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Decentralized fault diagnosis of discrete-event systems with unreliable sensors using linear temporal logic

Science China(information Sciences) 2025年第1期68卷 322-341页

作者： Weijie DONG Shaoyuan LI Xiang YIN Key Laboratory of System Control and Information Processing Ministry of EducationDepartment of Automation Shanghai Jiao Tong University

In this paper,we investigate a decentralized diagnosis problem of a discrete-evnt system(DES) subject to unreliable sensors,where the sensor observations of local diagnosers may be non-deterministic as a result of possible *** studies on decentralized robust diagnosis can only deal with different types of sensor failures separately,e.g.,all sensors suffer from the same type of sensor failures such as intermittent sensor failures or permanent sensor ***,since sensors of different local diagnosers may face different external environments and have different internal characteristics,sensors corresponding to different local diagnosers may also have their own fault *** this paper,we propose a flexible framework of decentralized diagnosis for DES subject to unreliable sensors such that sensors of different local diagnosers are permitted to have different types of sensor *** this end,we extend the existing decentralized diagnosis framework to the case where there exist common sensors broadcasting their observations to all local *** apply linear temporal logic(LTL) to constrain infinite behaviors of private sensors of local diagnosers and common ***,a new notion of φ-codiagnosability is proposed as the necessary and sufficient condition for the existence of a decentralized diagnoser that works correctly under sensors,satisfying LTL-based sensor ***,we provide an effective approach to verify the φ-codiagnosability.

关键词： discrete event systems decentralized diagnosis codiagnosability automata

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Value Iteration-Based Distributed Adaptive Dynamic Programming for Multi-Player Differential Game With Incomplete information

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 436-447页

作者： Yun Zhang Yuqi Wang Yunze Cai the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China the National Key Laboratory of Air-based Information Perception and Fusion

In this paper, a distributed adaptive dynamic programming(ADP) framework based on value iteration is proposed for multi-player differential games. In the game setting,players have no access to the information of others' system parameters or control laws. Each player adopts an on-policy value iteration algorithm as the basic learning framework. To deal with the incomplete information structure, players collect a period of system trajectory data to compensate for the lack of information. The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy. Theoretical analysis shows that by adopting proximal policy searching rules, the approximated policies can converge to a neighborhood of equilibrium policies. The efficacy of our method is illustrated by three examples, which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.

关键词： Distributed adaptive dynamic programming incomplete information multi-player differential game (MPDG) value iteration

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Non-uniform electron density estimation based on electromagnetic wave attenuation in plasma

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Chinese Physics B 2025年第1期34卷 381-386页

作者： Zhaoying Wang Lixin Guo Maixia Fu Shaoshuai Guo Yinsheng Li Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China School of Physics Xidian UniversityXi’an 710071China

The surface of a high-speed vehicle reentering the atmosphere is surrounded by plasma *** to the influence of the inhomogeneous flow field around the vehicle,understanding the electromagnetic properties of the plasma sheath can be *** the electron density of the plasma sheath is crucial for understanding and achieving plasma stealth of *** this work,the relationship between electromagnetic wave attenuation and electron density is deduced *** attenuation distribution along the propagation path is found to be proportional to the integral of the plasma electron *** result is used to predict the electron density ***,the average electron density is obtained using a back-propagation neural network ***,the spatial distribution of the electron density can be determined from the average electron density and the normalized derivative of attenuation with respect to the propagation *** to traditional probe measurement methods,the proposed approach not only improves efficiency but also preserves the integrity of the plasma environment.

关键词： attenuation electromagnetic propagation plasma electron density

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MULTI-SCALE RESNET INTEGRATED WITH ATTENTION MECHANISM FOR ENHANCED WHEAT FRESHNESS DIAGNOSIS BASED ON BIOPHOTONICS

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Acta Scientiarum Polonorum, Technologia Alimentaria 2024年第4期23卷 525-535页

作者： Shi, Weiya Chen, Liang Key Laboratory of Grain Information Processing and Control Ministry of Education Henan 450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan 450001 China College of Artificial Intelligence and Big Data Henan University of Technology Henan 450001 China

