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IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Renjie Tan Juan Yang Libo Zhao Xiangguang Han Wei Li Yong Xia Yi Gao Chengying Wang Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies School of Mechanical Engineering Xi’an Jiaotong University Xi’an China China Xi’an Satellite Control Center Xi’an China State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies School of Instrument Science and Technology Xi’an Jiaotong University Xi’an China Xi’an Aerospace Propulsion Institute Xi’an China

ISBN: (数字)9798350359831

ISBN: (纸本)9798350359848

This paper establishes theoretical circuit and electrostatic current multi-physical field simulation models for a typical comb capacitor fabricated with silicon-on-insulation. The influence of related electrical parameters and conditions on equivalent capacitance is revealed by analytical solution and simulation, and several methods are proposed to suppress the interference of parasitic capacitance on ideal capacitance, such as high-resistivity substrate Si, relatively high-frequency excitation, and grounded substrate Si. The models and methods proposed in this paper can provide theoretical guidance for the design and suppression of parasitic capacitance of MEMS (Micro-Electro-Mechanical System) capacitive sensors.

关键词： Micromechanical devices Analytical models Capacitors Silicon Software Nanoscale devices Integrated circuit modeling

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measurement Method of Key Parameters of Automobile Connector Based on Binocular Stereo Vision

Measurement Method of Key Parameters of Automobile Connector...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE International Conference on

作者： Guangze Bai Jun Gu Yan Xing Guiyan Xue Xu Guo Wanze Song Congbo Luo Yu Gu Lai Wei Xue Liu Automotive Institute Changchun Vocational and Technical College Changchun China Changchun Jielin Intelligent Technology Co. Ltd Changchun China Changchun City Construction Engineering School Changchun China Changchun Automotive Technical College Changchun China Xinhe Measurement and Control Technology (Changchun) Co. Ltd Changchun China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

As a kind of interconnection device widely used in automobile, the quality and reliability of automobile connector directly affect the overall safety of automobile. As a key parameter of connectors, pin spacing has important effects on electrical characteristics and durability of connectors. Therefore, a method for measuring pin spacing of automotive connectors based on binocular stereo vision is proposed in this paper. Firstly, a visual measurement system is designed in this paper, which provides hardware support for pin detection and pin spacing measurement of the connectors. After that, this paper uses the image processing method to detect the pin of the car connector, mainly carries out the pre-processing of the car connector image and edge feature extraction, and on the basis of the edge feature extraction, the corner feature detection is carried out, and finally the pin detection of the car connector is completed. Finally, according to the detection results of the car connector pins, the paper adopts binocular stereo vision method to measure the pin spacing of the connector pins, and the final measurement accuracy reaches millimeter level.

关键词： Connectors Visualization Image edge detection Feature extraction Pins Stereo vision Sensors Safety Automobiles Reliability

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Research on detection and measurement method of automotive connector terminal based on improved YOLOv7

Research on detection and measurement method of automotive c...

引用

Electronic technology, Communication and Information (ICETCI), IEEE International Conference on

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

As a vital part of the automotive electrical system, the connector pins support the transmission of electrical signals, data and power to ensure the correct connection and collaboration between the various components of the vehicle, and their quality and reliability are crucial to the performance, safety and reliability of the vehicle. Therefore, it is of great significance to study an automatic detection method for the connection pins of automobile connectors. The traditional connector needle detection mainly relies on manual method, due to the relatively dense distribution of the connector needle and the human eye is easy to fatigue and other factors. This method has the problems of high detection error rate, low accuracy and low efficiency. Therefore, this paper proposes an automatic detection method for connecting pins of automotive connector based on improved Yolov7 target detection. This method adds attention mechanism on the basis of Yolov7 target detection, which can be more suitable for small target detection. Finally, after the improvement, the results of the YOLOV7 target detection model training, mAP@0.5, reached 94.87%, and map@0.5:0.95, reached 75.28%. compared with the results before the improvement, mAP@0.5, 69.7%, map@0.5:0.95, 51.2%. Increases of 25.17% and 24.08%, respectively. It is proved that the improved model is more suitable for the detection of the connection pin of the auto-mobile connector. The detection rate of the final car connector pin is as follows: After the car connector pin is detected, the pin spacing is measured by the method of image processing in this paper, and the measurement accuracy meets the practical requirements. At the same time, the research content of this paper has laid the groundwork for other electrical and signal tests of automobile connector in the future.

