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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiaqi Li Hongxu Li Yifan Wu Hailiang Zhou Luigi Manfredi Peng Li Hong Zhang Department of Key Lab of Industrial Computer Control Engineering Hebei Province Yanshan University Qinhuangdao China Department of Computing Science University of Alberta Edmonton AB Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. Department of Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots.

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Sensor Fault Detection in nonlinear system using Adaptive Weighted Fusion Algorithm

Sensor Fault Detection in nonlinear system using Adaptive We...

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第40届中国控制会议

作者： Fan Qin Shu-ya Yan Yi-ming Fang Jian-xiong Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University

In view of the problem that the state estimation contains fault values when detecting sensor faults containing noise based on the Kalman filter algorithm,a sensor fault detection method based on adaptive weighted fusion is proposed in this *** adaptive weighted fusion algorithm gives different weights to several groups of measurement output data to generate the measurement output without ***,the composite embedded cubature Kalman filter algorithm is used to get the state estimation without faults,which will make the mean or variance of the generated residuals change,so as to realize fault ***,the single-link direct drive manipulator system driven by permanent magnet brush DC motor is simulated and *** results show that,compared with the CECKF algorithm alone,the proposed method can eliminate the faults contained in the Kalman filter state estimation,and detect all the output sensor faults more effectively.

关键词： Sensor fault detection Adaptive weighted fusion State estimation Kalman filter

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Super-twisting Finite Time control for Third Order Nonlinear Systems

Super-twisting Finite Time Control for Third Order Nonlinear...

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Chinese control Conference (CCC)

作者： Xinjian Liu Jianxiong Li Zhi Li Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Qinhuangdao China The Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

A novel super-twisting control method is proposed for a class of third-order systems with matched Lipschitz disturbance in this paper. The proposed method ensures finite-time convergence to the fourth-order sliding set, by using a homogeneous, continuously differentiable and strict Lyapunov function. Finally, numerical simulation results are shown to illustrate the effectiveness of the proposed method.

关键词： Uncertain systems Numerical simulation Nonlinear systems Sliding mode control Convergence Lyapunov methods

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy industrial Park

arXiv

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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Liu, Yuxiang Wang, Zhaojian Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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An improved model predictive control method for wave energy converters

An improved model predictive control method for wave energy ...

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第40届中国控制会议

作者： Zhenchun Wang Feng Luan Nianguo Wang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Engineering Research Centre of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University

The model accuracy and objective function complexity are key issues that remain in the realisation of model predictive control（MPC） of wave energy converters（WECs）.In this study,the viscous force is considered in a WEC model to improve model accuracy,and an improved model predictive control（IMPC） method is proposed that can reduce the quadratic programming（QP） performance requirements of the processor while maintaining a high energy conversion *** effectiveness of the IMPC method was verified through numerical simulations.

关键词： Viscous force Wave energy converters Model predictive control

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ERT-GFAN: A multimodal drug–target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism

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computers in Biology and Medicine 2024年 180卷 109012-109012页

作者： Cheng, Xiaoqing Yang, Xixin Guan, Yuanlin Feng, Yihan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Automation Qingdao University Qingdao266071 China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao266071 China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of Education Qingdao University of Technology Qingdao266071 China

In drug discovery, precisely identifying drug–target interactions is crucial for finding new drugs and understanding drug mechanisms. Evolving drug/target heterogeneous data presents challenges in obtaining multimodal representation in drug–target prediction(DTI). To deal with this, we propose ‘ERT-GFAN’, a multimodal drug–target interaction prediction model inspired by molecular biology. Firstly, it integrates bio-inspired principles to obtain structure feature of drugs and targets using Extended Connectivity Fingerprints(ECFP). Simultaneously, the knowledge graph embedding model RotatE is employed to discover the interaction feature of drug–target pairs. Subsequently, Transformer is utilized to refine the contextual neighborhood features from the obtained structure feature and interaction features, and multi-modal high-dimensional fusion features of the three-modal information constructed. Finally, the final DTI prediction results are outputted by integrating the multimodal fusion features into a graphical high-dimensional fusion feature attention network (GFAN) using our innovative multimodal high-dimensional fusion feature attention. This multimodal approach offers a comprehensive understanding of drug–target interactions, addressing challenges in complex knowledge graphs. By combining structure feature, interaction feature, and contextual neighborhood features, ‘ERT-GFAN’ excels in predicting DTI. Empirical evaluations on three datasets demonstrate our method's superior performance, with AUC of 0.9739, 0.9862, and 0.9667, AUPR of 0.9598, 0.9789, and 0.9750, and Mean Reciprocal Rank(MRR) of 0.7386, 0.7035, and 0.7133. Ablation studies show over a 5% improvement in predictive performance compared to baseline unimodal and bimodal models. These results, along with detailed case studies, highlight the efficacy and robustness of our approach. © 2024 Elsevier Ltd

关键词： Forecasting

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Reinforcement Learning Combined with Heuristic Search for Solving Discrete Space Path Planning Problems

Reinforcement Learning Combined with Heuristic Search for So...