Background. Accurate detection of wheat freshness is important for ensuring the quality and safety of wheat products, thereby protecting the health and interests of consumers. Material and methods. This study integrates biophoton emission technology with advanced deep learning frameworks to transform the process of wheat freshness assessment. Leveraging the powerful feature extraction capabilities of the ResNet architecture, we employ a multi-scale framework integrated with the Gaussian Context Transformer (GCT) attention mechanism. The MS-GCT-ResNet method presents a groundbreaking approach that not only enhances the accuracy and efficiency of wheat freshness discrimination but also demonstrates the potential for combining biophysical phenomena with cutting-edge Artificial Intelligence (AI) technologies for precision agriculture and food quality control. Results. This model enhances detection accuracy and adaptability, offering a powerful technique for the rapid and precise evaluation of wheat freshness. Its effectiveness is validated using years of wheat sample data. Based on the experimental results, MS-GCT-ResNet achieves a recognition accuracy of 93.6%. Compared with traditional CNN and ResNet models, the recognition accuracy increases by 1.9% and 1.1%, respectively. Conclusion. MS-GCT-ResNet is a highly promising, non-invasive, and efficient technological advancement capable of quickly and accurately assessing wheat freshness. This method holds immense potential to revolutionize the agriculture and food processing industries. © (2024), (Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu). All rights reserved.

关键词： attention mechanism biophoton emission detection multi-scale wheat freshness

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An access control model for data security sharing cross-domain in consortium blockchain

IET Blockchain

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IET Blockchain 2023年第1期3卷 18-34页

作者： Liu, Yang Yang, Weidong Wang, Yanlin Liu, Yang College of Information Science and Engineering Henan University of Technology Henan Zhengzhou China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan Zhengzhou China Ministry of Education Key Laboratory of Grain Information Processing and Control Henan Zhengzhou China

With the rapid increment of the demand for data sharing among parties, data is considered a cornerstone component to provide value in the big data environment. Concerns regarding sharing data security have impeded the development of cross-domain data interaction. Therefore, an access control model for data security sharing cross-domain is proposed, Fabric-ABAC, that is based on Hyperledger Fabric and Attribute-based Access control (ABAC). In order to solve the data security challenges caused by a trusted central organization implementation, a distributed environment is constructed that consists of stakeholders among parties. The unified attribute model is designed for multi-environment combined with smart contracts. Fabric-ABAC realizes multi-level, fine-grained, and auditable access control, enabling data security through automatic permission verification. Considering the ledger is visible to all participants in consortium blockchain, it is necessary to protect the confidentiality of sensitive data. Thus, Proxy Re-Encryption (PRE), which is implemented by smart contracts, is adopted in the scheme to realize the ciphertext interaction without the third party. The security of PRE and the access control model used in Fabric-ABAC is discussed to show that a secure environment for data sharing is provided. Moreover, the completeness of the implementation and effectiveness of the system performance in the multi-domain environment is demonstrated in the experimental results. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Smart contract

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grain condition inspection: one improved grey wolf algorithm for nodes deployment optimization combining with path planning of multi-inspection robots

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Soft Computing 2024年第20期28卷 11971-11986页

作者： Zhu, Chunhua Zhang, Jilong Yang, Jing Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Henan Zhengzhou450001 China

grain inspection robots can detect abnormal information such as grain insects and impurities on the grain surface by deploying detection nodes, it has a significant for the safety of grain storage. Generally, the grey wolf optimization (GWO) algorithm has significant advantages such as fewer control parameters and easy implementation, is one of the commonly employed approaches aiming to solve the problem in many fields. However, the traditional GWO has the shortcomings such as poor population initialization diversity, slow convergence speed and easily fall into local optimal values, it is difficult to solve deployment of inspection nodes problem in the grain surface. In this study, an improved Latin Hypercube grey wolf optimization (LH-GWO) is presented to overcome the disadvantages of the traditional GWO algorithm in solving inspection node deployment problem. The LH-GWO algorithm contains two novel mechanisms which are the Latin Hypercube Sampling population initialization and an improved nonlinear convergence factor. Moreover, a grain surface boundary inspection nodes deployment strategy is proposed to optimize the location of nodes at the boundary. For verifying the effectiveness of the proposed LH-GWO, comparing with six commonly used algorithms based on benchmark functions experiment and coverage rate experiment. The experimental results demonstrate the merits of LH-GWO in terms of convergence speed, optimization accuracy and area coverage. Finally, the optimal inspection nodes are divided into three inspection regional areas by k-means algorithm clustering, and in each inspection regional area, the optimal inspection path of each robot is determined by solving the travel salesman problem (TSP). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Motion planning

