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检索条件"任意字段=3rd International Conference on Robotics and Control Engineering, RobCE 2023"

共 3900 条记录，以下是1671-1680 订阅

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A Compliance control Strategy for an Upper-Limb Rehabilitation Robot with a Quick and Real-Time Human-Robot Interactive Force Estimation 3

A Compliance Control Strategy for an Upper-Limb Rehabilitati...

引用

3rd international Symposium on Intelligent robotics and Systems, ISoIRS 2023

作者： Li, Guoning Ye, Sijia Meng, Jingyan Zuo, Guokun Zhang, Jiaji Shi, Changcheng Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China University of Chinese Academy of Sciences Beijing China Ningbo Cixi Institute of Biomedical Engineering Ningbo China

ISBN: (纸本)9798350339963

In order to improve the safety of rehabilitation training and estimate physical human-robot interaction(pHRI) force without force sensor, this paper designs a real-time interactive force learning controller in an End-Effector Type Robot. The adaptive sliding mode term in controller is used to solve the uncertainties in the robot modeling and inertial parameter measurements. The learning law term can estimate pHRI force. To investigate the performance of this controller, experiments were designed to repeat the tracking task on a circular trajectory. The result shows that this controller can make the tracking error and the estimate error of pHRI force gradually acquire a desired stability margin. The error of resultant force of pHRI between the estimating and the measuring by the force sensor is 2.87±2.50N. This controller enables the robot to exhibit high compliance and its estimation of the pHRI force takes less time and is more accurate. © 2023 IEEE.

关键词： controllers

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Hand Motion Perception Prediction Based on Surface Electromyography 3

Hand Motion Perception Prediction Based on Surface Electromy...

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3rd international conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Fan, Chen Huang, Fuhua Jing, Ming Zhang, Xiaodong Yan, Yuanyuan Liu, Ye Zhang, Junzi Zhang, Xiaoqi Xi'an Jiaotong University City College Xi'an710018 China Xi'an Jiaotong University Xi'an710049 China Qilu University of Technology Jinan250353 China

ISBN: (纸本)9781510664814

In order to solve the real-time control problem of hand rehabilitation exoskeleton robot, a motion angle decoding model was proposed based on surface EMG signal and synchronous motion angle value. The long short-term memory neural network was used to construct the hand motion angle decoding model. During recognition, EMG signal and synchronous angle signal are sent to the model for decoding, and the output of the model is the angle prediction value after 200ms. The experimental results show that the combination of motion angle signal and EMG signal can significantly improve the decoding ability of the model. © 2023 SPIE.

关键词： Real time control

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Attentional SoftLexicon for Chinese named entity recognition 3

Attentional SoftLexicon for Chinese named entity recognition

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3rd international conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Liu, Dongsheng Peng, Zhiping He, Jieguang Liang, Zhushen Qiu, Jinbo Cui, Delong School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Electronic and Information Engineering Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9781510681880

Some effective word embeddings have been proposed by researchers and acted in NER (Named Entity Recognition) tasks, but these embeddings often suffer from under-utilization, resulting in poorly characterized word vectors encoded. Driven by the lack of entity information, I perform lexical enhancement techniques at the input layer of the NER model for Chinese. Roughly speaking, we introduce an attention mechanism to the Softlexicon approach, which makes the embedded entity information more complete. We conducted experiments. On all four Chinese NER datasets, the performance of the improved Softlexicon is improved compared to the original one. In addition to using different datasets, we also embedded the improve d Softlexicon in combi nation with different pre-trained models, and the attention mechanism still led to good performance. © 2024 SPIE.

关键词： Embeddings

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Safety Design of Uncontained Engine Rotor Failure for Civil Aircraft Primary Flight control System 3

Safety Design of Uncontained Engine Rotor Failure for Civil ...

