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Practical Applicability of PLA for Industrial Wireless Networks: Gaps and Directions

IEEE Network 2025年

作者： Liang, Wei Wei, Fuyou Wang, Qi Zhan, Ming Wang, Ke Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shandong Normal University Jinan250014 China Taizhou University School of Electronics and Information Engineering Zhejiang Taizhou China

The rapid transformation of Industrial Cyber-Physical system (ICPS) has introduced significant cyber security challenges, particularly in the context of ICPS. Wireless networks are a critical component of these systems, but their open nature makes them vulnerable to security threats. Physical Layer Authentication (PLA) algorithms based on Channel State Information (CSI) are essential complements to existing security technologies in wireless networks. However, their practical application faces challenges such as restricted sampling rates and uneven sampling interval, which result in low-precision CSI, significant random phase offsets, and signal distortion. This study aims to explore the feasibility of deploying and applying existing representative algorithms on industrial wireless commercial devices. According to characteristics of authentication algorithms and the adopted authentication features, we selected five of the latest physical layer authentication algorithms including supervised, semi-supervised, and unsupervised algorithms and deployed them on commercial devices that comply with the WIA-FA standard. We conducted systematic experiments focusing on the motion parameters of devices, the CSI data sampling frequency, the interference caused by worker movement, the surface materials of the devices, and the types of attacks. This study explores the ability of these algorithms to resist attacks and environmental interference in commercial devices and industrial scenarios. Extensive experimental data were collected, and the results indicate that existing authentication algorithms still need improvement to meet the requirements of industrial applications. The experiment findings and the summarized application challenges of these algorithms provide significant guidance for future research and practical industrial implementations. © 1986-2012 IEEE.

关键词： Authentication

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Nonlinear steam generator feedwater system identification based on wavelet neural network

Nonlinear steam generator feedwater system identification ba...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Hongrui Wang Mingzhe Yuan Pengbin Zhang Kunpeng Xing Guangzhou Institute of Industrial Intelligence Guangzhou People's Republic of China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang People's Republic of China China Nuclear Power Engineering Co. Ltd Shenzhen People's Republic of China

ISBN: (数字)9781837241910

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability.

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Long Cheng Haoyang He Yan Gu Qingzhi Liu Zhiming Zhao Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Information Technology Group Wageningen University and Research Netherlands Research Group of Multi-Scale Networked Systems University of Amsterdam Netherlands

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of $53.18 \%$ and costs by $61.98 \%$ compared to basic techniques, and achieving $20.26 \%$ and $25.71 \%$ improvements over the latest advanced methods, respectively.

关键词： Training Cloud computing Privacy Costs Scalability Deep reinforcement learning Dynamic scheduling Real-time systems Complexity theory Protection

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A Fast Conversion Method of Tube Coordinates

A Fast Conversion Method of Tube Coordinates

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作者： Hao Zhao Renbo Xia Yueling Chen Liming Tao Daixian Zhao Tianyu Zhang Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institute of Robotics and Intelligent Manufacturing Innovation Chinese Academy of Sciences University of Chinese Academy of Sciences

Inspired by the idea of the coordinate transformation of the robot arm, the tube structure is analogized to the robot arm structure, and the conversion method of the tube bend processing parameters LRA to the three-dimensional coordinate points is obtained. The method first regards the tube as a "mechanical arm";then, using the similarity of the LRA processing parameters and DH parameters, all the LRA parameters are converted into D-H parameters;finally, the D-H parameter method is applied to calculate the distance between each axis of the " mechanical arm ". If the first space coordinate point is defined as the origin of the base coordinate system (0, 0, 0), then the position of the origin of other coordinate systems in the base coordinate system can be obtained;It has been proved by experiments that this method fast and feasible.

关键词： Tube Coordinates Fast Conversion Method Mechanical Arm D-H Parameter Method

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Research on NLOS environmental ranging method based on gradient boosting tree

Research on NLOS environmental ranging method based on gradi...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Wenyu Li Shun Cao Xianda Liu Minghao Wang Shuan Sheng School of Information Engineering Shenyang University of Chemical Technology Shenyang Liaoning China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Liaoning China Shenyang National Research Institute of Intelligent Robots Co. Ltd. Liaoning China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Indoor distance measurement plays a critical role in positioning services. The complexity of indoor environments, coupled with a limited and small sample size, results in suboptimal distance accuracy. This paper proposes a method for non-line-of-sight distance measurement based on a gradient boosting tree model, effectively addressing the issue of sample size dependency. The random hyperparameter settings of the gradient boosting tree model lead to prolonged fitting times. Additionally, the model utilizes a fixed learning rate, which limits the analysis of features in small samples. To tackle these concerns, we develop a grid optimization algorithm that identifies the best hyperparameter combinations through a hyperparameter grid search. We also implement a weighted value approach and a cosine annealing algorithm to precisely update the learning rate according to training conditions, thus maximizing the learning of small sample features. Our proposed model significantly enhances distance accuracy in non-line-of-sight environments under constraints of training sample size and variety. Comparative experiments demonstrate that our model improves distance accuracy by 63% relative to other machine learning models, with a mean square error of only 0.168m.

