检索结果-内蒙古大学图书馆

Chinese Automation Congress (CAC)

作者： Fuhao Shang Lelai Zhou Yibin Li Chen Zhang Center for Robotics School of Control Science and Engineering Shandong University Jinan China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China

Robots that have the ability to work in complex natural environments have broad application prospects. Focusing on the lack of autonomous navigation ability of the robot in the wild environment, this paper respectively proposes the improved localization and trajectory tracking methods. The multi-vision sensor fusion localization method is based on dual-factor graph optimization, which realizes the rapid processing of asynchronous sensor information. The dual-factor graph optimization can improve the localization accuracy and fault tolerance of the system under outdoor environments. Meanwhile, the multi-fine granularity MPC-based trajectory tracking method for unstructured terrain. Under the large-scale global path, a refined map is built around the robot. And the elevation difference is introduced to impose new constraints at the omni-directional velocity. The proposed methods in this study are implemented in actual environment. Experiments and simulations in representative terrain environments have verified the effectiveness of the improved methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Landing Area Prediction in Complex Terrains for Walking-assisted Lower-limb Exoskeleton Robot

Landing Area Prediction in Complex Terrains for Walking-assi...

引用

2023 International Conference on Biomimetic Intelligence and Robotics

作者： Liu, Yuexun Liu, Du-Xin Ma, Yue Guo, Sheng Wu, Xinyu Robotics Research Center School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

With the expansion of application scenarios, the walking-assisted lower-limb exoskeleton robot urgently needs to improve its walking ability in complex terrains. However, due to the lack of environmental understanding ability and decision-making ability, the exoskeleton can not meet the need of people with lower-limb disability in complex terrains. To improve the walking abillity, this paper proposes a method of predicting the landing area in complex terrains for walking-assisted lower-limb exoskeleton robot. Multiple sensors were used to obtain the terrain information, step length, and stride length of human walking, and a multi-type input convolutional network was built to establish the mapping relationship to learn the decision-making mechanism of human standing in a complex environment. The result shows that the established network effectively predicts the landing area. Under the condition of full training, the predicted position deviation of the landing area is controlled within 6%, and the prediction rationality of the landing area can reach 90%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Simultaneous enhancement of sensitivity, resonant frequency, and overload capability of piezoresistive accelerometers by heavily-doped fillet structures on sensing beams

引用

IEEE Sensors Journal 2025年第11期25卷 19138-19146页

作者： Xia, Yong Wang, Tao Lv, Yang Han, Xiangguang Yu, Mingzhi Tan, Renjie Huang, Mimi Jia, Chen Zhao, Libo Sun, Lin Yang, Ping Lu, Dejiang Xi'an Jiaotong University School of Mechanical Engineering State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi'an710049 China Ningbo CRRC Times Transducer Technology Co. LTD Department of Basic Research and Development Ningbo315021 China Research Institute for Intelligent Sensing Technology and System School of Instrument Science and Technology State Key Laboratory for Manufacturing Systems Engineering State Industry-Education Integration Center for Medical Innovations International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi'an Jiaotong University Xi'an710049 China Shandong Laboratory of Advanced Materials and green Manufacturing at Yantai Yantai264000 China

High sensitivity, high natural frequency, and high overload capability are desired for accelerometers used in industrial applications, but simultaneously enhancing all is challenging. Here, via designing novel heavily-doped fillet structures, we prove the sensitivity, resonant frequency, and overload capability can be simultaneously enhanced. The enhancements are attributed to regulations of stress concentration and effective sensing regions by the heavily-doped fillets. The sensors with and without fillets are designed, fabricated, packaged, and tested. The results show a sensitivity of 0.31±0.02 mV/g@5V, a natural frequency of 37.87±2.09 kHz, and an overload of 10040.17±531.76 g for sensors with 10-μm fillets, which are superior to 0.27±0.01 mV/g@5V, 34.13±0.90 kHz, and 7368.55±843.92 g for those no-fillet counterparts. Additionally, the developed sensor further exhibited feasibility in air-bearing vibration monitoring. The proposed design is envisioned to give insights into the novel designs of high-performance stress-based sensors, including accelerometers, pressure sensors, and strain gauges. © 2001-2012 IEEE.

关键词： Strain gages

来源：评论

学校读者我要写书评

暂无评论

Highly sensitive piezoresistive and thermally responsive fibrous networks from the in situ growth of PEDOT on MWCNT-decorated electrospun PU fibers for pressure and temperature sensing

引用

Microsystems & Nanoengineering 2023年第5期9卷 73-85页

作者： Yunyun Luo Libo Zhao Guoxi Luo Linxi Dong Yong Xia Min Li Ziping Li Kaifei Wang Ryutaro Maeda Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemXi’an Jiaotong UniversityXi’anChina School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing YantaiChina Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems College of Electronics and InformationHangzhou Dianzi UniversityHangzhouChina Department of Emergency The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina

