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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Junwen Gu Yukai Feng Zhengxing Wu Jian Wang Junzhi Yu Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Achieving agile, fish-like turning remains a critical challenge in the development of robotic fish. The flexible deformation of a fish’s body significantly contributes to its turning ability. Consequently, researchers have devoted substantial efforts to enhancing the compliance and agility of robotic fish. This paper presents a novel flexible fishtail, inspired by the musculoskeletal structure of real fish, which can control the deformation of its flexible components during turns, improving the turning performance of robotic fish. The fishtail primarily consists of a carbon fiber plate with attached artificial muscles. These muscles can contract and stretch, causing the carbon fiber tail to bend and deform. To enable precise deformation control, a dynamic model of the fishtail is established based on Hamilton’s principle. Subsequently, deep reinforcement learning methods such as deep deterministic policy gradient and soft actor-critic are employed to develop a deformation controller under joint deflection. Cross-validation ensures the controller’s accuracy and robustness. The experimental results demonstrate that this controller facilitates the deformation control of the flexible tail across various turning amplitudes and speeds, allowing the fishtail to exhibit adjustable compliance or resistance to fluids during turning motion. Consequently, this enhances the agility of the robotic fish.

关键词： Artificial muscles Deformation Tail Learning (artificial intelligence) Muscles Turning Fish Robustness Carbon Robots

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Electrolyte Effect on Photoetching of Gallium Nitride

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Nanomanufacturing and Metrology 2024年第1期7卷 61-74页

作者： Huiqiang Liang Zhenghao Wei Jiongchong Fang Yanming Li Changli Li Zhirun Xie Yun Hau Ng Guosong Zeng Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and TechnologyShenzhen 518055People’s Republic of China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055People’s Republic of China School of Materials Sun Yat-Sen UniversityGuangzhou 510275People’s Republic of China School of Energy and Environment City University of Hong KongKowloon 999077Hong Kong SARPeople’s Republic of China City University of Hong Kong Shenzhen Research Institute Shenzhen Hi-Tech Industrial ParkNanshan DistrictShenzhen 518000People’s Republic of China SUSTech Energy Institute for Carbon Neutrality Southern University of Science and TechnologyShenzhen 518055People’s Republic of China

The limited material removal rate of conventional chemical mechanical polishing(CMP)significantly hinders the fabrica-tion efficiency and surface quality,thereby preventing the development of gallium nitride(GaN)-based ***,the incorporation of photoelectrochemistry in CMP has garnered increasing attention because of its potential to enhance the quality and efficiency of the GaN ***,a considerable gap still exists in the comprehensive understanding of the specific photoelectrochemical(PEC)behavior of ***,we report the influence of the electrolyte on the PEC etching of *** acids and bases were tested,with their pH being carefully *** concentrations of the cations and anions were also *** results showed that photocorrosion/photoetching was more pronounced in sulfuric acid,phosphoric acid,and nitric acid environments than in alkaline environments,but it was less pronounced in hydrochloric ***,the effects of pH and anion concentration on photoetching were investigated,and the results revealed that pho-toetching in acidic environments weakened with increasing pH levels and diminished with increasing sulfate *** underlying reasons contributing to this observation were *** findings provide ideas for improving the pho-toetching efficiency of GaN,thereby enriching the photoelectrochemical mechanical polishing(PECMP)technology of GaN.

关键词： GaN Photoelectrochemistry Photoetching pH

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A kinematic calibration method for robots based on the PGTF error model and Levenberg-Marquardt algorithm

A kinematic calibration method for robots based on the PGTF ...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhi Zheng Jin Wang Jun Zhu Luan Liu Haiyun Zhang Guodong Lu State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China Robotics Institute of Zhejiang University Hangzhou China School of Robotics Ningbo University of Technology Ningbo China Zhejiang Qiantang Robot and Intelligent Equipment Research Co. LTD Hangzhou China

Robots use motors for power output and realize joint motion through reducers and mechanical transmissions, which affect the absolute positioning accuracy. In view of this problem, an error model based on Preprocessing-Geometric-Transmission-Flexibility (PGTF) is proposed and solved by Levenberg-Marquardt algorithm. First, the theoretical kinematic model is established by MD-H model method. Second, the data was pre-processed in dimension upgrade and the robot error identification model was established for geometric error, transmission error and flexible error. Third, Levenberg-Marquardt algorithm was used to find the global convergence solution to realize the identification of error parameters, and the actual robot kinematic model was compensated. Finally, the experiment is carried out on the real robot machine. Through theoretical analysis and verification of real machine data, the average error of kenbei-5kg robot is reduced from 6.8787 mm to 0.1267 mm, the positioning accuracy is improved by 98.16%, the accuracy compensation algorithm is accurate, and the compensation effect is remarkable.

