selection algorithms, the proposed improved ReliefF algorithm is equipped with: (i) different weights of features according to the characteristics of heterogeneous samples in different classes; (ii) justice for imbalanced datasets; and (iii) threshold constraints resulting from the practical feature requirements. Finally, experiments on a judicial dataset and six public datasets well illustrate the effectiveness and the superiority of the proposed feature selection algorithm in improving the classification accuracy for imbalanced datasets.

Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns. Matrix factorization (MF) technique is an efficient method to extract the useful latent factors (LFs) from the LF model, which directly gives rise to the so-called MF model. However, most MF models cannot maintain some frequently encountered constraints such as nonnegativity of LFs and the symmetry of the target network. In addition, in spite of its potential capability of obtaining the effectiveness of both the computation and the storage, the currently developed double factorization (DF)-based model still suffers from the problem of the low prediction accuracy due to the limited amount of LFs. To address the above problems, a novel MF model is proposed in terms of the triple-factorization (TF) technique, thereby leading to TF-based symmetric and nonnegative latent factor (SNLF) models. Compared with the traditional DF-based SNLF model, the proposed TF-based SNLF model is equipped with: 1) constraints on symmetry and nonnegativity; 2) desirable performance with high accuracy; 3) the convergence of the algorithm; and 4) fairly low storage and computational complexity. Furthermore, in order to reduce overfitting so as to further improve the model performance, regularization is precisely considered into the proposed TF-based SNLF model. Experiments on real datasets show that the proposed TF-based SNLF model has a whelming ability of improving the estimation accuracy for the missing data as well as guaranteeing the symmetry of the target network and the nonnegativity of LFs at a little expense of the computation and storage burden. Moreover, it is easy to be implemented for the data analysis.

This article presents a resilient robust model predictive control (RMPC) strategy for cyber-physical systems (CPSs) with polytopic uncertainties and state saturation nonlinearities under the try-once-discard (TOD) scheduling. To reduce the transmission burden, a so-called TOD protocol is adopted to schedule the nodes. The objective of the addressed problem is to design a set of resilient RMPC controllers such that, in the simultaneous presence of polytopic uncertainties and state saturations under the TOD protocol, the resilience, robustness, and exponential stability are guaranteed for the underlying CPSs. With the help of TOD-dependent Lyapunov-like function, and the average dwell-time (ADT) approach, sufficient conditions are obtained to guarantee the recursive feasibility of the proposed resilient RMPC approach and the exponential stability of the closed-loop system. Finally, two examples including a high-purity distillation process and a numerical simulation are used to demonstrate the effectiveness of the proposed methods.

In this article, the asynchronous resilient robust model predictive control (RMPC) problem is investigated for Markovian jump systems (MJSs) subject to polytopic parameter uncertainties and hard constraints on states and inputs. A hidden Markov model with partially available mode detection probabilities is introduced to characterize the asynchronous phenomenon between the detected modes and the system modes. A novel resilient control law is formulated such that, in the framework of the RMPC approach, the MJSs are mean-square stable in spite of partly accessible mode detection probabilities. Subsequently, by resorting to the stochastic analysis technique, sufficient conditions are derived, where the requirement of the terminal invariant set is met and the upper bound of the worst-case infinite horizon cost function is obtained. Moreover, by means of solving certain auxiliary optimization problems, the explicit expression of the desired controller is parameterized. Finally, simulation examples are presented to verify the validity of the proposed methods.

This paper is concerned with the N-step model predictive control (MPC) problem for a class of constrained systems with persistent bounded disturbances under the stochastic communication protocol (SCP). The control signals are transmitted to the plant via a shared network subject to a prescribed SCP for the purpose of avoiding data collisions. The SCP scheduling, which is governed by a Markov chain, is applied to orchestrate the transmission order of the controller nodes. Under the SCP, only one control node is allowed to update the control signal sent to the plant at each communication instant. Our aim is to design a set of desired controllers in the framework of N-step MPC such that the mean-square input-to-state stability of the closed-loop system is guaranteed. An optimization algorithm consisting of both off-line and online parts is developed to cope with the design problem of the N-step controller. Finally, a numerical example is utilized to illustrate the validity of the proposed N-step MPC strategy.

Non-negative latent factor (NLF) models have great capabilities of extracting useful knowledge from a symmetric, sparse and high-dimension (SHDiS) matrix with positive constraints. For the purpose of obtaining the NLFs, the tradition SNLF model is constructed based on the doublefactorization (DoF)-based matrix factorization (MF) technique, however it suffers from the low prediction accuracy. In order to improve the performance, an improved SNLF model in terms of triple factorization MF technique is proposed. Whereas, such a better prediction accuracy might be obtained at an evident cost of the time efficiency. Aiming at finding a fine balance between the model performance and the time cost, a novel triple factorization-like SNLF (TFL-SNLF) model is developed by introducing a pre-given non-negative symmetric matrix and a non-negative and symmetric LF matrix. Based on this established model, a single latent factor-dependent nonnegative additive gradient descent (AGD) update algorithm is designed for obtaining desired LFs. Experiments on two actual industrial data sets illustrate that the novel TFL-SNLF model can not only have a better performance of the prediction accuracy on missing data than the DoF-based SNLF model, but also is superior at the efficiency over the TrF-based SNLF. Hence, the proposed TFL-SNLF model is more applicable according to the industrial engineering.

This paper is concerned with the robust H2/H∞model predictive control problem for a class of linear systems with polytopic uncertainties under weighted maximum-error-first and try-once-discard (MEF-TOD) protocol. To prevent data from collision, the weighted MEF-TOD protocol is employed during the data transmission from sensors to controller, where only one sensor is allowed to transmit the measurement at every time instant. A switched linear system is established with respect to the switching signals generated by the adopted weighted MEF-TOD protocol. By taking the influence of the exogenous disturbance and the weighted MEF-TOD protocol into consideration, an online optimization problem with nonconvex conditions is formulated. Then, to deal with the couplings of unknown variables, singular value decomposition technique is utilized to transform the nonconvex conditions into solvable ones. Subsequently, a set of dynamic output-feedback controllers is designed to guarantee the stability of the closed-loop system with guaranteed robust H2/H∞performance. Finally, a direct current motor example is used to illustrate the validity and effectiveness of the proposed methods.