Flexible electronics have demonstrated various strategies to enhance the sensory ability for tactile perception and wearable physiological *** microstructures have attracted much interest because of their excellent mechanical properties and ***,a structurally robust fibrous mat was first fabricated by electrospinning,followed by a sequential process of functionalization utilizing ultrasonication treatment and in situ polymerization *** polyurethane(PU)microfibers were anchored with multi-walled carbon nanotubes(MWCNTs)to form conductive paths along each fiber by a scalable ultrasonic cavitation treatment in an MWCNT ***,a layer of poly(3,4-ethylene dioxythiophene)(PEDOT)was grown on the surface of PU fibers decorated with MWCNTs to enhance the conductive conjunctions of *** to the superior electromechanical behaviors and mechanical reinforcement of PEDOT,the PEDOT/MWCNT@PU mat-based device exhibits a wide working range(0–70 kPa),high sensitivity(1.6 kPa−1),and good mechanical robustness(over 18,000 cycles).The PEDOT/MWCNT@PU mat-based sensor also demonstrates a good linear response to different temperature variations because of the thermoelectricity of the PEDOT/MWCNT *** novel strategy for the fabrication of multifunctional fibrous mats provides a promising opportunity for future applications for high-performance wearable devices.

关键词： fibers PEDOT conductive

来源：评论

学校读者我要写书评

暂无评论

Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

引用

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

来源：评论

学校读者我要写书评

暂无评论

Tracking performance limitations of networked control systems with packet dropouts and logarithmic quantization 41

Tracking performance limitations of networked control system...

引用

第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

来源：评论

学校读者我要写书评

暂无评论

Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

A bionic approach for the mechanical and electrical decoupling of an MEMS capacitive sensor in ultralow force measurement

引用

Frontiers of Mechanical engineering 2023年第2期18卷 297-317页

作者： Wendi GAO Bian TIAN Cunlang LIU Yingbiao MI Chen JIA Libo ZHAO Tao LIU Nan ZHU Ping YANG Qijing LIN Zhuangde JIANG Dong SUN State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265599China State Key Laboratory of Robotics and Systems(HIT) Harbin 150006China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Department of Biomedical Engineering City University of Hong KongHong KongChina

Capacitive sensors are efficient tools for biophysical force measurement,which is essential for the exploration of cellular ***,attention has been rarely given on the influences of external mechanical and internal electrical interferences on capacitive *** this work,a bionic swallow structure design norm was developed for mechanical decoupling,and the influences of structural parameters on mechanical behavior were fully analyzed and optimized.A bionic feather comb distribution strategy and a portable readout circuit were proposed for eliminating electrostatic *** instability was evaluated,and electrostatic decoupling performance was verified on the basis of a novel measurement method utilizing four complementary comb arrays and applicationspecific integrated circuit *** electrostatic pulling experiment showed that the bionic swallow structure hardly moved by 0.770 nm,and the measurement error was less than 0.009% for the area-variant sensor and 1.118% for the gap-variant sensor,which can be easily compensated in *** proposed sensor also exhibited high resistance against electrostatic rotation,and the resulting measurement error dropped below 0.751%.The rotation interferences were less than 0.330 nm and(1.829×10^(-7))°,which were 35 times smaller than those of the traditional differential *** on the proposed bionic decoupling method,the fabricated sensor exhibited overwhelming capacitive sensitivity values of 7.078 and 1.473 pF/μm for gap-variant and area-variant devices,respectively,which were the highest among the current *** immunity to mechanical disturbances was maintained simultaneously,i.e.,less than 0.369% and 0.058% of the sensor outputs for the gap-variant and area-variant devices,respectively,indicating its great performance improvements over existing devices and feasibility in ultralow biomedical force measurement.

关键词： micro-electro-mechanical system capacitive sensor bionics operation instability mechanical and electrical decoupling biomedical force measurement

来源：评论

学校读者我要写书评

暂无评论

An Approach to the Extraction of Intersecting Pipes Weld Seam Based on 3D Point Cloud 11

An Approach to the Extraction of Intersecting Pipes Weld Sea...

引用

11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Yang, Shuai Shi, Xiaorui Tian, Xincheng Liu, Yan Center for Robotics School of Control Science and Engineering Shandong University Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China SINOTRUK Jinan Power Co. Ltd. Jinan250220 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a method based on point cloud for weld seam extraction of intersecting pipes workpiece is proposed. Firstly, a lightweight clustering algorithm based on the normal vector direction was proposed to extract the feature points of the weld seam. After filtering the feature points, a novel key points extraction algorithm was proposed to extract the key points of the weld seam. After that, a non-Uniform Rational B-Spline curve was used to approximate the key points and the preliminary spatial weld curve was obtained. Finally, an iterative algorithm was adopted to optimize the generated weld curve. The proposed method is verified on the ideal and real intersecting pipes point cloud models. Experiments show that the proposed method has high accuracy and robustness, and can accurately extract the intersecting pipes weld seam curve, providing guidance for the subsequent accurate automatic welding. © 2022 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：