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4th International Conference on Information Science and Control Engineering (ICISCE)

作者： Long, Yi Hou, Xingzhe Sun, Hongliang Liu, Yongxiang Wang, Qian Li, Zhi Xu, Tingting Chongqing Elect Power Res Inst Chongqing 401123 Peoples R China

ISBN: (纸本)9781538630136

The electric vehicle parameters collected by time-sharing lease management platform have been accumulated increasingly, which has laid a firm foundation for large data analysis. Pretreatment technology plays an important role in the process of data acquisition. In particular, there is a risk of intervention in the process of transmitting monitoring data between two platforms. Therefore, this paper puts forward the prediction method of decision tree analysis based on kalman filter by adopting the intervention probability curve of monitoring data and the quantitative relationship between the covariance ratio, putting the intervention probability curve interval and measurements as input to establish a regression relationship, and adding the object decision tree into the traditional kalman filtering algorithm. This method can reduce the intervention effects, and achieve filtering and predicting function for the data collected by the main operating platform, which lays a solid technical support for big data analysis efficiently.

关键词： electric vehicle time-sharing leasing kalman filter algorithm intervention prediction

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Unmanned aerial vehicle positioning based on multi-sensor information fusion

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Geo-Spatial Information Science 2018年第4期21卷 302-310页

作者： Wenjun Li Zhaoyu Fu The 710th Research Institute of China Shipbuilding Industry Corporation YichangChina

Unmanned aerial vehicle(UAV)positioning is one of the key techniques in the field of UAV *** the high positioning precision of UAV can be achieved through global positioning system(GPS),the frequency of updating signal in GPS is low and the energy consumption of GPS module is huge,which does not satisfy the real-time demand of UAV *** this paper,a multi-sensor information fusion method based on GPS,inertial navigation system(INS),and the visible light sensors is proposed for UAV *** kalman filter combining with simulated annealing algorithm is used to estimate the position error between GPS or INS and the visible light sensors,and then the motion trajectory is corrected according to this position error ***,the positioning accuracy of UAV can be improved in case of only INS being *** results demonstrate that the proposed method can remarkably improve the positioning accuracy and greatly reduce the energy consumption.

关键词： kalman filter algorithm simulated annealing algorithm target tracking integrated positioning system

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UAV Localization Model Based on Multi-Dimensional Eigenvalue-kalman filter 6

UAV Localization Model Based on Multi-Dimensional Eigenvalue...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Tian, Mengxian Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798350305623

With the rapid development of science and technology, drone technology is gradually becoming an indispensable part of human life. With the increasing demand for complex tasks of UAVs from all walks of life, a single UAVs will not be able to effectively complete the task, and UAVs cluster gradually comes into people's perspective, and the resulting positioning of UAVs cluster has become the focus of current research. Azimuth-only passive positioning is a method that can adjust the position of UAVs and maintain formation. Several drones in the team send signals, and the rest of the drones passively receive signals to obtain direction information for positioning, and then adjust the position of the drones. This paper establishes kalman filter algorithm model based on multidimensional eigenvalues, path planning model based on Bayesian estimation, and uses triangulation positioning method, particle swarm optimization algorithm and other methods to optimize azimuthing-only passive positioning of UAVs in formation flight. This method effectively improves the accuracy of UAV positioning and plays an important role in solving practical problems. © 2023 IEEE.

关键词： Bayesian estimation path planning model kalman filter algorithm particle swarm optimization algorithm triangulation positioning method

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Design of weak current measurement system and research on temperature impact

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Nuclear Science and Techniques 2024年第4期35卷 46-56页

作者： Chu-Xiang Zhao San-Gang Li Rong-Rong Su Li Yang Ming-Zhe Liu Qing-Yue Xue Shan Liao Zhi Zhou Qing-Shan Tan Xian-Guo Tuo Yi Cheng College of Nuclear Technology and Automation Engineering Chengdu University of Technology1#DongsanluErxianqiaoChengdu 610059China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu University of Technology1#DongsanluErxianqiaoChengdu 610059China University of Science and Technology of China Hefei 230026China School of Physics and Electronic Engineering Sichuan University of Science and EngineeringZigong 643000China

