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2024 9th International Seminar on computer Technology, Mechanical and Electrical engineering, ISCME 2024

作者： Li, Hui Yang, Hao Zhang, Suli Miao, Jiahao School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun130012 China

In this research, a fuzzy adaptive PD control approach is introduced for managing the coupled indoor temperature and humidity system. Initially, the mathematical framework of indoor temperature and humidity is analyzed. For the coupled system, a decoupling control strategy is introduced, which eliminates the mutual influence between temperature and humidity by incorporating decoupling components, thereby enabling the system to be equivalently controlled as two independent single-input single-output systems. Secondly, a fuzzy adaptive PD control method is employed to achieve parameter self-tuning. Finally, to assess the efficacy of the introduced control approach, the present study undertakes a simulation analysis, utilizing a medium-sized office as a case study. The outcomes of the simulation suggest that upon implementing the fuzzy adaptive PD control methodology, the modulation of indoor temperature and humidity achieves enhanced precision and swiftness, notably demonstrating greater robustness in the presence of disturbance signals. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Self tuning control systems

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Fully Distributed Model-Free Adaptive Sliding Mode control for MASs With Hybrid-Attacked Topology

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi 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

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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MedFILIP: Medical Fine-grained Language-Image Pre-training

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3587-3597页

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo The School of Computer Science and Technology Harbin Institute of Technology Harbin China The Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The College of computer and control engineering Northeast Forestry University Harbin China The Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia The Department of Biomedical Engineering Department of Computer Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestXray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. © 2013 IEEE.

关键词： Semantics

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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Enhancing deformation characteristics prediction of coarse-grained soils with time-series generative adversarial network-based data augmentation and pre-training

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Frontiers of Structural and Civil engineering 2025年第3期19卷 396-410页

作者： Ying ZHANG Meng JIA Xuedong ZHANG Liping CAO Ziying AN Hongchao WANG Jinyu WANG School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China The State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China

Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.

关键词： coarse-grained soils deformation characteristics TimeGAN data augmentation pre-training

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Analysis of the External Characteristics and Eddy Current Loss of PMSG-Based WECS Relying on a Multi-Domain Coupling Model 19th

Analysis of the External Characteristics and Eddy Current Lo...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zuo, Peng Li, Weili Liu, Xiaoke Liu, Wenmao Li, Dan Qiao, Tianhuai School of Electrical Engineering Beijing Jiaotong University Beijing China Department of Electrical Engineering Tsinghua University Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9789819613861

With the trend towards larger-scale wind generators, the internal physical field and control strategy of high-capacity generator is becoming increasingly complex. The physical field change law inside the generator is often overlooked by the conventional wind energy conversion system (WECS) model. Therefore, developing a comprehensive and precise WECS model is essential for the safe operating of wind generators. Aiming at the above problems, this paper takes a 5 MW direct-drive permanent magnet synchronous generator (PMSG) as an example, and establishes a multi-domain coupling model. This approach can be used to investigate how the external characteristics of PMSG change under different operating conditions. Additionally, it has the ability to monitor how eddy currents are distributed in permanent magnets (PM), supporting generator design theoretically. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Analysis of Subsynchronous Oscillations Caused by Interactions of DFIG-Based Farms and SVGs

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IEEE Transactions on Power Systems 2025年第2期40卷 1570-1582页

作者： Li, Kejiang Chen, Wuhui Qin, Wei Li, Canbing Qin, Wenping Qiu, Jing Guo, Xiaolong Chang, Xiqiang Liu, Defu College of Electrical and Power Engineering Taiyuan030024 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia State Grid Xinjiang Electrical Power Company Ltd. Xinjiang Control Center for Power Dispatching Wulumuqi830002 China

The static var generators (SVGs) installed at the point of common coupling (PCC) of wind farms can significantly impact the sub-synchronous oscillation (SSO) performance of the power system with grid-connected wind farms. For example, in 2017 multiple SSO events were caused by interactions among a doubly fed induction generator (DFIG) based farm, constant reactive power mode (CRPM) based SVGs, and the weak grid in Xinjiang, China. This paper reveals the mechanisms of the SSO caused by the interaction among DFIG-based farms, SVGs with different control modes, and weak grids. It is discovered that CRPM-based SVGs with low current controller bandwidths (CCBWs) can suppress SSOs in the power system with grid-connected DFIG-based farms, but that constant voltage mode (CVM) based SVGs and constant power factor mode (CPFM) based SVGs can worsen the SSO performance of the system. Furthermore, it is revealed why the amplitude of the super-synchronous component is larger than the sub-synchronous component in these events by deriving their amplitude ratio equation. Finally, the theoretical analysis results are verified by the small-signal method and electromagnetic transient (EMT) simulation. © 1969-2012 IEEE.

关键词： Phase locked loops

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Communication Countermeasure Strategy Decision-Making Based on Incomplete Information Game 27

Communication Countermeasure Strategy Decision-Making Based ...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Liu, Jinyue Gao, Xiang Yang, Haowei Zhang, Jihao Liu, Yuhan Gong, Peng National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Group Beijing China

ISBN: (纸本)9791188428137

In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communication technologies, the electromagnetic environment has become increasingly complex, making intelligent decision-making in communication countermeasures a prominent research focus. However, most existing studies adopt deep learning and reinforcement learning methods, which require extensive data samples and computational resources. To address these limitations, this paper proposes a game-theoretic model for intelligent radio communication countermeasures under incomplete information. It quantitatively evaluates common attack and defense tactcis used in communication countermeasures and explores the mixed-strategy equilibrium of the game when both the attackers and defenders consist of multiple types. Furthermore, simulation-based comparative analyses are conducted to assess the factors influencing the utility of both the attacker and defender. The findings offer theoretical insights and decision-making support for the deployment of communication countermeasure strategies in practical military operations, contributing to significant strategic value. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep reinforcement learning

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