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第43届中国控制会议

作者： Jianpu Cai Zhigang Ren Bo Zhang Zongze Wu School of Automation Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Injection molding, as a common plastic processing technology, has significant importance in modern manufacturing industry. However, due to the nonlinear dynamic characteristics of injection molding machines(IMMs) and the influence of various factors such as raw materials, equipment, and environment during the injection molding process(IMP), uncertainty residuals are generated, which makes it difficult to achieve optimal tracking control of injection speed, thereby affecting product quality and production efficiency. To address this issue, we propose a learning-based model predictive control(LMPC) *** scheme uses Gaussian process regression(GPR) to predict and model uncertainty residuals in the IMP. The residual model of IMM is learned through historical injection molding data, and the model predictive control(MPC) is used to achieve optimal tracking control of injection speed based on the predicted model, thereby automatically adjusting the injection speed according to requirements. In addition, we use differential dynamic programming with control constraints to solve the optimization ***, we conduct comprehensive numerical simulation experiments to prove the effectiveness and efficiency of the proposed LMPC scheme in injection speed control of IMM.

关键词： Injection molding model predictive control optimal control gaussian process learning control

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RHNet: A Real-Time High-Resolution Network for Surface Defect Detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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Efficient Network Compression through Smooth-Lasso Constraint

Efficient Network Compression through Smooth-Lasso Constrain...

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2020 International Conference on Communications, Information System and Computer engineering, CISCE 2020

作者： Ye, Xiaowei Xu, Ning Liu, Xiaofeng Yao, Xiao Jiang, Aiming Hohai University The College of IoT Engineering Changzhou China Hohai University Changzhou Key Laboratory of Robotics and Intelligent Technology College of Iot Engineering Dept. of Communication Engineering Changzhou China

ISBN: (纸本)9781728197616

The powerful capabilities of deep convolutional neural networks make them useful in various fields. However, most edge devices are difficult to afford the huge amount of parameters and high computational cost. Therefore, it is highly imperative to compress these huge models to make them lightweight to enable real-time inference on edge devices. Channel pruning is a mainstream method of network compression. Generally, the Lasso constraint is imposed on the scaling factor in the batch normalization layer to make them tend to zero for selecting unimportant channels and then prune them. However, Lasso is a non-smooth function that is not derivable at zero, we experimentally find that when the value of the loss function is small, it is difficult to decline continuously. Aiming at the above problems, this paper proposes a pruning strategy based on the derivable function Smooth-Lasso, using Smooth-Lasso as a regularization constraint to perform sparse training and then prune the network. Experiments on benchmark datasets and convolutional networks show that our method can not only make the loss function converge quickly, but also save more storage space and computational cost than the baseline method while maintaining the same level of accuracy as the original network. © 2020 IEEE.

关键词： Convolutional neural networks

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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Shifting Technique for Cogging Torque Suppression in Yokeless and Segmented Armature Machines

Shifting Technique for Cogging Torque Suppression in Yokeles...

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Power Systems and Electrical technology (PSET), IEEE International Conference on

作者： Feng Yi Chi Zhang Shuheng Qiu Jinhua Chen Wei Liu Xindong Shu Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo Institute of Materials & Technology and Engineering Ningbo China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials & Technology and Engineering Ningbo China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

This paper proposed the shifting technique for cogging torque suppression in yokeless and segmented armature (YASA) machines. This shifting technique includes the rotor, stator, and hybrid shifting. The bilateral cogging torques produced from the lower and upper air gaps can be compensated by regulating the rotor and stator shifting values. Subsequently, an analytical model is formulated based on the superstition principle, which yields the optimal shifting value for cogging torque minimization. The impact of the rotor, stator, and hybrid shifting techniques on cogging torque suppression is analyzed, respectively. In addition, comprehensive comparisons of electromagnetic performance are conducted, in terms of the air-gap flux density, back-EMF, cogging torque, and electromagnetic torque. The comparative results demonstrate a reduction in cogging torque by 15.8%, 17.1%, and 11.1% with the rotor, stator, and hybrid shifting, respectively, without compromising output performance.