关键词： Connectors Training Accuracy Object detection Needles Pins Safety

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The Meta Distribution of SINR in UAV-Assisted Cellular Networks

arXiv

引用

arXiv 2023年

作者： Shi, Minwei Yang, Kai Niyato, Dusit Yuan, Hang Zhou, He Xu, Zhan The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

Mounting compact and lightweight base stations on unmanned aerial vehicles (UAVs) is a cost-effective and flexible solution to provide seamless coverage on the existing terrestrial networks. While the coverage probability in UAV-assisted cellular networks has been widely investigated, it provides only the first-order statistic of signal-to-interference-plus-noise ratio (SINR). In this paper, to analyze high-order statistics of SINR and characterize the disparity among individual links, we provide a meta distribution (MD)-based analytical framework for UAV-assisted cellular networks, in which the probabilistic line-of-sight channel and realistic antenna pattern are taken into account for air-to-ground transmissions. To accurately characterize the interference from UAVs, we relax the widely applied uniform off-boresight angle (OBA) assumption and derive the exact distribution of OBA. Using stochastic geometry, for both steerable and vertical antenna scenarios, we obtain mathematical expressions for the moments of condition success probability, the SINR MD, and the mean local delay. Moreover, we study the asymptotic behavior of the moments as network density approaches infinity. Numerical results validate the tightness of the theoretical results and show that the uniform OBA assumption underestimates the network performance, especially in the regime of moderate altitude of UAV. We also show that when UAVs are equipped with steerable antennas, the network coverage and user fairness can be optimized simultaneously by carefully adjusting the UAV parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Directional patterns (antenna)

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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UAV-Assisted IoT Data Collection Scheme for IRS-Enabled Backscatter Communication Networks

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Han, Shuai Wang, Jinming Xu, Sai Wang, Shiji Li, Cheng Harbin Institute of Technology School of Electronics and Information Engineering China 5GIC and 6GIC GuildfordGU2 7XH United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Beijing Aerospace Measurement and control Technology CO.LTD Beijing China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada

Data collection schemes assisted by unmanned aerial vehicle (UAV) play a key role in Internet-of-Things (IoT) networks. Nonetheless, the massive IoT devices bring new challenges, especially the high energy consumption. The backscatter communication (BackCom) technique can utilize ambient signals for passive transmission, thus being applicable to IoT systems. Due to its numerous reflecting elements, Intelligent Reflecting Surface (IRS) can accomplish backscatter with high beamforming gain. Thus, IRS is suitable to alleviate the double-fading effect, which significantly restrains the development of BackCom networks. This paper utilizes the IRS-based BackCom technique to achieve efficient IoT data collection with the help of a UAV. The UAV provides carriers for multiple IRSs, which can emit data through backscatter. Then, the UAV collects the backward data dynamically. To maximize the collected data, the UAV's transmit power, trajectory, and the IRS's beamformers are jointly optimized. The optimization is realized through an alternative algorithm based on block coordinate descent (BCD) and reinforcement learning (RL). The Lagrangian dual transformation and semi-definite relaxation (SDR) approaches are employed to address the non-convexity of the problem effectively. Finally, simulation results demonstrated the effectiveness and feasibility of the proposed optimization scheme in the introduced system model. © 2017 IEEE.

关键词： Reinforcement learning

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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Huaxin Gu Shuaiqi Liu Feihong Yu Deyu Xu Xingwei Chen Liyang Shao Yu Wu Haifeng Zhang College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Advanced Measurement and Control and Equipment R&D Center Research Institute of Tsinghua University Chengdu China

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events.