A dedicated weak current measurement system was designed to measure the weak currents generated by the neutron ionization *** system incorporates a second-order low-pass filter circuit and the kalman filtering algorithm to effectively filter out noise and minimize interference in the measurement *** conducted under normal temperature conditions has demonstrated the system's high precision ***,it was observed that temperature variations can affect the measurement *** were collected across temperatures ranging from -20 to 70℃,and a temperature correction model was established through linear regression fitting to address this *** feasibility of the temperature correction model was confirmed at temperatures of -5 and 40℃,where relative errors remained below 0.1% after applying the temperature *** research indicates that the designed measurement system exhibits excellent temperature adaptability and high precision,making it particularly suitable for measuring weak currents.

关键词： Weak current measurement system Neutron ionization chamber kalman filter algorithm Temperature correction model

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Remaining lifetime prediction for nonlinear degradation device with random effect

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Journal of Systems Engineering and Electronics 2018年第5期29卷 1101-1110页

作者： CAI Zhongyi CHEN Yunxiang GUO Jiansheng ZHANG Qiang XIANG Huachun Equipment Management and Unmanned Aerial Vehicle Engineering College Air Force Engineering University Defense Technology Academy China Aerospace Science and Industry Corporation Limited

For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function(PDF) of the first hitting time(FHT)is deduced. A parameter estimation method based on modified expectation maximum(EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model(SSM) and the kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.

关键词： remaining lifetime(RL) prediction nonlinear degradation model Wiener process random coefficient kalman filter algorithm

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Predicting aircraft taxiing estimated time of arrival by cluster analysis

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第2期16卷 252-262页

作者： Tang, Xinmin Ji, Xiaoqi Liu, Jinan Nanjing Univ Aeronaut & Astronaut Civil Aviat Coll Nanjing 211100 Peoples R China

In order to predict the aircraft taxiing estimated time of arrival (ETA) to reduce scene conflicts and improve the operating efficiency of airports, the kalman filter algorithm is employed to preprocess historical trajectory data for an airport scene. For the first time, in order to measure the distance between trajectory samples, the aircraft taxiing period and the number of aircraft in a scene are used as sample features for aircraft taxiing ETA prediction. The dynamic time warping algorithm is employed to extract the different features of the trajectory. The Euclidean distance between two sample features is taken as their similarity. The initial clustering centre is determined based on the divided difference maximum principle by using the K-means algorithm to cluster samples and selecting the cluster with the highest matching degree according to the time period of the aircraft under planning and the number of aircraft operating in the scene. The trajectory sequence for the sample at the cluster centre and the static path planned by the tower are combined to predict the aircraft taxiing ETA. The accuracy of this method at predicting the aircraft taxiing ETA was demonstrated in simulations by comparing and analysing actual trajectory data with predicted taxiing ETA.

关键词： aircraft taxiing estimated time aircraft taxiing ETA prediction historical trajectory data actual trajectory data predicted taxiing ETA dynamic time warping algorithm path planning kalman filter algorithm cluster analysis sample features airports initial clustering centre time-of-arrival estimation kalman filters K-means algorithm cluster samples Euclidean distance aircraft airport scene difference maximum principle

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Inverse parameter identification of cohesive zone model for simulating mixed-mode crack propagation

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INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES 2014年第13期51卷 2400-2410页

作者： Xu, Yangjian Li, Xiangyu Wang, Xiaogui Liang, Lihua Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Zhejiang Peoples R China

