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3rd International Conference on intelligent Mechanical and Human-Computer Interaction technology, IHCIT 2024

作者： Luo, Peiyue Zhang, Hongxin Tian, Liguo Xie, Ziyang Sun, Miao School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin300222 China

ISBN: (纸本)9781510683105

In this paper, an improved gray wolf optimization linear active disturbance rejection control (IGWO-LADRC) had been proposed by improving the grey wolf population initialization, convergence factor, and grey wolf position updating strategy of the traditional grey wolf optimization algorithm. The problems of standard gray wolf optimization algorithm in linear active disturbance rejection control parameters tuning, such as low efficiency of optimization search and easy fall into local optimal solution, had been solved. A brushless DC motor control system based on IGWO-LADRC and a brushless DC motor control system based on gray wolf optimization linear active disturbance rejection control (GWO-LADRC) had been built. The comparison had shown that IGWO-LADRC had a fast optimization-seeking speed, high control accuracy, quick response speed, and strong anti-interference ability. © 2024 SPIE.

关键词： Disturbance rejection

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Wireless power transmission system with CC and CV output based on hybrid LCC-S/P topology 5

Wireless power transmission system with CC and CV output bas...

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5th International Conference on Electronics and Communication, Network and Computer technology, ECNCT 2023

作者： Li, Kun Gong, Zheng Meng, Jiaheng Liu, Qing Tianjin University of Technology Tianjin Key Laboratory of Film Electronic and Communication Device Tianjin China Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China

ISBN: (纸本)9798350328660

Wireless Power Transfer (WPT) technology provides a convenient and efficient power supply solution for mobile devices. The utilization of constant current (CC) and constant voltage (CV) control in WPT systems proves valuable for charging battery applications. This paper presents a novel WPT system with CC and CV output characteristics, employing a switching topology. Specifically, the LCC-S and LCC-P topologies are applied and rigorously simulated. The proposed MCR-WPT system's output characteristics in both CC and CV modes are meticulously validated using Saber software. The simulation results distinctly demonstrate the system's capability to achieve CC and CV outputs within a varying load resistance range of 5Ω to 30Ω, thus conclusively establishing the efficacy of the topology switching technology. © 2023 IEEE.

关键词： Topology

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A Dual-branch Model for Early Detection of Alzheimer's Disease Using Resting-State fMRI 7

A Dual-branch Model for Early Detection of Alzheimer's Disea...

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7th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2024

作者： Wang, Yixuan Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images. © 2024 IEEE.

关键词： Feature extraction

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Multi-Class Token Attention Learning for Weakly Supervised Semantic Segmentation 23

Multi-Class Token Attention Learning for Weakly Supervised S...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zeng, Zhen Luo, Huilan School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000 China

ISBN: (纸本)9798331528041

Weakly supervised semantic segmentation (WSSS) using only image-level class labels is a challenging task. Due to the local receptive fields of convolutional neural networks (CNNs), CAMs applied to CNNs often suffer from partial activation-highlighting the most discriminative parts of the image. To capture both local features and global representations, we propose an approach called MCTRM, which is a WSSS solution based on an improved Conformer as backbone. To ensure each class token's discriminative ability for the respective target class, we design a class-aware training strategy (CAT) to establish a one-to-one correspondence between the output class tokens and the ground-truth class labels. To enhance the class discrimination ability between each class token, we propose a class token regularization module (CTRM) to enhance the learning of discriminative class tokens so that the model can better capture the unique features and attributes of each class. To obtain a fine-grained CAM, we propose the refinement module (RM) to combine the class-specific attention and inherent patch-level pairwise affinity between image patches learned by the Transformer branch with the CAM of the CNN branch. Despite its simplicity, MCTRM achieves the state-of-the-art performance of 69.0% and 68.4% on the PASCAL VOC 2012 validation and test sets, respectively. © 2024 IEEE.

