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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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Distributed Secure Balancing Control for Battery Energy Storage Systems Under Privacy-Preserving Mechanisms

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IEEE Transactions on automation Science and Engineering 2025年 22卷 15768-15777页

作者： Zhuoyang Zhao Engang Tian Licheng Wang School of Optical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Automation Engineering Shanghai University of Electric Power Shanghai China

This paper deals with the privacy-preserving-based distributed secure balancing control problem for battery energy storage systems (BESSs) in a microgrid. A novel distributed consensus algorithm is proposed based on the state decomposition strategy and the homomorphic cryptography technique, under which the state-of-charge (SOC) balancing issue is addressed amid potential privacy leak threats. By decomposing the state of the modified SOC for BESSs based on the number of neighboring BESSs, the privacy of each BESS’s state is safeguarded, provided that the BESS has at least one neutral neighboring BESS. Each homologous substate exchanges information directly, and non-homologous substates exchange information using a homomorphic cryptography algorithm. A weighted approach is used to ensure that the sum of the homologous substates aligns with the group decision value, thereby maintaining consistency among these homologous substates. The effectiveness of the proposed method is validated through a series of illustrative simulation experiments. Note to Practitioners—The integration of BESSs into microgrid poses challenges in maintaining SOC consistency, attributed to inherent parameter differences among BESS units. To meet these challenges, a distributed control algorithm is proposed, aiming to stabilize charging and discharging states across these systems. The use of public internet infrastructure for BESS communication raises privacy concerns, as it is vulnerable to security breaches. To mitigate the risk of eavesdropping and enhance data security, robust privacy protection mechanisms are integrated into the proposed methodology, ensuring the secure integration of BESS into microgrid systems. The results of this study provide a simple but useful algorithms achieve SOC consistency between BESSs, while performing privacy preservation.

关键词： Microgrids State of charge Privacy Decentralized control Renewable energy sources Protection Partial discharges Multi-agent systems Homomorphic encryption Communication channels

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Concept-Aware Entity Alignment Network for Industrial Knowledge Graph

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4316-4323页

作者： Wu, Shuai Tong, Wei Hou, Yuhong Li, Ping Yang, Weidong Wu, Edmond Q. Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China School of Mechanical Engineering Northwest University of Technology Xi'an710072 China Chinese Flight Test Establishment Technology and Engineering Center of Chinese Flight Test Establishment Xi'an710076 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China Fudan University Shanghai200433 China

The industrial knowledge graph (IKG) can improve the cognitive intelligence of the manufacturing system and is recognized as one of the cores of the next-generation industrial management information system. Due to the multisource heterogeneous nature of industrial data, aligning entities with the same semantics (entity alignment) is the core technology for building large-scale, high-coverage IKGs. Existing approaches show that embedded learning of IKGs performs well for this task. However, most advanced methods ignore concept information when learning topological information about IKGs. Inspired by the ontology matching theory, in this article, we realize the importance of entity concepts in alignment. The conceptual semantics of entities can usually be obtained through the is-a relation. However, the IKG is usually constructed by triples (entity, relation, entity) automatically extracted from a large text corpus. This will lead to entities in the IKG having problems such as lacking conceptual information, belonging to multiple concepts, or having different concept granularities. To solve the two problems of lacking conceptual information and different concept granularity, we propose the concept-aware entity alignment network (CAEA), aggregating bidirectional relations and attributes to get the entity concept semantics by a novel concept-aware graph attention mechanism. The excellent performance of the CAEA can better support the construction of large and complete IKGs and support downstream applications such as industrial knowledge recommendation and assisted decision-making. To verify the performance of the CAEA on the IKG, we construct a new entity alignment benchmark using industrial control network security data and verify the effectiveness of the CAEA on the new benchmark and several mainstream datasets. Experimental results show that our method outperforms other state-of-the-art (SOTA) methods and promotes the development of IKGs. © 2025 IEEE. All rights rese

关键词： Knowledge graph

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Data-Driven Group Formation Control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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BACFuse: Towards Noise-Resistant BAC Detection Based on Multi-Modal Fusion on Smartphone

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IEEE Internet of Things Journal 2025年第11期12卷 16410-16423页

作者： Wu, Yuan Zhao, Gaorong Yang, Zheng Feng, Yong Wang, Yongmei Ding, Lei Chen, Jia Zhang, Jian Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China Wuhan University School of Computer Science Wuhan230036 China Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users' active participation, making it impractical to monitor Blood Alcohol Content (BAC) during driving. This paper introduces BACFuse, a device-free, contactless, and non-invasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multi-modal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse's performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with a MAE of 2.13mg/dl, showing promise for future in-car driving management paradigms. © 2014 IEEE.

关键词： Ultrasonic devices

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A Risk Identification Method for Power Operation Scenarios Using Image Caption and Semantic Text Similarity Analysis

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4488-4498页

作者： Li, Wei Ma, Fuqi Jia, Rong Wang, Bo Wang, Hongxia Alharbi, Abdullah M. Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China University of Denver Department of Electrical and Computer Engineering DenverCO80208 United States Prince Sattam bin Abdulaziz University Department of Electrical Engineering College of Engineering Wadi ad-Dawasir11942 Saudi Arabia

Power production is a complex process that involves multiple interactions, which require rich semantic knowledge to categorize and evaluate. Utilizing high-level image understanding to accurately identify risks is significant in ensuring staff safety and grid stability. In this work, we propose image captioning and semantic text similarity analysis (IC-STSA), a risk identification method for complex power operation scenarios the employs image captioning and semantic text similarity analysis. First, image caption is used to transform an image at the site into the text that best describes the visual data. Next, a safe power production (SPP) knowledge base is established, after which semantic text similarity analysis is introduced to transform the risk identification into a text classification problem. By comparing the image caption information and the SPP knowledge base and identifying similarities, we achieve refined descriptions of operation scenarios and thereby risk identification. In the final step, actual images are used to verify the findings of the IC-STSA. Results show the proposed method performs well, generating accurate, targeted descriptions, and risk identification. © 2025 IEEE. All rights reserved.