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2023 3rd international conference on Mechatronics Technology and Aerospace engineering, ICMTAE 2023

作者： Wang, Xiao-Hui Shanghai Aircraft Design and Research Institute Shanghai China

The safety design of uncontained engine rotor failure for civil aircraft primary flight control systems is more difficult than other airborne systems. This paper presents a safety design method that establishes a two-stage collision model inspection mechanism and introduces an automatic analysis algorithm in the process of comprehensive impact analysis of the cascade system. The method applies to the detailed design stage of civil aircraft systems. It can reduce the difficulty of the safety analysis process and improve the accuracy of the analysis results. The verification of a civil aircraft primary flight control system design shows that after using the safety design method, the time cost of the analysis process is significantly reduced, and the analysis results are more accurate, thereby reducing the difficulty of design changes. © Published under licence by IOP Publishing Ltd.

关键词： Flight control systems

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Assessment of Depression Level Based on Multi-Scale 3D Convolution and Expression Videos 3

Assessment of Depression Level Based on Multi-Scale 3D Convo...

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3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Cao, Qishuang Gao, Junlong Lv, Zeying Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

The automatic diagnosis of depression using deep learning has recently shown significant progress, and 3DCNN has been applied to assessing depression levels from videos. There are two problems to be encountered applying 3DCNN, including insufficient data to train the model and an insufficient diversity of features extracted on a single scale. An approach is presented in this paper to address these issues. Firstly, we augmented the raw data by adding noise and applying rotations to increase the amount of the training set. Secondly, we constructed a 3DCNN to extract more diverse features from multiscales. We evaluated our proposed method on the AVEC 2014. Our approach achieved MAE=7.13 and RMSE=9.16, demonstrating its effectiveness in improving the accuracy of depression assessment compared to existing approaches. © 2023 SPIE.

关键词： Deep learning

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Interleaved Boost Converter control Technique Improvements for Fuel Cell Electric Vehicles 1

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3rd international conference on Electronic engineering and Renewable Energy, ICEERE 2022

作者： Benzaouia, Soufyane M’Sirdi, Nacer Rabhi, Abdelhamid Oubbati, Brahim Khalil Zouggar, Smail LIS UMR CNRS 7020 of Aix Marseille University Marseille France Laboratory of Modelisation Information and Systems—MIS UPJV Amiens France LTSS Laboratory Laghouat University Laghouat Algeria Laboratory of Electrical Engineering and Maintenance—LEEM University Mohammed 1st High School of Technology Oujda Oujda Morocco

ISBN: (数字)9789811962233

ISBN: (纸本)9789811962226

This study presents some control technique improvements for an interleaved boost converter for fuel cell electric vehicles. Self-tuning PI controller and an adaptive PI controller using fuzzy logic technique have been designed and developed to improve the voltage tracking accuracy. Several scenarios and tests including load disturbances were carried out to evaluate and to compare the performance of the improved controllers with the conventional one. The obtained results show a superior and high performance of the improved techniques compared to the fixed gains PI controller without adding any extra sensors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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An Experimental Analysis to Measure the Impact of Data Size on Accuracy of Deep Learning Model to Detect and Classify Monkey-Pox Using Skin Lesion Images 3

An Experimental Analysis to Measure the Impact of Data Size ...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Kaur, Gurpreet Handa, Disha Jalodia, Vikas Shukla, Anand Kr. University Institute of Computing Chandigarh University India School of Engineering and Technology Noida International University India

ISBN: (纸本)9798350342338

As a zoonotic disease, Monkey pox is caused by an orthodox virus. This is communicable disease and highly contagious. Detection of the virus at the early stage may stop its spread in the large community. Rapid and accurate diagnosis of monkey-pox is important for effective management and control of outbreaks. Deep learning techniques, a sub-branch of AI, are especially fruitful in visual detection research and many researchers have done fruitful effort in this area. In this study, with the help of experiments, we are doing comparative analysis of detection techniques based on size of dataset. The objective behind is to validate the relationship between accuracy and size of the data. In this study, the analysis is based on the relationship of the size of dataset and accuracy of the model. In order to make observations we have used two different sized datasets and observed that the increase of data of dataset size increases the accuracy of the model. © 2023 IEEE.