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第33届中国控制与决策会议

作者： Xiuling Zhang Xuenan Kang Kailun Wei Jinxiang Li Kai Ma Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Reinforcement learning(RL) has been successfully applied to solve path planning problems,but learning is generally *** main reason is not making full use of information collected during interaction with the *** paper proposes a novel method to solve the discrete space path planning problem in an environment without prior knowledge with intensive obstacles based on RL and heuristic ***,we apply Dyna-Q algorithm of RL to explore the map and search for the target point and optimize its policy with upper confidence bound(UCB).Then,when the target point is found,we use heuristic search to plan the path from the starting point to the target point and narrow the path to a small ***,we combine Dyna-Q algorithm with the heuristic search recommended path for path *** evaluate our algorithm using maze navigation *** results verify that heuristic search accelerates Dyan-Q convergence.

关键词： Dyna-Q algorithm Heuristic search Path planning Discrete space

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ICA and PIND based Remainder Particle Detection Technique for Space-borne Equipment

ICA and PIND based Remainder Particle Detection Technique fo...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhishuai Jiang Zengshuai Qiu Yuanda Tian Junyu Shi Chengzhong Yu Hongzhi Gao Yuliang Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao China DFH Satellite Co. Ltd. Beijing China

ISBN: (纸本)9781665405362

Space-borne equipment is a major component of the satellite system, which plays a very important role in the process of satellite launch and reception. Therefore, the satellite system has high requirements for the reliability and safety of space-borne equipment. The remainder particle are metal and non-metal particles inside the space-borne equipment that affect the normal operation of the space-borne equipment. When the space-borne equipment is in a weightless state, the remainder particle will produce irregular movement accordingly cause huge safety hazards to the space-borne equipment. Particle impact noise detection (PIND) by way of vibrating the space-borne equipment cause remainder particle in the space-borne equipment to collide with the inner wall of the space-borne equipment. Then, the acoustic emission sensor is used to receive the sound generated by the vibration of the remainder particles. We can analyze the received sound signal to determine whether the space-borne equipment contains remainder particle. However, how to avoid the interference of environmental noise has always been a thorny issue in the process of using PIND to detect remainder particle. In the process of detecting noise, there may be environmental noise that will inevitably affect the detection of remainder particle. In order to filter out the environmental noise, we recorded mixed audio of the remainder particle. Then the ICA algorithm is used to separate the remainder particle signal. Through many experiments, we learn that the recognition rate of this judgment method reaches 100%.

关键词： Vibrations Satellites Working environment noise Metals Transforms Interference Robot sensing systems

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Mesenchymal stem cells loaded on 3D-printed gradient poly(e-caprolactone)/methacrylated alginate composite scaffolds for cartilage tissue engineering

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Regenerative Biomaterials 2021年第3期8卷 70-83页

作者： Yanyan Cao Peng Cheng Shengbo Sang Chuan Xiang Yang An Xiaochun Wei Zhizhong Shen Yixia Zhang Pengcui Li Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education MicroNano System Research CenterCollege of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of OrthopedicsThe Second Hospital of Shanxi Medical UniversityTaiyuan 030001China Department of Plastic Surgery Peking University Third HospitalBeijing 100191China Department of Biomedical Engineering College of Biomedical EngineeringTaiyuan University of TechnologyTaiyuan 030024China

Cartilage has limited self-repair ability due to its avascular,alymphatic and aneural *** combination of three-dimensional(3D)printing and tissue engineering provides an up-and-coming approach to address this ***,we designed and fabricated a tri-layered(superficial layer(SL),middle layer(ML)and deep layer(DL))stratified scaffold,inspired by the architecture of collagen fibers in native cartilage *** scaffold was composed of 3D printed depth-dependent gradient poly(e-caprolactone)(PCL)impregnated with methacrylated alginate(ALMA),and its morphological analysis and mechanical properties were *** prove the feasibility of the composite scaffolds for cartilage regeneration,the viability,proliferation,collagen deposition and chondrogenic differentiation of embedded rat bone marrow mesenchymal stem cells(BMSCs)in the scaffolds were assessed by Live/dead assay,CCK-8,DNA content,cell morphology,immunofluorescence and real-time reverse transcription polymerase chain ***-loaded gradient PCL/ALMA scaffolds showed excellent cell survival,cell proliferation,cell morphology,collagen II deposition and hopeful chondrogenic differentiation compared with three individual-layer ***,our study demonstrates the potential use of the gradient PCL/ALMA construct for enhanced cartilage tissue engineering.

关键词： three-dimensional printing cartilage tissue engineering PCL methacrylated alginate bone marrow mesenchymal stem cells

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