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2022 8th International Forum on Manufacturing Technology and Engineering Materials, IFEMMT 2022

作者： Zhu, Tongbo Cai, Fan Provincial Key Laboratory of Industrial Robot Measurement and Control Rapid Die Manufacturing Minnan University of Science and Technology Fujian Quanzhou362700 China

Polishing technology is widely used in the polishing process of aspheric optical elements, complex curved surface molds, and so on because of its high machining accuracy, good flexibility, and other advantages. Based on the Preston equation, this paper deduces the material removal function of spherical self-rotating polishing by applying the kinematics principle, obtains the removal function with approximate Gaussian distribution by using computer simulation method, conducts processing technology experiments through the spherical self-rotating polishing device, analyzes the influence of polishing pressure and polishing speed on the polishing removal characteristics of ceramic materials by simulation, and verifies the stability of the removal function through experiments. The results show that the removal function graph is Gaussian distribution, which is conducive to the surface shape convergence of airbag machining. The influence of process parameters on material removal rate is in good agreement with the theory and practice and has a good linear relationship. It is a controllable and deterministic processing mode. © Published under licence by IOP Publishing Ltd.

关键词： Gaussian distribution

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Fruit target detection method based on faster R-CNN

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International Journal of Wireless and Mobile Computing 2021年第3期21卷 207-213页

作者： Yin, Guanghui Xie, Yuanmin Yun, Juntong Ning, Lichuan Liu, Ying Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the rapid development of agricultural modernisation, fruit picking is becoming more and more automatic. The detection of fruit target by machine vision technology is the key to realise fruit automatic picking. In recent years, with the development of deep learning technology, target detection algorithm based on deep learning has gradually become a hot research topic, and the detection accuracy has been greatly improved. However, the shape and size of fruits in natural environment are different, and the light intensity changes at any time, which affects the detection accuracy to a certain extent. In this paper, aiming at the problem of fruit detection and location in natural environment, based on Fast R-CNN target detection model, a fruit detection and location method combining image processing and deep learning is proposed. The experimental results show that the combination of image processing and deep learning can achieve high detection accuracy and speed. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Fruits

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Research on Model Based Optimization of Thermal Management for Pure Electric Vehicles Under Low Temperature Operating Conditions

SSRN

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SSRN 2024年

作者： Zhang, Chunfeng Wan, Yilong Yang, Yanhong Lin, Qiao Xihua University Chengdu610039 China Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province Chengdu610039 China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan Chengdu610039 China Sichuan Industrial School of Intelligent and New Energy Automobile Chengdu610039 China

A reasonable and effective high-precision thermal management system can alleviate the significant increase in energy consumption and decrease in range of pure electric vehicles under low-temperature conditions. This article establishes a subsystem model of the vehicle thermal management system based on common thermal management system architectures, including vehicle power system, battery motor, compressor, condenser, heat exchanger, chiller and heater models. A vehicle thermal management system under development was built using a subsystem model, and the effectiveness of the model was verified by comparing the collaborative simulation data in Amesim and Simulink with the actual vehicle validation data under low-temperature conditions. From both simulation and experimental data, it was found that the energy consumption of PTC exceeds 30% under low temperature conditions. Therefore, a model-based optimization study on vehicle thermal management was conducted to address the high energy consumption of PTC. The optimization results show that the ambient temperature threshold strategy has increased the driving range by 3.37% to 4.85% compared to the original strategy;The thermal management system architecture with a waste heat recovery motor has increased the range from 268.07 km to 285.14 km, an increase of 6.37%. Prove the effectiveness and universality of the model. © 2024, The Authors. All rights reserved.