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Femtosecond Laser-Inscribed Polarization-Maintaining Fiber Bragg Grating Using Line-by-Line Technique for Generating Wavelength-Switchable Erbium-Doped Fiber Lasers

SSRN

引用

SSRN 2023年

作者： He, Wei Hu, Jun Liu, Haochong Ma, Siqi Zhu, Lianqing Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China Beijing Laboratory of Optical Fiber Sensing and Systems Beijing Information Science and Technology University Beijing100192 China Beijing Institute of Control Engineering Beijing100190 China

In this study, a polarization-maintaining fiber Bragg grating (PM-FBG) was inscribed by a femtosecond laser using a line-by-line method and used as a wavelength selector for a wavelength-switchable erbium-doped fiber laser (EDFL). The PM-FBG period was 1.075 μm, and the grating length was 3200 μm. For the proposed EDFL, the lasing threshold was 64 mW, and switchable lasing emissions with stable single and dual wavelengths were realized by adjusting a polarization controller. For the experiments, different single-wavelength switchable lasers with scope values of 1563.34–1564.02 nm were generated, six dual-wavelength lasers were obtained, and a triple-wavelength laser output was realized. The power fluctuations of the single- and dual-wavelength lasers were lower than 0.331 and 0.682 dB, respectively, over a 25 min monitoring time. © 2023, The Authors. All rights reserved.

关键词： Femtosecond lasers

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Logging-while-drilling formation dip interpretation based on long short-term memory

引用

Petroleum Exploration and Development 2021年第4期48卷 978-986页

作者： SUN Qifeng LI Na DUAN Youxiang LI Hongqiang TANG Haiquan College of Computer Science and Technology in China University of Petroleum Qingdao 266580China Drilling Technology Research Institute of Shengli Petroleum Engineering Corporation Limited SinopecDongying 257000China Measurement and Control Technology Research Institute of Shengli Petroleum Engineering Corporation Limited.Dongying 257000 China

Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is *** study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data *** examining formation response characteristics of azimuth gamma ray(GR)curve,the preliminary formation change position is detected based on wavelet transform modulus maxima(WTMM)method,then the dynamic threshold is determined,and a set of contour points describing the formation boundary is *** classification recognition model based on the long short-term memory(LSTM)is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation ***,relative dip angle is calculated by nonlinear least square *** of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes,improve the accuracy of formation dip interpretation,and meet the needs of real-time LWD geosteering.

关键词： logging while drilling azimuth gamma stratigraphic identification artificial intelligence long short-term memory wavelet transform

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A Method for Recognition of Mixed Gas Composition Based on PCA and KNN 19

A Method for Recognition of Mixed Gas Composition Based on P...

引用

19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Xia, Wanyu Song, Tingting Yan, Zhanwei Song, Kai Chen, Deyun Chen, Yinsheng National Experimental Teaching Demonstration Center for Measurement and Control Technology and Instrumentation Harbin University of Science and Technology Harbin150080 China Postdoctoral Research Station of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9781665424462

In the mining process of coal, oil, and natural gas, there are often a large number of toxic, hazardous and explosive gases, which pose a greater safety hazard. These gases usually exist in the form of mixtures. Accurately identifying the composition of the mixed gas is of great significance to the prevention of safety accidents. In order to improve the accuracy of the component recognition of the mixed gas, this paper proposes a mixed gas component recognition algorithm based on the combination of principal component analysis (PCA) and k-nearest neighbor algorithm (KNN). PCA is used to extract the characteristics of the sensor array signal to obtain the characteristic value of the gas, and then KNN is used to realize the recognition of the gas type. The results show that the recognition rate of the feature quantity after dimensionality reduction as input is higher than that before dimensionality reduction. Finally, PCA and KNN algorithm and PCA and support vector machine (SVM) algorithm are compared for the recognition rate of mixed gas. Experimental results show that the proposed method has a recognition rate of 96.88% for mixed gas components. © 2021 IEEE.

关键词： Support vector machines

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