关键词： Forging Analytical models Torque Air gaps Rotors Stators Minimization Permanent magnets Power systems Electromagnetics

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Planning strategy for intruder agent based on game theory and artificial potential field

Planning strategy for intruder agent based on game theory an...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Xu, Jiahong Liu, Xiaofeng Zhou, Xu Wang, Huan Lin, Qiguang Jiangsu Key Laboratory of Special Cloud Robots Changzhou213022 China Changzhou Key Laboratory of Cloud Robots and Intelligent Technology Changzhou213022 China College of IoT Engineering Hohai University Changzhou213022 China

ISBN: (纸本)9781665436786

In our work, we propose a planner based on game theory and artificial potential field (APF) that enables intruder to confront defender in 2-D environment. The intruder would compute the mixed strategy approximate Nash equilibrium in the space of agent APF trajectory to maximally approach the target region while evading the pursuit of defender. First, according to the improved APF, the finite pure strategy matrix of agents is obtained by considering the opponent's response to the agents' actions iteratively. Then, Particle Swarm optimization (PSO) is taken to compute the mixed strategy approximate Nash equilibrium based on the expected profit composed of pure strategy matrix. Final, extensive simulation tests on agents are conducted to measure the performance of the planner we proposed. The simulation results show that our method not only has great advantages in the face of greedy defenders, but also is superior to the traditional APF planning algorithm. © 2021 IEEE.

关键词： Game theory

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An Assembly Sequence Planning Method Using Ant Colony Algorithm for Arbitrary Placed Workpieces

An Assembly Sequence Planning Method Using Ant Colony Algori...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Hou, Ruiming Xu, Sheng Yang, Chenguang Zhu, Bing Duan, Jianghua Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Science Beijing101408 China Shandong Institute of Advanced Technology Cas Shandong250102 China South China University of Technology College of Automation Science and Engineering Guangzhou510640 China Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350327182

This paper proposes a particular and effective online sequence planning method to help manipulators complete complex assembly tasks. In many existing studies, the workpiece is constrained to be placed at a particular initial position. To overcome this drawback, we combine the ant colony algorithm with a target detection algorithm, which could get the positions of the workpieces in real time for online sequence planning. In addition, the constraint and obstacle avoidance of different workpieces in the assembly process is also considered. According to the experiment results, an observation is found that the new method is superior to the general way in terms of efficiency and cost function value. Furthermore, the proposed method effectively solves different practical and feasible assembly sequence planning problems. © 2023 IEEE.

关键词： Ant colony optimization

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Optical Micro/Nano Fibers Enabled Smart Textiles for Human-Machine Interface

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Advanced Fiber Materials 2022年第5期4卷 1108-1117页

作者： Shuqi Ma Xiaoyu Wang Pan Li Ni Yao Jianliang Xiao Haitao Liu Zhang Zhang Longteng Yu Guangming Tao Xiong Li Limin Tong Lei Zhang Research Center for Intelligent Sensing Zhejiang LabHangzhou 310000People’s Republic of China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074People’s Republic of China State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027People’s Republic of China Tencent Robotics X Lab Tencent Technology(Shenzhen)Co.Ltd.Shenzhen 518054People’s Republic of China

Wearable human-machine interface(HMI)is an advanced technology that has a wide range of applications from robotics to augmented/virtual reality(AR/VR).In this study,an optically driven wearable human-interactive smart textile is proposed by integrating a polydimethylsiloxane(PDMS)patch embedded with optical micro/nanofibers(MNF)array with a piece of *** by the highly sensitive pressure dependent bending loss of MNF,the smart textile shows high sensitivity(65.5 kPa^(−1))and fast response(25 ms)for touch *** from the warp and weft structure of the textile,the optical smart textile can feel slight finger slip along the ***,machine learning is utilized to classify the touch manners,achieving a recognition accuracy as high as 98.1%.As a proof-of-concept,a remote-control robotic hand and a smart interactive doll are demonstrated based on the optical smart *** optical smart textile represents an ideal HMI for AR/VR and robotics applications.

关键词： Smart textiles Human-machine interfaces Optical micro/nano fiber tactile sensor Augmented/virtual reality robotics

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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High-Speed Trajectory Tracking Control for Quadrotors via Deep Reinforcement Learning

High-Speed Trajectory Tracking Control for Quadrotors via De...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hean Hua Hang Zhong Hui Zhang Yongchun Fang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Robotics Hunan University Changsha China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.

关键词： Training Adaptation models Trajectory tracking Heuristic algorithms Optimal control Deep reinforcement learning Hardware

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