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Joint Probabilistic Scheduling and Resource Allocation for Wireless networked control systems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 258-260页

作者： Meng Zheng Lei Zhang Wei Liang IEEE the State Key Laboratory of Robotics and the Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences the University of Chinese Academy of Sciences

Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.

关键词： networked control systems

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Soft Resource Slicing for Industrial Mixed Traffic in 5G Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 463-465页

作者： Jingfang Ding Meng Zheng Haibin Yu the State Key Laboratory of Robotics and the Key Laboratory of Networked Control Systems Chinese Academy of Sciences the Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences the University of Chinese Academy of Sciences IEEE

Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.

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An intelligent matching method for the equivalent circuit of electrochemical impedance spectroscopy based on Random Forest

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材料科学技术（英文版） 2025年第6期209卷 300-310页

作者： Wenbo Chen Bingjun Yan Aidong Xu Xin Mu Xiufang Zhou Maowei Jiang Changgang Wang Rui Li Jie Huang Junhua Dong Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110169China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110169China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110169China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110169China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China General Research Institute China Oil & Gas Pipeline Network CorporationLangfang 065000China

One of the core works of analyzing Electrochemical Impedance Spectroscopy(EIS)data is to select an appropriate equivalent circuit model to quantify the parameters of the electrochemical reaction ***,this process often relies on human experience and judgment,which will introduce subjectivity and *** this paper,an intelligent approach is proposed for matching EIS data to their equivalent circuits based on the Random Forest *** can automatically select the most suitable equivalent circuit model based on the characteristics and patterns of EIS *** the typical scenario of metal corrosion,an atmospheric corrosion EIS dataset of low-carbon steel is constructed in this paper,which includes five different corrosion *** dataset was used to validate and evaluate the pro-posed method in this *** contributions of this paper can be summarized in three aspects:(1)This paper proposes a method for selecting equivalent circuit models for EIS data based on the Random Forest algorithm.(2)Using authentic EIS data collected from metal atmospheric corrosion,the paper es-tablishes a dataset encompassing five categories of metal corrosion scenarios.(3)The superiority of the proposed method is validated through the utilization of the established authentic EIS *** ex-periment results demonstrate that,in terms of equivalent circuit matching,this method surpasses other machine learning algorithms in both precision and ***,it shows strong applicability in the analysis of EIS data.

关键词： Electrochemical impedance spectroscopy Random forest Corrosion Equivalent circuit model

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MATD3-Based End-Edge Collaborative Resource Optimization for NOMA-Assisted Industrial Wireless Networks

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Computers, Materials & Continua 2025年第2期82卷 3203-3222页

作者： Ru Hao Chi Xu Jing Liu College of Information Engineering Shenyang University of Chemical TechnologyShenyang110142China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China

Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely MATD3-CO, for iterative optimization. Specifically, we first decouple SOM into two sub-problems, i.e., joint sub-channel allocation and task offloading sub-problem, and computation resource allocation sub-problem. Then, we propose MATD3 to optimize the sub-channel allocation and task offloading ratio, and employ the convex optimization to allocate the computation resource with a closed-form expression derived by the Karush-Kuhn-Tucker (KKT) conditions. The solution is obtained by iteratively solving these two sub-problems. The experimental results indicate that the MATD3-CO scheme, when compared to the benchmark schemes, significantly decreases system overhead with respect to both delay and energy consumption.

关键词： Industrial wireless networks(IWNs) multi-access edge computing(MEC) non-orthogonal multiple access(NOMA) task offloading resource allocation

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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Dynamic Blockchain-Empowered Trustworthy End-Edge Collaborative Computing via Rotating Multi-Agent DRL

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4864-4878页

作者： Xu, Chi Zhang, Peifeng Yu, Haibin Li, Yonghui Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes. © 2002-2012 IEEE.

关键词： Markov processes

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A surface defect detection method for steel pipe based on improved YOLO

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Mathematical Biosciences and Engineering 2024年第2期21卷 3016-3036页

作者： Wang, Lili Song, Chunhe Wan, Guangxi Cui, Shijie State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China College of Information Liaoning University Shenyang110036 China

Surface defect detection is of great significance as a tool to ensure the quality of steel pipes. The surface defects of steel pipes are charactered by insufficient texture, high similarity between different types of defects, large size differences, and high proportions of small targets, posing great challenges to defect detection algorithms. To overcome the above issues, we propose a novel steel pipe surface defect detection method based on the YOLO framework. First, for the problem of a low detection rate caused by insufficient texture and high similarity among different types of defects of steel pipes, a new backbone block is proposed. By increasing high-order spatial interaction and enhancing the capture of internal correlations of data features, different feature information for similar defects is extracted, thereby alleviating the false detection rate. Second, to enhance the detection performance for small defects, a new neck block is proposed. By fusing multiple features, the accuracy of steel pipe defect detection is improved. Third, for the problem of a low detection rate causing large size differences in steel pipe surface defects, a novel regression loss function that considers the aspect ratio and scale is proposed, and the focal loss is introduced to further solve the sample imbalance problem in steel pipe defect datasets. The experimental results show that the proposed method can effectively improve the accuracy of steel pipe surface defect detection. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Aspect ratio