Inverse analysis is widely applied to the identification of material properties or model parameters. In order to improve the computational efficiency of the inverse method based on the genetic algorithm, an interpolation scheme upon the response surface constructed by the finite element simulation has been adopted in this paper. Meanwhile, a gradual homogenization treatment scheme has also been presented to improve the convergence of the inverse method based on the kalman filter algorithm. Both methods are proven effective in dealing with the single-objective inverse problem. However, literature studies show that the adoption of multiple types of experimental information is useful to improve the accuracy of inverse analysis. In this case, it turns into a multiple-objective inverse problem. Our practice proved that the above-mentioned two methods might not yield a proper result if the sensitivity issue of different types of information is not considered. Therefore, another multi-objective inverse method, in combination of the above two optimization algorithms and a weight-estimating scheme that can consider such sensitivity, has been further presented. Finally, by using a mixed-mode crack propagation simulation and two types of experimental information (loading-displacement response curve and crack path profile), the parameters of the cohesive zone model were inversely identified and its simulation results are in good agreement with the experiment. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Cohesive zone model Inverse analysis kalman filter algorithm Genetic algorithm Weight-estimating scheme

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TRANSPUTER IMPLEMENTATION OF kalman filterS

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IEE PROCEEDINGS-D CONTROL THEORY AND APPLICATIONS 1991年第4期138卷 355-362页

作者： MAGUIRE, LP IRWIN, GW Department of Electrical and Electronic Engineering Queen's University of Belfast Belfast UK

The paper is concerned with transputer implementations of three forms of the kalman filter algorithm. Two approaches to a concurrent realisation are considered, involving heuristic partitioning of the conventional sequential algorithm and proposing a new strategy for mapping the systolic array descriptions onto parallel processors. Real-time implementation results on transputer networks are presented and the relative performances evaluated in terms of speed, parallel processing efficiency and numerical accuracy for a test system. This attempts not only to quantify the performances of parallel kalman filters but also to address the more general issues involved in mapping algorithms onto architectures.

关键词： kalman filter algorithm TRANSPUTER PARALLEL PROCESSING

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A load estimation model for axle box bearings of high-speed trains based on vehicle dynamic response

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 221卷

作者： Yang, Chen Wu, Xingwen Chi, Maoru Cai, Wubin Ma, Zikui Yao, Xuesong Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Schaeffler Trading Shanghai Co Ltd Shanghai 201804 Peoples R China

The axle box bearing is a critical component of high-speed train bogies, closely related to vehicle safety. Accurate bearing boundary load is crucial for bearing fatigue life assessment. This paper derives the dynamic mechanism equations of a novel axle box bearing-half vehicle system in detail and develops a bearing load estimation model for straight scenarios based on the augmented kalman filter algorithm and mechanism equations. Evaluation indices for the load estimation effectiveness are formulated by comparing estimated results with those calculated by a virtual test model. Subsequently, an improved niche genetic algorithm combined with the virtual mapping relationship is proposed to optimize and match key parameters of the estimation model. Validation and analysis of the load estimation model under different operating conditions are conducted. The results suggest that the estimated vertical, lateral, rolling, and yaw loads of the eight axle box bearings in the entire vehicle are consistent with those calculated by the virtual test model, with correlation coefficients above 0.9. The proposed estimation model has good applicability for identifying the service load of axle box bearings under different measured wheel and rail irregularities, carbody weights, and vehicle speeds, which has important implications for monitoring the boundary load of high-speed train axle box bearings.

关键词： Axle box bearing Load estimation Bearing-half vehicle system dynamics kalman filter algorithm Improved niche genetic algorithm High-speed train

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FORECASTING WITH INCOMPLETE DATA

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 1995年第5期24卷 1255-1269页

作者： LEE, CP NAT I LAN INST AGR & TECHNOL ILAN 260 TAIWAN

The Bayesian method is used to forecast with incomplete data. This involves the application of the kalman filter technique. It is shown that this is an appropriate procedure. Among many advantages, the kalman filter algorithm can generate forecasts with paucity of data, and it is possible to calculate the exact likelihood function involved. Several models are presented in their state space representations, and their uses in forecasting are discussed.

关键词： kalman filter algorithm STATE SPACE REPRESENTATION FORECASTING MISSING OBSERVATIONS BAYESIAN METHOD

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