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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mingyi Hu Junlei Song Yi Yan Ying Liu Xiaoxue Li Kaifeng Dong Fang Jin Wenqin Mo Yajuan Hui School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

During super low frequency (SLF) magnetic induction communication, the sensitive antenna carried by autonomous underwater vehicles (AUVs) is highly susceptible to vibration, resulting in a large amount of noise in communication signals, which seriously affects communication performance. The type of motion-induced noise typically exhibits randomness and nonlinearity. Based on the strong correlation between the motion-induced noise and the antenna's vibration signal, we propose a multi-channel normalized least mean square (MCNLMS) algorithm. The algorithm utilizes the six degrees of freedom vibration acceleration information from both ends of the magnetic antenna as the reference signal input for the canceller, providing more comprehensive information related to motion-induced noise than the single-channel adaptive noise cancellation algorithm. Experimental results indicate that the MCNLMS algorithm demonstrates effective suppression of the motion-induced noise, achieving an average reduction of approximately 7 dB within the 160–170 Hz communication band. Additionally, it exhibits improved stability and faster convergence.

关键词： Vibrations Industrial electronics Correlation Adaptive arrays Magnetic noise Approximation algorithms Noise cancellation Antennas Magnetic susceptibility Convergence

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Online Estimation of State of Healh for Lithium-ion Batteries Based on Attention-LSTM Netwrok Using Partial Charging Features

Online Estimation of State of Healh for Lithium-ion Batterie...

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Energy, Electrical and Power engineering (CEEPE), International Conference on

作者： Yue Yao Ying Wang Jiangfeng Zhang Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China State Grid Zhejiang Electric Power Research Institute Zhejiang China

Accurate estimation for state-of-health (SOH) of lithium-ion batteries is critical to ensure the security and durability of battery operation. Accordingly, this paper proposes a SOH estimation method based on Attention-LSTM neural network using the partial charging features. First, features are extracted from partial constant-current charging curves. Second, Pearson coefficient analysis is employed to analysis the mapping relationship between extracted features and SOH. Third, an Attention-LSTM network is utilized to realize the SOH estimation. The attention mechanism can adaptively assign weights to the input features and time steps, thereby improving the estimation accuracy. Validation experiments are conducted on public dataset from NASA Ames Prognostics Center of Excellence. The experimental results highlight that the proposed method has higher accuracy and robustness compared with other approaches.

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A Predictive Tracking control Algorithm for Initial Angle of Brushless DC Motor Based on Reinforcement Learning

A Predictive Tracking Control Algorithm for Initial Angle of...

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Asian control Conference

作者： Yu Zhu Haodong Chen Xihong Fei Kang Wang Jing Wang School of Electrical and Information Engineering Anhui University of Technology Ma'anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The initial angle detection and tracking control of brushless direct current motor (BLDC) is widely used in military, aviation, industrial, automotive, civil control systems, and household appliances. The motor is a nonlinear system, which is easily affected by external factors, so it is difficult to maintain the stability of the system with fixed control parameters. Therefore, this paper proposes a control method for initial angle tracking of BLDC based on reinforcement learning (RL). In order to determine the initial angle of the surface-mounted direct current (DC) motor, in the traditional Field-Oriented control (FOC) algorithm, the pulsating high-frequency injection algorithm is introduced and combined with the RL theory. According to the given PI parameters, the appropriate rewards are obtained through the RL controller. Then the proportional integral (PI) parameters are dynamically adjusted to make the estimated angle closer to the true angle value. This method avoids the error of angle estimation caused by fixed PI parameters in different situations, and obtains the best results by dynamically adjusting the parameters. Finally, the simulation experiment is carried out to prove the feasibility of the proposed scheme.

关键词： Home appliances Brushless DC motors Heuristic algorithms Estimation Reinforcement learning Prediction algorithms control systems

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Multi-object Tracking by Detection and Query: an efficient end-to-end manner

arXiv

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arXiv 2024年

作者： Jia, Shukun Cao, Yichao Yang, Feng Lu, Xin Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Instrument Science and Engineering Southeast University Nanjing210096 China

Multi-object tracking is advancing through two dominant paradigms: traditional tracking by detection and newly emerging tracking by query. In this work, we fuse them together and propose the tracking-by-detection-and-query paradigm, which is achieved by a Learnable Associator. Specifically, the basic information interaction module and the content-position alignment module are proposed for thorough information Interaction among object queries. Tracking results are directly Decoded from these queries. Hence, we name the method as LAID. Compared to tracking-by-query models, LAID achieves competitive tracking accuracy with notably higher training efficiency. With regard to tracking-by-detection methods, experimental results on DanceTrack show that LAID significantly surpasses the state-of-the-art heuristic method by 3.9% on HOTA metric and 6.1% on IDF1 metric. On SportsMOT, LAID also achieves the best score on HOTA metric. By holding low training cost, strong tracking capabilities, and an elegant end-to-end approach all at once, LAID presents a forward-looking direction for the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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A Segmented Sliding Window-Based Comprehensive Periodic Feature Extraction Method for APT Classification

A Segmented Sliding Window-Based Comprehensive Periodic Feat...

