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engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 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 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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Comprehensive Anomaly Score Rank Based Unsupervised Sample Selection Method

SSRN

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SSRN 2024年

作者： He, Zhonghai Du, Yinzhi Feng, Zhanbo Zhang, Xiaofang School of Control Engineering Northeastern University at Qinhuangdao Hebei Province Qinhuangdao066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China School of Optics and Photonics Beijing Institute of Technology Beijing China

The process of selecting representative samples is crucial for establishing an accurate calibration model. To enhance the representativeness of the samples, a method for sample selection, utilizing the degree of anomaly as the evaluation criterion, is proposed. Initially, anomaly scores corresponding to various detection methods are obtained to ensure a comprehensive evaluation. These scores are then normalized by the confidence lower limit to establish a consistent scoring criterion. Subsequently, the weights of different detection methods are determined through eigenvector centrality analysis of a graph, where the methods serve as nodes and the similarity acts as weighted edges. Finally, the comprehensive anomaly scores are computed as the sum of weighted scores and are subsequently sorted. Representative samples are selected using a uniformly-spaced sampling approach, with the spacing determined by a predefined and provided sample number. The efficacy of the method is validated across different sample sets. © 2024, The Authors. All rights reserved.

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Retired Lithium-ion Batteries Screening Based on Partial Discharge Curves and an Improved Crossformer

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IEEE Transactions on Transportation Electrification 2025年

作者： Lin, Mingqiang Xu, Zijun Zheng, Gengfeng Meng, Jinhao Wang, Wei Wu, Ji Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of Matter Fujian Jinjiang362200 China Fujian Special Equipment Inspection and Research Institute Fuzhou350008 China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control China Xi’an Jiaotong University School of Electrical Engineering Xi’an710049 China Xi’an Jiaotong University School of Management Xi’an710049 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

With the increasing number of retired lithium-ion batteries from electric vehicles, effective screening and classification are necessary to enable their secondary utilization. The existing retired lithium-ion battery screening methods have limitations in accuracy and high energy consumption. This paper proposes a method based on local discharge curves and an improved Crossformer model to reduce energy consumption and improve screening accuracy. Considering the actual conditions of retired lithium-ion batteries, features are extracted from the discharging stage. Select the segments of the capacity sequence that exhibit the most significant variation with the voltage sequence and construct a partial capacity-voltage sequence. The standard deviation and Shannon entropy of the capacity sequence, as well as the variance, energy, and the maximum amplitude of the low-frequency part of the Fourier-transformed voltage sequence, are used as features. Then, the Crossformer utilizes a flexible embedding mechanism and a hierarchical structure for handling the dependencies of the feature. The Crossformer is improved for classification by adding a cross-attention layer at the final stage of the Crossformer decoder, which enhances its ability to capture the dependencies between features and improves the model’s performance in battery screening classification. Finally, the validation of 252 retired batteries shows that the method achieves a classification accuracy of 94.74%, outperforming other advanced models, including the original Crossformer, Transformer, ResNet, and MLP models. © 2015 IEEE.

关键词： Lithium-ion batteries

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UKF-Based Multi-sensor Data Processing Algorithm for SLAM

UKF-Based Multi-sensor Data Processing Algorithm for SLAM

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IEEE International Conference on control Science and Systems engineering (CCSSE)

作者： Kun Xu Boheng Zhang Jiangang Li Guizhong Wang Mingjian Sun School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Woan Technology (Shenzhen) Co. Ltd. Shenzhen China Hebei Key Laboratory of Grid Advanced Measurement and Monitor Jinyanhagong Industrial Technology Research Institute Zhangjiakou China School of Information Science and Engineering Harbin Institute of Technology Harbin China

Simultaneous localization and mapping (SLAM) algorithm is widely used in unmanned and robotic applications. SLAM usually uses sensor data, which are from the LiDAR, the inertial measurement unit (IMU) and the wheeled odometry, to build maps of the surrounding environment. However, the motion distortion of LiDAR data and the error accumulation of wheeled odometry can affect the accuracy of the front-end odometry of the SLAM algorithm, which can lead to serious errors in the maps created. Therefore, in order to solve the problem of poor map construction results of SLAM algorithm due to various types of sensor errors. we propose the traceless Kalman filtering based (UKF-based) multi-sensor data processing algorithm. Firstly, the proposed algorithm uses the UKF to fuse IMU and wheeled odometry data to reduce the cumulative error of wheeled odometry. And then the algorithm uses IMU and corrected wheeled odometry data and combines with the motion states of the robot to solve the motion distortion problem of LiDAR data. Finally, we verified the effectiveness of the proposed algorithm for wheeled odometry data correction in Gazebo simulation environment and real environment experiments, and compared the mapping effectiveness of SLAM by using the original data and the data processed by our algorithm. The experimental results show that the map construction effect of SLAM algorithm is significantly improved when using the sensor data processed by the algorithm. Experimental validation on the robot shows that the proposed algorithm has universal applicability and good compatibility.