关键词： Semantics Decoding Visualization Safety Production Feature extraction Electronic mail Vectors Long short term memory Knowledge based systems

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A Tennis Momentum Analysis Method Based on Gaussian Dynamics and Machine Learning 24

A Tennis Momentum Analysis Method Based on Gaussian Dynamics...

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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Long, Zheng Li, Na Sun, Mengqing Luo, Jiawen Pan, Ting Yin, Yujie Yang, Xinhua School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou China School of Economics and Management Lanzhou University of Technology Gansu Lanzhou China College of Energy and Power Engineering Beihang University Beijing Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machine learning techniques to quantitatively analyze and predict momentum changes in tennis matches. By selecting and defining key indicators such as serve success rate and scoring rate, we developed an algorithm to identify and predict momentum shifts. The proposed method employs ensemble learning techniques, specifically Stacking, to combine multiple machine learning models, enhancing prediction accuracy and execution efficiency. Experimental validation using data from major tennis tournaments, including the 2023 Wimbledon Championships, demonstrates the effectiveness of the proposed approach. The results show that the Stacking model outperforms individual models in accuracy and robustness, providing scientific decision support for coaches and players. This method has low computational requirements, is simple to implement, and considers a wide range of variables, making it highly efficient. Future research will focus on further optimizing the model and applying it to other sports and match types. © 2024 Copyright held by the owner/author(s).

关键词： Gaussian dynamics model

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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Efficient Cross-modal Prompt Learning with Semantic Enhancement for Domain-robust Fake News Detection 31

Efficient Cross-modal Prompt Learning with Semantic Enhancem...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Fei Jin, Hao Hu, Changhui Ji, Yimu Jing, Xiao-Yuan Jiang, Guo-Ping College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Guangdong Provincial Key Laboratory of Petrochemical Equipment Intelligent Security Guangdong University of Petrochemical Technology Maoming China School of Computer Wuhan University Wuhan China

ISBN: (纸本)9798891761964

With the development of multimedia technology, online social media has become a major medium for people to access news, but meanwhile, it has also exacerbated the dissemination of multi-modal fake news. An automatic and efficient multi-modal fake news detection (MFND) method is urgently needed. Existing MFND methods usually conduct cross-modal information interaction at later stage, resulting in insufficient exploration of complementary information between modalities. Another challenge lies in the differences among news data from different domains, leading to the weak generalization ability in detecting news from various domains. In this work, we propose an efficient Cross-modal Prompt Learning with Semantic enhancement method for Domain-robust fake news detection (CPLSD). Specifically, we design an efficient cross-modal prompt interaction module, which utilizes prompt as medium to realize lightweight cross-modal information interaction in the early stage of feature extraction, enabling to exploit rich modality complementary information. We design a domain-general prompt generation module that can adaptively blend domain-specific news features to generate domain-general prompts, for improving the domain generalization ability of the model. Furthermore, an image semantic enhancement module is designed to achieve image-to-text translation, fully exploring the semantic discriminative information of the image modality. Extensive experiments conducted on three MFND benchmarks demonstrate the superiority of our proposed approach over existing state-of-the-art MFND methods. © 2025 Association for Computational Linguistics.

关键词： Semantics

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A Survey on Security Control and Estimation for Cyber-Physical Systems Under Cyber-Attacks: Advances, Challenges and Future Directions

Artificial Intelligence Science and Engineering

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Artificial Intelligence Science and Engineering 2025年第1期1卷 1-16页

作者： Haoyang YU Zidong WANG Lei ZOU Yezheng WANG Jun LI College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao Shandong China Department of Computer Science Brunel University London Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Automation Nanjing University of Information Science and Technology Nanjing China

Cyber-physical systems (CPSs) are regarded as the backbone of the fourth industrial revolution, in which communication, physical processes, and computer technology are integrated. In modern industrial systems, CPSs are widely utilized across various domains, such as smart grids, smart healthcare systems, smart vehicles, and smart manufacturing, among others. Due to their unique spatial distribution, CPSs are highly vulnerable to cyber-attacks, which may result in severe performance degradation and even system instability. Consequently, the security concerns of CPSs have attracted significant attention in recent years. In this paper, a comprehensive survey on the security issues of CPSs under cyber-attacks is provided. Firstly, mathematical descriptions of various types of cyber-attacks are introduced in detail. Secondly, two types of secure estimation and control processing schemes, including robust methods and active methods, are reviewed. Thirdly, research findings related to secure control and estimation problems for different types of CPSs are summarized. Finally, the survey is concluded by outlining the challenges and suggesting potential research directions for the future.

关键词： Surveys Graphical models Estimation Process control Medical services Cyber-physical systems Smart grids Security Cyberattack Smart manufacturing

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