Industrial Cyber-Physical Systems (ICPS) are increasingly vulnerable to sophisticated attacks and operational disturbances that induce subtle and hard-to-detect anomalies, particularly in industrial edge environments. Existing anomaly detection methods often rely on sufficient labeled data and involve excessive computational overhead, hindering real-time detection and lightweight deployment. To address these challenges, we propose a Multimodal Feature fusion-enhanced time series Anomaly Detection framework (MFAD) in ICPS. MFAD enhances the representation of subtle anomalies by jointly modeling temporal dynamics and industrial characteristics through a unified multimodal feature fusion mechanism. Moreover, MFAD adopts a three-stage detection strategy with adaptive thresholding, which further improves robustness under varying operating conditions, while its lightweight overall architecture supports edge deployment. In addition, we provide the Industrial Gas Cyber-Physical System (IGCPS) dataset collected from real-world industrial operations. Experiments on ICPS benchmark datasets of varying scales, including IGCPS, PUMP, WADI, and SWaT, demonstrate that MFAD achieves an F1 score exceeding 96.7% with efficient resource utilization, validating its effectiveness for real-time detection and lightweight deployment in resource-constrained industrial edge environments. Note to Practitioners—This paper is motivated by the increasing need for reliable and efficient anomaly detection in Industrial Cyber-Physical Systems (ICPS), particularly deployed in resource-constrained industrial edge environments. Existing approaches often treat temporal and industrial features separately, rely on sufficient labeled data, and require substantial computational resources, which limits their applicability in real-world industrial settings. In contrast, the proposed MFAD provides a lightweight and practical solution that integrates multimodal feature fusion with robust semi-supervised detection mechanisms to effectively capture subtle anomalies in time series industrial data. The framework is designed with deployment feasibility that it offers strong detection accuracy, low latency, and efficient resource consumption suitable for industrial edge devices. The methods presented here can inform practitioners seeking to enhance the reliability and real-time performance of ICPS anomaly detection systems. Future extensions may focus on expanding MFAD for broader online industrial applications, integrating it with more edge platforms, and enabling large-scale distributed deployment.

It is necessary to establish a spatio-temporal correlation model in the traffic data to predict the state of the transportation system. Existing research has focused on traditional graph neural networks, which use predefined graphs and have shared parameters. But intuitive predefined graphs introduce biases into prediction tasks and the fine-grained spatio-temporal information can not be obtained by the parameter sharing model. In this paper, we consider it is crucial to learn node-specific parameters and adaptive graphs with complete edge information. To show this, we design a model based on graph structure that decouples nodes and edges into two modules. Each module extracts temporal and spatial features simultaneously. The adaptive node optimization module is used to learn the specific parameter patterns of all nodes, and the adaptive edge optimization module aims to mine the interdependencies among different nodes. Then we propose a Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting (DAGCAN), which relies on the above two modules to dynamically capture the fine-grained spatio-temporal relationships in traffic data. Experimental results on four public transportation datasets, demonstrate that our model can further improve the accuracy of traffic prediction.

In light of the shortage of acupuncturists and issues such as subjectivity leading to inconsistent efficacy and variability in accuracy and stability, an eight-degree-of-freedom (DOF) dexterous acupuncture robot system is developed in this article. An effective model predictive control (MPC) scheme, integrating needle bending compensation and manipulative techniques, is proposed to ensure the safety, accuracy, and stability of acupuncture manipulative trajectory tracking control. Based on acupuncture’s working scenarios, a dexterous 2-DOF acupuncture mechanism is designed. Then, the kinematic and acupoint models are established. To enhance the controller’s robustness, trajectory tracking constraints are included as soft constraints in the objective function before needle insertion, leading to the development of a trajectory-constrained MPC (TCMPC) model. This model not only improves the tracking accuracy and convergence speed but also ensures the stability of the solution. Additionally, a needle bending correction trajectory control method, considering needle deformation, is derived to perceive and correct the needle bending deformation under complex force environments. Furthermore, an adaptive impedance control method, integrating feedforward-feedback control (FFC), is proposed to simultaneously control force and track manipulative techniques under various contact environments. The uniform ultimate boundedness of the closed-loop system is verified using the Lyapunov theory. Finally, the effectiveness of the proposed methods and prototypes is validated through numerical simulations and practical experiments.

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%.

Positioning and posture of the robotic joints and end effector could probably introduce random initial errors. Those errors could exponentially deteriorate with compounded of common noise perturbation to cause the final failure of repetitive tracking control. To better improve the tolerance of those complex errors, a novel reciprocal of the exponential varying-parameter recurrent neural network (RE-VP-RNN) is proposed in this article to consider superimposed noise interference including the initial position deviation and noise perturbation together. Theoretical analysis further proves the convergence of the proposed method. The effectiveness, accuracy, and robustness of the proposed RE-VP-RNN solver are verified by simulation and physical experiments on three representative redundant and hype-redundant manipulators. The proposed model could be widely used in robot control for high-precision machining scenarios such as medical, industry, and aviation.