关键词： Semantic Segmentation

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Hybrid Decision-making Strategy Combining Reinforcement Learning and Decision Rules for Highway Unmanned Driving

Hybrid Decision-making Strategy Combining Reinforcement Lear...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zhang, Lele Yang, Zeyu Yu, Caocao Hu, Ziniu Zhou, Yunshui Cui, Qingjia State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Wuxi Intelligent Control Research Institute of Hunan University Wuxi China

ISBN: (纸本)9798350384185

In recent years, reinforcement learning has been employed to address the challenge of unmanned driving decision-making, due to its ability to handle uncertainty and continuous optimization. However, compared with rule-based algorithms, its training time is considerable and the reliability of its results is difficult to guarantee. To enhance the security of decision-making processes, this paper proposes a hybrid decision-making algorithm designed to accelerate the training process of reinforcement learning and enhance the reliability of decision-making. The method first evaluates the confidence levels of both rule-based and learning-based strategies by leveraging the data collected throughout the training phase. Subsequently, an activation function is crafted to determine the final strategy with the greatest level of confidence. This guarantees that the final strategy performs at least on par with the rule-based approach. A simulation focused on highway driving was conducted as a case study to illustrate the effectiveness of the proposed method. The results demonstrate that the proposed approach offers greater profitability than the rule-based method and higher reliability compared to the learning-based method. © 2024 IEEE.

关键词： Reinforcement learning

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Design the Wind Pendulum Based on Fuzzy PID control System 3

Design the Wind Pendulum Based on Fuzzy PID Control System

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Lin, Chaoyue Fu, Qiang Cui, Wangzhan Wang, Zipeng Chen, Peng School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Tianjin University of Technology Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin China

ISBN: (纸本)9798331540319

This paper described the design of a wind pendulum system, comprising four main components: the operative unit, the data acquisition module, the human-computer interaction interface, and the algorithmic core. By leveraging the principles of the simple pendulum and Lissajous curves, the design enabled the wind pendulum to achieve precise linear and circular motions. The control strategy relied on a fuzzy PID control algorithm, which facilitated closed-loop control and enhanced system stability and performance. The schematic design and PCB layout for the control system were created using Electronic Design Automation (EDA) tools. Furthermore, MATLAB simulations and experimental validation were conducted to assess the performance of the fuzzy PID algorithm in comparison to traditional PID control. The experimental results revealed that fuzzy PID control not only reduced overshoot, shortened settling time, and improved accuracy, but also greatly improved the wind pendulum's ability to execute fixed linear and circular motions with speed and precision. This approach demonstrated the benefits of fuzzy PID in optimizing dynamic system performance, particularly in applications demanding high precision and rapid response. © 2024 IEEE.

关键词： Fuzzy PID STM32F103C8T6 Wind pendulum

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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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The Magnetic Positioning System Based on Genetic Algorithm-optimized BP Natural Network 25

The Magnetic Positioning System Based on Genetic Algorithm-o...

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25th IEEE China Conference on System Simulation technology and its Application, CCSSTA 2024

作者： Lin, Chaoyue Liu, Huajun Li, Xingyu Fu, Qiang Chen, Peng Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China Tianjin University of Technology Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin China

ISBN: (纸本)9798350366600

This article details a magnetic positioning system using a genetic algorithm-optimized BPNN. The hardware includes a control module with an Arduino Mega 2560, a data collection module using Honeywell HMC5883L magnetometers for XYZ magnetic data, and a communication module integrating I2C via TCA9548A for sensor connections. The BPNN and genetic algorithm principles are combined in MATLAB to optimize weights and biases. Results show the system converges faster, predicts coordinates closer to actual values, and improves accuracy and robustness across environments, enhancing overall system performance and stability. © 2024 IEEE.

关键词： MATLAB

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Development and Implementation of a Wisdom Course Utilizing the "teaching-Innovation-Competition" Model within the Virtual Teaching and Research Section of Mechatronic Engineering Major in China 24

Development and Implementation of a Wisdom Course Utilizing ...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Chen, Wei Ma, Wenpeng Hu, Wenhua Liu, Fulong Zhang, Xiaotao Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400712692

A new teaching model of teaching-innovation-competition is presented and a wisdom course based on this model is developed for mechatronics engineering majors, mainly to deal with several existing problems such as limited experimentation and lack of innovative inspiration in Chinese universities. This study elaborates the course objectives and structure of curriculum system, including digitalized teaching content, project-driven teaching means, innovation platform, web-based resource support, team competition and diversified evaluation method. Through a teaching case study within mechatronic engineering major, the teaching mechanism and achievements are demonstrated. The study results indicate that the proposed teaching mode based on the virtual teaching and research section, significantly improves students' innovation ability and the teaching effects, compared to traditional teaching methods. This teaching mode can also be extended to other majors for reference. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Teaching

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HTRNN: A New Protein Nuclear Localization Prediction Tool 7

HTRNN: A New Protein Nuclear Localization Prediction Tool

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7th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2024

作者： Miao, Xinyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties. © 2024 IEEE.

关键词： Proteins

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