关键词： Training Analytical models Accuracy Stability criteria Fitting Power system stability Boosting Distance measurement Data models Optimization

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Sen Tao Kai Wang Peng Zeng Tianda Yu Hao Wu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang China Nuclear Power Institute of China Chengdu China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis.

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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A method of laser focusing control in microlaser-induced breakdown spectroscopy

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Plasma Science and Technology 2019年第3期21卷 31-39页

作者： Wei WANG Lanxiang SUN Peng ZHANG Liming ZHENG Lifeng QI Wei DONG Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences

This paper presents a method for the automatic adjustment of the laser defocusing amount in micro-laser-induced breakdown spectroscopy. A microscopic optical imaging system consisting of a CCD camera and a 20× objective lens was adopted to realize the method. The real-time auto-focusing of the system was achieved by detecting the effective pixels of the light spot generated by the laser pointer. The focusing accuracy of the method could achieve 3 μm. The element concentrations of Mn and Ni in low-alloy steels were analyzed at a crater diameter of about 35 μm using the presented method. After using the presented method, the determination coefficients of Mn and Ni both exceeded 0.997, with the root-mean-square errors being 0.0133 and 0.0395, respectively. Scanning analysis was performed on the inclined plane and the curved surface by means of focusing control and non-focusing control. Ten characteristic spectral lines of Fe were selected as the analysis lines. With the focusing control, the average relative standard deviations obtained on the inclined plane and curved surface were both less than 5%, and much less than the values without focusing control, 14.6% and 40.39%.

关键词： LIBS microanalysis ablation crater focusing control

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Hybrid Beamforming Design for Millimeter Wave Multiuser MIMO systems with Dynamic Subarrays

arXiv

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arXiv 2022年

作者： Wang, Gengshan Yang, Zhijia Gong, Tierui State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The University of Chinese Academy of Sciences Beijing100049 China Singapore University of Technology and Design Singapore

In this letter, we investigate the millimeter wave (mmWave) downlink multiuser multiple-input multiple-output (MU-MIMO) system, adopting the dynamic subarray architecture at the base station and considering the multi-stream communication for each user. Aiming at maximizing the system spectral efficiency, we propose a novel hybrid beamforming design. First, assuming no inter-user interference (IUI), we easily get the optimal fully-digital beamformers and combiners using the singular value decomposition of each user channel and the waterfilling algorithm. Then, based on the obtained fully-digital beamformers, we propose a Kuhn-Munkres algorithm-assisted dynamic hybrid beamforming design, which guarantees that each radio-frequency chain is connected to at least one antenna. Finally, we propose to further project each obtained digital beamformer onto the null space of all the other equivalent user channels to cancel the IUI. Numerical results verify the superiority of our proposed hybrid beamforming design. © 2022, CC BY.

关键词： Millimeter waves

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A New Method of Fault Detection and Diagnosis for the Tennessee Eastman Process

A New Method of Fault Detection and Diagnosis for the Tennes...

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第32届中国过程控制会议(CPCC2021)

作者： Pengbin Zhang Tao Zou Jingyang Wang Zhijia Yang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China. Institutes for Robotics and Intelligent ManufacturingChinese Academy of Sciences University of Chinese Academy of Sciences College of Mechanical and Electrical Engineering Guangzhou University State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

This paper proposes a new fault detection and diagnosis(FDD) method for the Tennessee Eastman(TE) large-scale chemical *** method is built integrated with a curve-type recognizer and a dynamic Bayesian network(DBN).The fault detection is carried out utilizing the curve-type recognizer,while the diagnosis is completed using the ***,the recognizer extracts fault symptoms from different times by distinguishes the curves' ***,the DBN is constructed to handle these time-related fault symptoms *** DBN will give the probability of each fault more and more accurately as time goes *** whole procedure consists of 1) deciding monitoring variables using chemical and control engineering knowledge 2) extracting fault symptoms,3) building a *** advantage over other data-driven FDD methods in reasonability and interpretability is also *** last,the simulation results demonstrate the availability of the proposed method.

关键词： A New Method of Fault Detection and Diagnosis for the Tennessee Eastman Process

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