Category: IEEE Transactions on Industrial Informatics

The increasing size and complexity of cyber-physical production systems (CPPS) lead to an increasing number of faults, such as broken components or interrupted connections. Nowadays, faults are handled manually, which is time-consuming because for most operators mapping from symptoms (i.e., warnings) to repair instructions is rather difficult. To enable CPPS to adapt to faults autonomously, reconfiguration, i.e., the identification of a new configuration that allows either reestablishing production or a safe shutdown, is necessary. This article addresses the reconfiguration problem of CPPS and presents a novel algorithm called AutoConf. AutoConf operates on a hybrid automaton that models the CPPS and a specification of the controller to construct a QSM. This QSM is based on propositional logic and represents the CPPS in the reconfiguration context. Evaluations on an industrial use case and simulations from process engineering illustrate the effectiveness and examine the scalability of AutoConf.

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for a multirobot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this article, a novel deep-reinforcement-learning-based approach is proposed to take both multitarget coverage and connectivity preservation into account simultaneously, which consists of four parts: a hierarchical observation attention representation, an interaction attention representation, a two-stage policy learning, and a connectivity-guaranteed policy filtering. The hierarchical observation attention representation is designed for each robot to extract the latent features of the relations from its neighboring robots and the targets. To promote the cooperation behavior among the robots, the interaction attention representation is designed for each robot to aggregate information from its neighboring robots. Moreover, to speed up the training process and improve the performance of the learned policy, the two-stage policy learning is presented using two reward functions based on algebraic connectivity and coverage rate. Furthermore, the learned policy is filtered to strictly guarantee the connectivity based on a model of connectivity maintenance. Finally, the effectiveness of the proposed method is validated by numerous simulations. Besides, our method is further deployed to an experimental platform based on quadrotor unmanned aerial vehicles and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.

Principal component analysis (PCA) is widely adopted in local tangent space alignment to estimate local tangent spaces. These estimates are only accurate when uniformly distributed data lies in or is close to linear subspaces. In practice, such conditions are rarely satisfied. Therefore, this approach fails to reveal manifold intrinsic features, resulting in degraded fault detection accuracy. Considering the drawbacks, weighted linear local tangent space alignment (WLLTSA), a manifold learning method is put forward. First, weighted PCA is adopted to provide local tangent space estimates. The parameter selection criterion for the weight matrix is established by taking the context of geometric preservation into account. Second, global low dimensional coordinates are formed by aligning local coordinates with global feature space. Finally, the fault detection model is developed, and kernel density estimation is utilized to approximate confidence bounds for $mathrm{T}^{mathrm{2}}$ and SPE statistics. Simulation results are presented to illustrate the superior feature extraction and fault detection performance of WLLTSA.

Generalizing the application of machine learning models to situations where the statistical distribution of training and test data are different has been a complex problem. Our contributions in this article are threefold: 1) we introduce an anchored-based out-of-distribution (OOD) Regression Mixup algorithm, leveraging manifold hidden state mixup and observation similarities to form a novel regularization penalty; 2) we provide a first of its kind high-resolution distributed acoustic sensor dataset that is suitable for testing OOD regression modeling, allowing other researchers to benchmark progress in this area; and 3) we demonstrate with an extensive evaluation the generalization performance of the proposed method against existing approaches and then show that our method achieves state-of-the-art performance. We also demonstrate a wider applicability of the proposed method by exhibiting improved generalization performances on other types of regression datasets, including Udacity and Rotation-MNIST datasets.

Automatic recommendation has become an increasingly relevant problem for industries, which allows users to discover items that match their tastes and enables the system to target items at the right users. Graph neural networks have attracted many researchers' attention and have become a useful tool for recommendation. However, these models face two major challenges, which are heterogeneous information aggregation and aggregation weight estimation. In this article, we propose a graph neural networks-based recommendation model, i.e., a separated graph neural recommendation (SGNR) model, which achieves high-quality performance. SGNR separates BINs in recommendation systems into two weighted homogeneous networks for users and items, respectively, resolving the heterogeneous information aggregation problem. In addition, a propagation coefficient estimation method is proposed, which combines parametric and nonparametric estimation strategies. And, it is constructed with three characteristics, which are collaborative, side-information constrained, and adaptive. Thereinto, a three-hierarchy attention operator is contained for feature fusion, which optimizes the feature aggregation process via a more sensible and flexible propagation mechanism. Experimental results on four public databases indicate that the proposed methods perform better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments and achieve readability and interpretability to some extent.

As a primary countermeasure to mitigate traffic congestion and air pollution, promoting public transit has become a global census. Designing a robust and reliable bus timetable is a pivotal step to increase ridership and reduce operating cost for transit authorities. However, most previous studies on bus timetabling rely on historical passenger count and travel time data to generate static schedules, which often yield biased results in these uncertain scenarios, such as demand surge or adverse weather. In addition, acquiring real-time passenger origin/destination from a limited number of running buses is not feasible. This article considers the multiline dynamic bus timetable optimization problem as a Markov decision process model to address the aforementioned issues, and proposes a multiagent deep reinforcement learning framework to ensure effective learning from the imperfect-information game, where the passenger demand and traffic condition are not always known in advance. Moreover, a distributed reinforcement learning algorithm is applied to overcome the limitation of high computational cost and low efficiency. A case study of multiple bus lines in Beijing, China, confirms the effectiveness and efficiency of the proposed model. The results demonstrate that our method outperforms heuristic and state-of-the-art reinforcement learning algorithms by reducing 20.30% of operating and passenger costs compared with actual timetables.

Due to the data-driven intelligence from the recent deep learning based approaches, the huge amount of data collected from various kinds of sensors from industrial devices have the potential to revolutionize the current technologies used in the industry. To improve the efficiency and quality of machines, the machine manufacturer needs to acquire the history of the machine operation process. However, due to the business secrecy, the factories are not willing to do so. One promising solution to the abovementioned difficulty is the synthetic dataset and an informatic network structure, both through deep generative models such as differentially private generative adversarial networks. Hence, this article initiates the study of the utility difference between the abovementioned two kinds. We carry out an empirical study and find that the classifier generated by private informatic network structure is more accurate than the classifier generated by private synthetic data, with approximately 0.31–7.66%.

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions.

The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML)-based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on maximal frequent itemset mining. The proposed feature selection method uses Gini impurity and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a true positive rate of 100% and a false positive rate of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.

This article studies multisensor scheduling for remote state estimation in cyber-physical systems. We consider that each sensor monitors a dynamic process and sends its data to the remote end. This article focuses on minimizing remote estimation errors over a temporally correlated communication channel. The problem is formulated as the Markov decision process (MDP) with finite-horizon cost criterion. The optimal structured policies are derived for both Markov packet dropout and finite-state Markov channel models, which can reduce computation overhead. For the infinite-horizon case, we design algorithms to address the issues of unknown channel statistics and the curse of dimensionality in the MDP, respectively. Particularly, a heuristic algorithm with linear complexity is proposed to schedule multisensor in a decentralized manner. Simulation examples are provided to verify the theoretical results.