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DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory

DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inferenc...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Juyeop Han Youngjae Min Hyeok-Joo Chae Byeong-Min Jeong Han-Lim Choi Department of Aerospace Engineering KAIST Institutes for Robotics Korea Advanced Institide of Science and Technology (KAIST) Daejeon South Korea Laboratory for Information and Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA

Occupancy mapping has been widely utilized to represent the surroundings for autonomous robots to perform tasks such as navigation and manipulation. While occupancy mapping in 2-D environments has been well-studied, there have been few approaches suitable for 3-D dynamic occupancy mapping which is essential for aerial robots. This paper presents a novel 3-D dynamic occupancy mapping algorithm called DS-K3DOM. We first establish a Bayesian method to sequentially update occupancy maps for a stream of measurements based on the random finite set theory. Then, we approximate it with particles in the Dempster-Shafer domain to enable real-time computation. Moreover, the algorithm applies kernel-based inference with Dirichlet basic belief assignment to enable dense mapping from sparse measurements. The efficacy of the proposed algorithm is demonstrated through simulations and real experiments i i The code is available at: https://***/JuyeopHan/dsk3dom_public.

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Comparative Study on Fall Detection using Machine Learning Approaches 19

Comparative Study on Fall Detection using Machine Learning A...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jutharee, Wisanu Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand Institute of Field Robotics and Automation King Mongkut's University of Technology Thonburi Bangkok Thailand Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

Falls are one of the most dangerous problems for the elderly. A reliable fall detection system can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset that includes signals from portable devices. We propose a simple feature selection approach to reduce the number of effective input attributes based on a voting strategy. The dataset is classified into three categories, i.e., pre-fall, fall and post-fall using various machine learning approaches. The computational time is significantly reduced for most of the classification algorithms, and the relative reduction reaches to 66.67% with decision tree algorithm. Classification accuracy can reach as high as 95.17% when using neural network, while the relative reductions on the classification accuracy due to the feature selection do not exceed 1.50%. © 2022 IEEE.

关键词： Decision trees

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Automatic Facial Asymmetry Analysis for Elderly Stroke Detection by using Cosine Similarity 19

Automatic Facial Asymmetry Analysis for Elderly Stroke Detec...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jutharee, Wisanu Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Institute of Field Robotics and Automation Bangkok Thailand Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

For acute stroke patients, time to recognize the stroke symptom onset is crucial for the lifesaving treatment. The automatic detection of stroke signs has been increasingly developed for practical use. The better timely classification results could allow better detection of the symptoms and avoid morbidity and mortality. In this study, we proposed using cosine similarity between the left and right sides of the face as a part of features for classifying the stroke elderly patients from healthy elderly participants. We employed our method on images from the Toronto NeuroFace dataset. The results showed high classification accuracy (the average accuracy of 97.8998% from 6 classification methods). These results indicate that the cosine similarity features can be useful to detect facial asymmetry and pose very promising potential for elderly stroke detection. © 2022 IEEE.

关键词： Patient treatment

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A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

A Robust Surveillance System for AGV Using Forward-Downward ...

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Chinese Control and Decision Conference, CCDC

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC Nanjing China

Automatic Guided Vehicle (AGV) has been widely used in the warehouse for transporting the bulky and heavy cargos. However, the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to, e.g., cyber attacks, unexpected blocks of the wireless signals. In order to ensure AGV running safely, this paper presents a visual surveillance system by making full use of the measurements from the forward and downward cameras. Specifically, the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image sequences. Besides, the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference frame. Apart from that, the AGV poses from the downward camera could correct scale and poses estimated the forward view. The proposed method has been extensively performed on the developed platform. The results proves the effectiveness in using the complementary forward-downward visual measurements for AGV security surveillance.

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Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise

Lateral Walking Gait Phase Detection with Data Optimization ...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Wujing Cao Xinbo Zhu Rui Xu Changyu Li Hongliu Yu Mingming Zhang Haoyong Yu Worawarit Kobsiriphat Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems. College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jining Zhongke Intelligent Technology Co. Ltd. University of Shanghai for Science and Technology Southern University of Science and Technology Department of Biomedical Engineering National University of Singapore. National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.

关键词： Legged locomotion Training Support vector machines Solid modeling Machine learning algorithms Accuracy Exoskeletons

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Robust Control of Interleaved Boost Converter for Open-Cathode PEM Fuel Cell systems

Robust Control of Interleaved Boost Converter for Open-Catho...

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IEEE International Conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Jie Ying Tan R.M.T Raja Ismail Institute of Postgraduate Studies Universiti Malaysia Pahang Pekan Pahang Malaysia Robotics Intelligent Systems & Control Engineering (RISC) Research Group Faculty of Electrical & Electronics Universiti Malaysia Pahang Pekan Pahang Malaysia

This paper implements a thermal control and a super-twisting sliding mode (STSM) incorporated with an interleaved boost converter (IBC) for an open-cathode proton exchange membrane fuel cell (OC-PEMFC). The implementation of the thermal control is to regulate the stack temperature and adjust the air stoichiometric during the fuel cell current variation. The STSM controller is designed to ensure the fuel cell system’s robustness by achieving the reference values set. Therefore, simulation results discussed that the thermal control provides the suitable stack temperature to the system and maintains the fan voltage during temperature variations. A PI controller is designed and used as a comparison to the proposed STSM controller. Hence, the proposed STSM demonstrates its effectiveness in tracking down the reference current values for the fuel cell system.

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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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