A computation-intensive graph task comprises a set of tasks and corresponding data flows between adjacent tasks. In this paper, we consider a mobile edge computing (MEC) system based on vehicle-to-everything (V2X) communication in which each task initiator (TI) generates and offloads a set of correlated tasks to different task executors (TEs). We formulate the graph task assignment problem as a mixed-integer nonlinear programming problem (MINLP) to minimize the weighted sum of time-energy consumption (WETC). Due to the data-flow dependency and time-varying characteristics of the operating environment including channel gain, communication distance between TIs and TEs, available computing resources of TEs, traditional numerical optimization algorithms cannot solve such optimization problem efficiently, especially when the scale of the MEC system is quite large. To this end, we propose a graph task offloading mechanism named GT-DQN by integrating deep Q-Network (DQN) with breadth-first search technique. Firstly, DQN is trained to generate a near-optimal offloading strategy, through numerous interactions with the time-varying operating environment. Secondly, a breadth-first search algorithm is adopted to traverse the graph task, which can significantly reducing the computational complexity. Compared with existing algorithms, simulation results verify the superiority of GT-DQN.

Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as two promising paradigms for future mobile networks that can be utilized to improve the spectrum efficiency and the quality of computational experience (QoCE). In this paper, we study the task offloading problem in an MEC-enabled UDN architecture with the aim to minimize the task duration while satisfying the energy budget constraints. Due to the dynamics associated with the environment and parameter uncertainty, designing an optimal task offloading algorithm is highly challenging. Consequently, we propose an online task offloading algorithm based on a state-of-the-art deep reinforcement learning (DRL) technique: asynchronous advantage actor-critic (A3C). It is worthy of remark that the proposed method requires neither instantaneous channel state information (CSI) nor prior knowledge of the computational capabilities of the base stations. Simulations show that the our method is able to learn a good offloading policy to obtain a near-optimal task allocation while meeting energy budget constraints of mobile devices in UDN environment.

Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only models objects but also infers their relationships. Existing works usually learn coarse global context when predicting relationships leading to excessive redundant information being considered. In this article, we first classify objects into different subgroups according to the degree of correlations with several hub objects. Then, we propose a multihub driven attention network (MHDANet) based on deep learning that drives the information to pass within the subgroups and forces objects to attend more to related objects. Consequently, MHDANet learns compact relation-aware features of visual scenes and predicts accurate and diverse relationships. Experimental results show that MHDANet achieves superb performance on scene graph generation on real-world datasets and especially alleviates the imbalance of predicted relationship categories.

Digital multimeter, as a multi-purpose electronic measuring instrument, has basic fault diagnosis function and is widely used in laboratories and industries. In order to solve the problems of manual calibration wasting manpower, manual reading is error-prone, and to improve the accuracy and robustness of automatic reading recognition of digital meters. In this paper, a deep learning-based reading recognition method of the digital multimeter is proposed. Firstly, the YOLO target detection method is used to extract the digital display area of multimeter. Then, the CNN classification method is used to identify 0~9 numbers, negative signs and blanks in the reading area. Simultaneously, the connected domain method is adopted to locate the decimal point based on its location characteristics. Finally, the complete reading information can be obtained by merging the reading recognition result and the decimal point positioning result. Experiments were conducted and the results shown that the overall characters recognition accuracy reaches 99.85%. Besides, only one error occurred in the final test with an accuracy of 98%. Meanwhile, this proposed method can adapt to various complex environments, and meet the real-time requirements. Specifically, compared with the traditional method, the proposed modified deep learning method has high robustness and practicability.

Device-to-device communications underlaying cellular networks have been recognized as one of the key technologies for the fifth generation (5G) cellular system to improve the spectrum efficiency and system capacity. In this paper, we investigate the potential of deep reinforcement learning (DRL) for joint subcarrier assignment and power allocation in a general form of D2D networks, where a subcarrier can be assigned to multiple D2D pairs and each D2D pair is permitted to utilize multiple subcarriers. We first formulate the above problem as a Markov decision process, and then propose a double deep Q-network (DQN)-based subcarrier-power allocation algorithm to learn the optimal policy in the absence of full instantaneous channel state information (CSI). Specifically, each D2D pair acts as a learning agent that adjusts its own subcarrier-power allocation strategy iteratively through interactions with the operating environment in a trial-and-error fashion. Simulation results confirm that the proposed algorithm achieves near optimal performance in real time. It is worth mentioning that the proposed algorithm is especially suitable for the case where the environmental dynamics is not accurate and the CSI delay cannot be ignored.

To assess the performance of image quality metrics (IQMs), some regressions, such as logistic regression and polynomial regression, are used to correlate objective ratings with subjective scores. However, some defects in optimality are shown in these regressions. In this correspondence, monotonic regression (MR) is found to be an effective correlation method in the performance assessment of IQMs. Both theoretical analysis and experimental results have proven that MR performs better than any other regression. We believe that MR could be an effective tool for performance assessment in the IQM research.