关键词： classification Deeplearning Detection Machine learning Monkeypox

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Framework for Design and control of Automatic Stone—Glass Separator 1

引用

3rd Biennial international conference on Future Learning Aspects of Mechanical engineering, FLAME 2022

作者： Srivastava, Priyank Siddharth Vipul Anmol Shivam Amartya Sharma, Sanjeev Kumar Amity University Noida India

ISBN: (数字)9789819930333

ISBN: (纸本)9789819930326

Nowadays, the technology that has been into the industry revolves around the use of sensors and motors to segregate the waste that is put into the machine/bins. Machine automatically senses the type of waste being put into it and it segregates and pushes down the waste into the respective bin, but these machines have been built on an industrial scale which uses massive conveyer belts and more advanced technology to sense the type of the waste, and these machines are quick but very expensive. The automation in these machines is of utmost importance, and different controls are required for temperature management, operation, and other specific variable control. The present work is the effort of making a scalable model of stone and gas segregator machine. The objective is to segregate glass with similar materials having similar properties. In this study, a framework is proposed for designing and controlling the various sensors that will be used in machine. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Belt conveyors

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Robotic Manipulators: A control Architecture Approach Based on Deep Reinforcement Learning and Simulation Environments

Robotic Manipulators: A Control Architecture Approach Based ...

引用

Electrical, Computer, Communications and Mechatronics engineering (ICECCME), international conference on

作者： Carlos Calderon-Cordova Roger Sarango Dept. of Computer Science and Electronics Universidad Tecnica Particular de Loja Loja Ecuador

With the rapid technological advancement and the increasing need for more intelligent robotic systems in various work areas, it is essential that such systems exhibit improved robustness and control characteristics. In this context, Deep Reinforcement Learning has become a key technique for developing more accurate and efficient control models in robotics. Deep Reinforcement Learning involves training models from a simulated environment that allow different scenarios to be explored without the limitations and constraints of the real world. This allows robotics engineers and developers to fine-tune and improve the control systems of robotic manipulators, making them more reliable and effective in their work. In this article, we present an overview of the main tools, algorithms and simulators used for end-to-end control of robotic manipulators. The use of simulators and Deep Reinforcement Learning techniques in robotic manipulator training allows developers to train robots safely and effectively, without having to deal with the costs and risks associated with training robots in real environments. In addition, concrete examples and applications are included that illustrate how these techniques can be applied to improve the performance and efficiency of robotic manipulator control.

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Accident-CLIP: Text-Video Benchmarking for Fine-Grained Accident Classification in Driving Scenes 3rd

Accident-CLIP: Text-Video Benchmarking for Fine-Grained Acc...

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Yang, Kuan Fang, Jianwu Zhu, Tong Xue, Jianru College of Transportation Engineering Chang’an University Xi’an China The Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9789819710867

Road accident classification which is an essential but rarely explored problem in the safe driving field. The core issue of accident classification is to learn the feature representation for partitioning different kinds of accidents. Compared with accident detection or anticipation only with occurrence probability, feature representation learning in accident classification is more challenging because of the extremely imbalanced accident categories. In addition, the severe light or weather conditions, various occasions, and complex crashing-object movement exacerbate the challenges. In this work, we form a text-video benchmark for fine-grained road accident classification (named Accident-CLIP). Accident-CLIP owns 13,669 dashcam videos with 58 kinds of accidents, where each accident is annotated with the text description of the accident type and the accident window. In the benchmarking stage, six state-of-the-art methods are evaluated from different video frame sampling methods, frame mixup strategy, input frame length, and the adaptation for long-tailed accident distribution. From experiments, we observe that current video classification models need a large space (the best Top-1 value is 41.39% on 2000 testing videos) to adapt to the extremely imbalanced road accident classification, and the formed Accident-CLIP benchmark provides a promising evaluation platform. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Roads and streets

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