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A Baseline Modeling and Anomaly Detection Method for Inertial Navigation System Based on Integrated 1DCNN

A Baseline Modeling and Anomaly Detection Method for Inertia...

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International Conference on Electronic measurement and Instruments, ICEMI

作者： Wenjing Mo Chuanjin Han Yuan Wang Chengdu Aircraft Industrial (Group) Co. Ltd. Chengdu China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

Flight test is an essential step before aircraft delivery. Safe flight test can not only reduce the cost of aircraft research, but also has great significance for the study of aircraft performance. In order to ensure the safety of flight test, it is necessary to implement anomaly detection for aircraft systems. Inertial navigation system, as a complex system can provide accurate position, velocity, and attitude information for aircraft during flight test. It is very important to monitor the state of inertial navigation system for ensuring the safety of flight test. However, traditional single anomaly detection models perform poorly on different test data and have poor generalization capabilities. In order to improve the generalization ability of the anomaly detection model with inertial navigation data, this paper proposes a prediction-based anomaly detection method based on an integrated one-dimensional convolutional neural network (1DCNN). Firstly, multiple training sets are divided based on the bootstrap method, and then integrated 1DCNN prediction models are built for corresponding training sets. Finally, a static threshold judgment method based on the PauTa criterion is used to detect whether any abnormality occurs. This paper verifies the effectiveness of the proposed method through experiments with real inertial navigation data. The results show that compared with the traditional 1DCNN method, the proposed method significantly improves the performance of anomaly detection and has good generalization ability on different test conditions. The TPR of anomaly data detection reaches more than 97%, which proves that the method has effective anomaly data detection for inertial data.

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Design and Implementation of Coal Mine Monitoring Networking System Based on Service-Oriented Architecture

Design and Implementation of Coal Mine Monitoring Networking...

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International Conference on Network and Information Systems for Computers (ICNISC)

作者： Yunqi Chen Jin Xu Measurement and Control Technology Research Institute China Coal Technology Engineering Group Chongqing Research Institute Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665453523

Coal is the most important basic energy supporting the development of the national economy, safe production and effective supervision and management are of great significance. Currently, the coal mine safety monitoring system can be monitored locally, but it cannot help the supervision department to realize the remote supervision and centralized supervision. Establishing a corresponding coal mine safety monitoring network system has become a urgent need of coal mine safety supervision. In the paper, we first study the service-oriented architecture(SOA), and describes the windows communication foundation(WCF) technology, the technology is a main way to achieve SOA architecture. Then combined with WCF technology, the function and logic structure of coal mine safety monitoring network system are designed. Finally, the network system of mine monitoring system based on SOA was realized in the background of a mine monitoring system networking project of a company.

关键词： Economics Companies Computer architecture Coal Safety Coal mining Service-oriented architecture

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Research on Methodology for Digital Twin Construction of Powertrain Systems Under Time-Varying Conditions

SSRN

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SSRN 2024年

作者： Zhang, Dingge Ma, Biao Yu, Liang Xu, Xiaoyu Wang, Liyong Zhang, Jinle School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Software Shandong University Shandong250101 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100192 China China North Vehicle Research Institute Beijing100072 China

The dynamic characteristic investigation of powertrain system under time-varying conditions is highly relied on the dynamic torque signals of each component. However, methods that are sufficiently stable and efficient for obtaining these torque signals have not yet been realized. Thus, a five-dimensional digital twin system, including mechanism, behavior, geometric, statistical, and self-learning models, is established as a virtual mapping of physical system. These models are used to define solution methods, quantify characteristics, visualize displays, statistical analysis, and predict states, respectively. Then, a digital twin monitoring platform is established based on the virtual-real mapping, which can visualize dynamic characteristics of each component based on MQTT protocol. Moreover, the twin signals are verified from the perspectives of dynamics and kinematics through bench and real vehicle experiments, where the relative errors are less than 10%. In addition to the dynamic speed-torque signals of each component, twin signals also include jerk and sliding friction work used to evaluate the shifting quality. Through specific examples, it is demonstrated that the operating state for powertrain system can be monitored and evaluated based on all these twin signals, thus providing sufficient data support for further decision-making and feedback. © 2024, The Authors. All rights reserved.