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Connectivity-maintaining Consensus of Multi-agent Systems With Communication Management Based on Predictive control Strategy

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 700-710页

作者： Jie Wang Shaoyuan Li Yuanyuan Zou Key Laboratory of System Control and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China IEEE

This paper studies the connectivity-maintaining consensus of multi-agent *** the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be *** this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching *** proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving *** the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving *** results indicate the effectiveness and advantages of our scheme.

关键词： Consensus energy-saving multi-agent system predictive control

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Optimal Deceptive Strategy Synthesis for Autonomous Systems under Asymmetric information

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Lv, Peng Li, Shaoyuan Yin, Xiang Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

High-level task planning under adversarial environments is one of the central problems in the development of autonomous systems such as unmanned ground vehicles (UGV). Existing works commonly assume that the decision-maker such as UAV shares the same information with the environment. However, in many scenarios, the UGV, as an integral part of the system, generally has more information than the external adversary. For such a scenario, the decision-maker with more information may achieve better performance by using deceptive strategies. In this paper, we investigate the problem of optimal deceptive strategy synthesis for autonomous systems under asymmetric information between the internal decision-maker and the external adversary. Specifically, we model the dynamic system as a weighted two-player graph game and the objective is to optimize the mean payoff value per task. To capture the asymmetric information between two parties, we assume that the UGV has complete knowledge of the system, whereas the adversary may have misconceptions regarding the task as well as the cost. To synthesize an optimal deceptive strategy, we propose a synthesis algorithm based on hyper-games. The correctness as well as the complexity of the algorithm are analyzed. We illustrate the proposed algorithm by running examples as well as a simulation case study. Finally, we conduct an empirical experiment using real-world scenarios to verify the practical applicability of our algorithm. IEEE

关键词： Intelligent vehicle highway systems

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Strip width spread prediction in rough rolling process based on mechanism modeling and optimization

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Journal of Iron and Steel Research International 2023年第12期30卷 2416-2424页

作者： Yan-jiu Zhong Jing-cheng Wang Jia-hui Xu Jun Rao Department of Automation Key Laboratory of System Control and Information ProcessingShanghai Jiao Tong UniversityShanghai 200240China

Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm(BADE)was *** first,we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling ***,the BADE algorithm was proposed to optimize the improved spread mechanism *** optimization algorithm is based on a novel adaptive differential evolution algorithm,which can effectively achieve the global optimal ***,the prediction performances of five machine learning algorithms were compared in *** results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms,which proves the effectiveness of the proposed method.

关键词： Hot rolling Width spread prediction Bayesian optimization:Adaptive differential evolution Parameter optimization

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Prediction Method of Axial Compression Capacity of CCFST Columns Based on Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第3期51卷 243-251页

作者： Cheng, Yong Tao, Huawei Lu, Xiangying Xu, Zhijun Fu, Hongliang Zhu, Chunhua Key Laboratory of Food Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China Key Laboratory of Food Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China School of Civil Engineering Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China Henan Engineering Laboratory of Grain IOT Technology Henan University of Technology Zhengzhou450001 China

Accurate prediction of axial compression capacity (ACC) is crucial for evaluating the performance of circular concrete-filled steel tubes (CCFST). Traditional methods face challenges in dealing with the intricate nonlinear relationship inherent in the structure. This paper presents a deep learning (DL) method to predict ACC. The proposed method standardizes parameters, including steel yield strength, column length, and other relevant characteristics of CCFST. Subsequently, a three-layer Long Short-Term Memory (LSTM) network is constructed to capture the complex relationships among these parameters. To enhance the model’s capability for nonlinear fitting, the Rectified Linear Unit (ReLU) activation function is applied between network layers. Additionally, an attention mechanism is introduced to improve feature extraction capabilities. Comprehensive simulations demonstrate that the proposed method surpasses state-of-the-art techniques, achieving optimal values for Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2). The simulation results validate the efficacy of the proposed method. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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