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International Conference on control and Automation (ICCA)

作者： Wei Song Tiejun Wu Ya Zhang School of Software Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China School of Cyber Science and Engineering Southeast University. School of Automation Southeast University Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper studies the classification problem of Advanced Persistent Threat (APT) attacks and proposes a comprehensive periodic feature extraction method based on sliding window division. A new data processing approach is proposed. Considering the characteristics of APT attacks, the life cycle of APT traffic data is divided into three stages. The traffic data from each stage is sampled in a piecewise manner and divided into separate training and testing sets, and then the training and testing subsets are merged. For the integrated sets, a sliding window-based partition approach is employed to obtain training and testing window samples, while extracting features from each individual sample. Then, this paper proposes a differential-based periodic feature extraction methodology to capture the dynamic information present in APT traffic and integrate it with fundamental static features, thereby forming effective comprehensive features. After combining these static and differential features, a comprehensive foundation is established for model training. Finally, the effectiveness of the algorithm is verified using decision tree model, and its superiority is demonstrated by comparing it with traditional partition methods based on data streams.

关键词： Training Heuristic algorithms Feature extraction Data models Partitioning algorithms Classification algorithms Decision trees

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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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Towards Intelligent and Trustworthy Crowdsourcing systems with Cyber-physical Social Intelligence via Retrieval-augmented Generation

Towards Intelligent and Trustworthy Crowdsourcing Systems wi...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jing Yang Xingxia Wang Qinghua Ni Yuhang Liu Jing Wang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

In recent years, crowdsourcing systems that tackle complex tasks through the collective efforts of many individuals have garnered substantial attention. However, the existing crowd-sourcing systems face some challenges such as overreliance on human experience and effort, information overload, and risks of privacy breaches. To overcome these challenges, this paper proposes an intelligent and trustworthy crowdsourcing frame-work based on blockchain and retrieval-augmented generation. The framework centers around foundational models to integrate the three distinct and sequentially interdependent phases: task decomposition, task allocation or recommendation, and task execution, enabling the automatic provision of a personalized, comfortable, and efficient user experience. Blockchain and smart contracts are utilized to protect user privacy and interests without leaning on any third trusted authority. Additionally, the operational process of the framework is explained and validated by taking printed circuit board design and manufacturing as a case study and ChatGPT as its generator.

关键词： Crowdsourcing Retrieval augmented generation Smart contracts Privacy breach User experience Stability analysis Blockchains Social intelligence Resource management Reliability

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Multi-Agent Coverage control in Non-Convex Annulus Region with Conformal Mapping

arXiv

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arXiv 2025年

作者： Feng, Xun Zhai, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education Wuhan430074 China

Efficiently fulfilling coverage tasks in non-convex regions has long been a significant challenge for multi-agent systems (MASs). By leveraging conformal mapping, this paper introduces a novel sectorial coverage formulation to transform a non-convex annulus region into a topologically equivalent one. This approach enables the deployment of MASs in a non-star-shaped region while optimizing coverage performance and achieving load balance among sub-regions. It provides a unique perspective on the partitioned sub-regions to highlight the geodesic convex property of the non-star-shaped region. By utilizing the sectorial partition mechanism and the diffeomorphism property of conformal mapping, a decentralized control law is designed to drive MASs towards a desired configuration, which not only optimizes the global coverage cost but also ensures exponential convergence of equitable workload. Moreover, an iterative search algorithm is developed to identify the optimal approximation of multi-agent deployment in the non-star-shaped region. Theoretical analysis is conducted to confirm the asymptotic stability and global convergence with arbitrary small tolerance of the closed-loop system. Finally, numerical simulations demonstrate the practicality of the proposed coverage formulation with conformal mapping. Copyright © 2025, The Authors. All rights reserved.

关键词： Conformal mapping

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking Process

Application of Particle Swarm Optimization Algorithm Based o...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yiheng Chen Fan Yin Xiaochong Chen Yi Ren Jie Hu Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process.

关键词： Job shop scheduling Heuristic algorithms Clustering algorithms Production Traveling salesman problems Stability analysis Robustness complexity theory Particle swarm optimization Convergence

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