关键词： Mapping

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Phase transition and piezoelectric property of (Ag,K)NbO_(3) ceramics

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Journal of Materiomics 2022年第4期8卷 863-872页

作者： Lin Chen Mingyuan Zhao Jing Wang Suwei Zhang Fuhua Sun Lei Zhao Key Laboratory of High-precision Computation and Application of Quantum Field Theory of Hebei Province College Physics Science and TechnologyHebei UniversityBaoding071002China Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace EngineeringNanjing University of Aeronautics and AstronauticsNanjing210016China Center for Advanced Measurement Science National Institute of MetrologyBeijing100029China Institute for Advanced Materials Hubei Normal UniversityHuangshi435002China

AgNbO_(3) is an antiferroelectric (AFE) material with double hysteresis loop. Both the antiferroelectricityand ferroelectricity can be enhanced by doping. Herein, the ferroelectricity of AgNbO_(3) ceramics wasenhanced via K-doping and the phase diagram of the (Ag_(1-x)K_(x))NbO_(3) ceramics was upgraded. In details,(Ag_(1-x)K_(x))NbO_(3) ceramics are ferrielectric (FIE) M1 phase as x=5.00-5.50 mol% and ferroelectric (FE) Ophase as x=5.75-6.00 mol% before poling, and FE O phase as x=5.00-6.00 mol% after poling at roomtemperature. With increasing temperature, (Ag_(1-x)K_(x))NbO_(3) ceramics show the phase evolutions from FIEM1, AFE M2 to paraelectric (PE) T phase at x=5.00-5.50 mol% and from FE O, FE T to PE T phase at x=5.75-6.00 mol% before poling, and from FE O, FE T to PE T phase at x=5.00-6.00 mol% after *** d33 values of 180 pC/N and 285 pC/N are obtained at the FE O-FE T and FE T-PE T phase *** work sheds light on a novel and promising lead-free piezoelectric system.

关键词： (Ag_(1-x)K_(x))NbO_(3)ceramics Phase diagram Piezoelectric property

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Strip Weld Inspection Based on Improved VGG16

Strip Weld Inspection Based on Improved VGG16

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Sisi Fu Yuan Chen Dexin Xian Shiyuan Kong Hao Wang Jinsong Wang School of Optoelectronic Engineering Changchun University of Science and Technology Zhongshan Guangdong China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin Heilongjiang China School of Optoelectronic Engineering Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China School of Optoelectronic Engineering Changchun University of Science and Technology Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China

As one of the indispensable materials in modern industry, high precision and high efficiency defect detection of strip weld has become an important part of production and quality control. In response to the shortcomings of the existing strip weld detection technology with low detection accuracy, this paper proposes a deep learning based strip weld defect detection and classification technique. First, the CBAM attention mechanism is introduced into the network structure to improve the attention to important features; second, the traditional convolution layer is replaced by using the dilated convolution to reduce the computation while increasing the perceptual field; finally, the original ReLU activation function is replaced by the PReLU activation function to effectively overcome the problem of neuron death in the Relu activation function. The improved model finally achieves a recognition rate of 98.1% in the test set, which is 22.1% higher than that of the unimproved model, greatly improving the accuracy and robustness of the algorithm.

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A matching algorithm T-AKAZE for image recognition of hydroelectric equipment failure 4

A matching algorithm T-AKAZE for image recognition of hydroe...

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2021 4th International Conference on Computer Information Science and Application technology, CISAT 2021

作者： Xu, Jiayu Ji, Xiu Wang, Yan School of Electrical and Information Engineering Changchun Institute of Technology Jilin ChangchunJL431 China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun Institute of Technology Jilin ChangchunJL431 China

Aiming at the shortcomings of the current image-based fault identification algorithm for hydropower station equipment, such as low efficiency and poor accuracy, an improved image matching algorithm based on AKAZE, T-AKAZE was proposed. In this paper, AKAZE algorithm is firstly used to obtain image feature points. Then, the improved algorithm T-AKAZE is used for coarse matching of feature points. On the basis of Tanimoto coarse matching, RANSAC algorithm is used for fine matching to improve the algorithm accuracy. In order to verify the matching effect of the improved algorithm, ORB algorithm, AKAZE algorithm and ORB+Tanimoto algorithm were compared and analyzed. The experimental results show that the proposed algorithm has good robustness for image matching of different scales and similarity, and its timeliness also meets the application requirements, which provides convenience for the staff to identify the fault of hydropower station equipment. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Image matching

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