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Unsupervised Anomaly Detection Approach Based on Adversarial Memory Autoencoders for Multivariate Time Series

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Computers, Materials & Continua 2023年第7期76卷 329-346页

作者： Tianzi Zhao Liang Jin Xiaofeng Zhou Shuai Li Shurui Liu Jiang Zhu Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110000China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110000China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110000China University of Chinese Academy of Sciences Beijing100000China

The widespread usage of Cyber Physical systems(CPSs)generates a vast volume of time series data,and precisely determining anomalies in the data is critical for practical *** is the mainstream method for time series anomaly detection,and the anomaly is judged by reconstruction ***,due to the strong generalization ability of neural networks,some abnormal samples close to normal samples may be judged as normal,which fails to detect the *** addition,the dataset rarely provides sufficient anomaly *** research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series to solve the above ***,an encoder encodes the input data into low-dimensional space to acquire a feature ***,a memory module is used to learn the feature vector’s prototype patterns and update the feature *** updating process allows partial forgetting of information to prevent model *** that,two decoders reconstruct the input ***,this research uses the Peak Over Threshold(POT)method to calculate the threshold to determine anomalous samples from normal *** research uses a two-stage adversarial training strategy during model training to enlarge the gap between the reconstruction error of normal and abnormal *** proposed method achieves significant anomaly detection results on synthetic and real datasets from power systems,water treatment plants,and computer *** F1 score reached an average of 0.9196 on the five datasets,which is 0.0769 higher than the best baseline method.

关键词： Anomaly detection autoencoder memory module adversarial training

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Design and Implementation of High Reliable Punctuality system Based on RT1064 6

Design and Implementation of High Reliable Punctuality Syste...

引用

6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Gang, Mingxu Yan, Bingjun Hu, Bo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798331541750

To ensure that all types of control equipment in the industrial process control network are stable in the same clock reference for a long time, a highly reliable punctuality system is designed and implemented;Based on IEEE1588v2 high-precision clock synchronization protocol, the system uses dual redundant clock synchronization technology to achieve high-precision clock synchronization with the master clock in the network. When it is unable to synchronize with the master clock, the system adjusts the local clock by calling the stored historical clock deviation value to improve the punctuality of the system;The test and verification platform is built to test the clock synchronization accuracy and punctuality accuracy. The test results show that the clock synchronization accuracy is within ± 500ns after the system runs stably, and the punctuality accuracy is within ± 20us/h after the system cannot be synchronized with the master clock to enter the punctuality state, which meets the design requirements. © 2024 IEEE.

关键词： Mechanical clocks

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A Novel SPICE Modeling Approach for SiC D-MOSFET Based on BSIM3HV 3

A Novel SPICE Modeling Approach for SiC D-MOSFET Based on BS...

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3rd International Symposium on Semiconductor and Electronic Technology, ISSET 2024

作者： Cheng, He Yang, Zhijia Zhang, Chao Zhang, Zhipeng Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China

ISBN: (纸本)9798350390285

SiC MOSFETs have emerged as a highly promising solution for power electronics applications due to their superior material properties, including a wide bandgap, high thermal conductivity, high critical electric field strength, and high saturation velocity of charge carriers. Accurate compact modeling of SiC D-MOSFETs is crucial when designing analog circuits for applications such as solar inverters, motor drives, high-voltage DC-DC converters, and uninterruptible power supplies (UPS). The current characteristics of SiC MOSFETs can be numerically assessed using TCAD simulation, which compares them against measurements from the device datasheet. In this paper, we present a comprehensive modeling approach for the SCT50N120 1200V SiC D-MOSFET from STMicroelectronics. The well-established industry-standard BSIM3HV SPICE model is employed, and its key parameters are optimized to achieve an accurate fit between the simulated and measured current characteristics. This optimization process yields an refined SPICE circuit simulation model for the targeted SiC D-MOSFET device, which is subsequently verified through simulations. Ultimately, we demonstrate reasonable agreement between the optimized SPICE model simulations and the measured datasheet results for the IDS- VGS and IDS-VDS characteristics across various bias conditions. © 2024 IEEE.

关键词： SPICE

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