关键词： Temperature control

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Optimization and Verification of Thermal Management System for Pure Electric Vehicle Based on Multi Task Model

SSRN

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SSRN 2023年

There are many subsystems that require thermal management for pure electric vehicles, and their thermal impact mechanism is complex. Establishing a multi task model and comprehensively optimizing the parameters and architecture of the vehicle thermal management system is of great significance for reducing energy consumption and increasing driving range of pure electric vehicles. Taking the opportunity to participate in the development of a pure electric vehicle in a certain company, Firstly, this article constructs a vehicle dynamics model and a component model of the vehicle thermal management system based on the actual vehicle parameters, vehicle dynamics theory, and bench test data. Using the national standard CLTC-P cycle working condition as the testing standard, the accuracy of the model is verified, and a control strategy for the thermal management system is designed to complete the dynamic model construction of the vehicle thermal management system;Secondly, based on GB/T 18386.1-2021, the energy flow and temperature data were analyzed and compared to verify that the constructed model has high accuracy in terms of energy consumption performance of the entire vehicle under low temperature conditions and temperature control of key components;Finally, the energy consumption problem of PTC heating the battery pack is optimized and simulated using the ambient temperature threshold strategy, as well as the thermal management architecture. The results show that under this strategy, the driving range of the entire vehicle has been improved in low-temperature economic tests;The optimized thermal management architecture is more energy-efficient. © 2023, The Authors. All rights reserved.

关键词： Temperature control

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Optimization and Verification of Thermal Management System for Pure Electric Vehicle Based on Multi Task Model

SSRN

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SSRN 2023年

关键词： Temperature control

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Biomass Ppower generation: A pathway to carbon neutrality

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Science of the Total Environment 2024年 933卷

作者： Du, Wei Cui, Zhanpeng Wang, Jinze Qin, Yiming Ye, Jianhuai Lin, Nan Chen, Yuanchen Duan, Wenyan Chang, Zhaofeng Li, Hao Zhang, Peng Pan, Bo Yunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control Faculty of Environmental Science & Engineering Kunming University of Science & Technology Kunming650500 China Key Laboratory for Earth Surface Processes College of Urban and Environmental Sciences Peking University Beijing100871 China School of Energy and Environment City University of Hong Kong SAR 999077 Hong Kong Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks School of Environmental Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Environmental Health School of Public Health Shanghai Jiao Tong University Shanghai200025 China Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province College of Environment Zhejiang University of Technology Zhejiang Hangzhou310032 China

In light of the pressing need to reduce carbon emissions, the biomass power generation industry has gained significant attention and has increasingly become a crucial focus in China. However, there are still considerable gaps in the historical background, status, and prospects of biomass power generation. Herein, the historical and current status of biomass power generation in China are systematically reviewed, with a particular emphasis on supportive policies, environmental impacts, and future projections. By 2022, the newly installed capacity for biomass power generation reached 3.34 MW with a total installed capacity of 41 MW. The power produced from biomass power generation is 182.4 billion kWh in China. The total installed capacity and generated power in 2022 were 1652 and 1139 folds higher than in 2006 when the first biomass generation plant was established. However, disparities in the distribution of biomass resources and power generation were observed. key drivers of the industry development include tax, finance, and subsidy policies. Under the implementation of the 14th Five-Year Plan for renewable energy development and the goal of carbon neutrality, biomass power generation may achieve great success through more targeted policy support and advanced technologies that reduce air pollutant emissions. If combined with Bioenergy with Carbon Capture and Storage (BECCS) technology, biomass power generation will make its contribution to carbon neutrality in China. © 2024 Elsevier B.V.

关键词： Greenhouse gases

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New Sensing Technologies for Monitoring Machinery,Structures,and Manufacturing Processes

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Journal of Dynamics, Monitoring and Diagnostics 2023年第2期2卷 69-88页

作者： JDMD Editorial Office Zhaoyan Fan Robert X.Gao Qingbo He Yi Huang Tianxi Jiang Zhike Peng Luc Thévenaz Yuyong Xiong Shuncong Zhong Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China Department of Mechanical Industrialand Manufacturing EngineeringOregon State UniversityCorvallisORUSA Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOHUSA State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouPeople’s Republic of China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhouPeople’s Republic of China School of Mechanical Engineering Ningxia UniversityYinchuanPeople’s Republic of China EPFL Ecole Polytechnique Fédérale de Lausanne Group for Fibre OpticsSCI-STI-LT Station 111015 LausanneSwitzerland

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing *** this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,*** on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

关键词： fiber optic sensor metamaterials sensing microwave sensing multiphysics sensing terahertz sensing

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