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IEEE International Conference on Information and Automation (ICIA)

作者： Bai, Dianchun Liu, Zhicheng Hu, Qing Yang, Junyou Yang, Guang Ni, Changlin Yang, Deguo Zhou, Long Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Provin Peoples R China

ISBN: (纸本)9781509041022

Nowadays, electric wheelchairs on the market mostly were controlled by joystick which required users to operate by hands flexibly. But this wheelchair cound not be used by patients with hand disability or paraplegics. This paper designs an electric wheelchair controlled by eye movements. An eye tracker of Tobii is connected with a electric wheelchair. The eye tracker of Tobii is used to convert eye movement signals into coordinate of gazing points. But these data of gazing points are too diverging to be used directly. kalman filter algorithm is used to filter data of coordinate and obtain optimal data. By discriminating eye movement-controlled panel area where gazing points is located in, a computer sends appropriate signal to the wheelchair, so as to achieve the goal of moving wheelchair. Thus patients are no longer using hands to control electric wheelchairs.

关键词： eye tracker eye-controlled electric wheelchair kalman filter algorithm

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Research on Beam Tracking algorithm Based on kalman filter 22

Research on Beam Tracking Algorithm Based on Kalman Filter

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Proceedings of the 2022 5th International Conference on Telecommunications and Communication Engineering

作者： Yuhang Tang Wei Liu College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9781450397797

When using beamforming technology, it is often necessary to accurately determine the user's location. Aiming at the problem of low accuracy when using single base station to determine target location in remote areas at present. Based on the kalman filtering algorithm combined with the state estimation fusion method, this paper successfully improves the positioning effect. First, three kinds of motion scenes are designed and established: uniform linear motion scene, uniform acceleration linear scene, and high mobility scene. Then, the prediction equation and observation equation are respectively established for the three scenarios. At this time, two observation equations are established, and then the state estimation fusion method is used to improve the tracking accuracy. The effectiveness of this method is verified by experimental simulation.

关键词： Beamforming State fusion estimation kalman filter algorithm Motion model

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kalman filter spot centroid detection algorithm in optical wireless communication

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OPTICAL ENGINEERING 2022年第5期61卷 056104-056104页

作者： Yang, Shangjun Ke, Xizheng Sun, Dengke Liang, Jingyuan Zhang, Yuhao Xian Univ Technol Fac Automat & Informat Engn Xian Peoples R China Shaanxi Civil Mil Integrat Key Lab Intelligence Xian Peoples R China Shaanxi Univ Technol Coll Phys & Elect Hanzhong Peoples R China

Atmospheric turbulence creates a light spot on the focal plane of the receiving end scintillation and angle of arrival fluctuation in optical wireless communication. The combination of a spot centroid detection algorithm and kalman filter (KF) is proposed for spot tracking. The theoretical calculated value of the spot circle fit is filtered to obtain an optimal estimate of the spot center. The root mean square error of the detected spot centroids is used as an evaluation metric to verify the conventional algorithm and KF algorithm for the real-time tracking of the laser spot at transmission distances of 1.3, 4.1, and 10.3 km. The results show that the root mean square error of the spot in the pitch direction decreases from 0.740, 2.537, and 7.256 pixels to 0.683, 1.792, and 4.756 pixels, respectively, whereas the root mean square error of the spot in the horizontal direction decreases from 0.481, 2.131, and 5.932 pixels to 0.463, 1.471, and 4.362 pixels, respectively, compared with the conventional algorithm at three transmission distances. The envelope fluctuation of the baseband signal at the receiving end is significantly improved using KF algorithm compared with the conventional algorithm, which improves the stability of the optical wireless communication link. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： optical wireless communication atmospheric turbulence spot detection kalman filter algorithm

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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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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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Robust maximum correlation entropy kalman filtering algorithm based on S-functions under impulse noise

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 113-127页

作者： Huo, Yuanlian Yang, Kuojian Qi, Yongfeng Xu, Tianci Northwest Normal Univ Coll Phys & Elect Engn 967 Anning East Rd Lanzhou 730070 Gansu Peoples R China Northwest Normal Univ Coll Comp Sci & Engn 967 Anning East Rd Lanzhou 730070 Gansu Peoples R China

The impulse noise is heavy-tailed and distributed, which is common in many real-world scenarios of automatic control and target tracking. In order to improve the stability of kalman filtering algorithms in the context of impulse noise, a number of kalman filtering algorithms have been widely developed using the maximum correlation entropy instead of the minimum mean square error criterion. To better adapt to impulse noise environments. In this paper, we propose a new kalman filtering algorithm called robust maximum correlation entropy kalman filtering algorithm based on S-functions (HWSKF). The algorithm is based on a new cost function framework. On the one hand, this framework employs the properties of the Softplus function fused with the maximum correlation entropy to ensure that the algorithm is better adapted to the impulsive noise environment with faster convergence speed and stronger steady-state properties. On the other hand, the introduction of Huber regression weighting can effectively exclude the effect of outliers in the case of non-Gaussian noise. Under different impulse noise environments, we demonstrate through Monte Carlo simulation experiments that the algorithm proposed in this paper possesses high stationarity compared with the classical kalman filtering algorithm (KF), the maximum correlation entropy kalman filtering algorithm (MCKF), and the maximum correlation entropy kalman filtering algorithm based on the student's kernel (STTKF).

关键词： kalman filter algorithm Huber regression Impulse noise Softplus function Robust estimation

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Crop Yield Prediction Using Improved Extreme Learning Machine

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COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 2023年第1期54卷 1-21页

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh Chandra Amity Univ Comp Sci & Engn Noida Uttar Pradesh India Natl Inst Technol Agartala Comp Sci & Engn Agartala India

Crop yield prediction (CYP) is a critical challenge for decision-makers of any kind of levels, including regional and national decision-making. Farmers might use effective CYP model to help them determine what to cultivate or when to grow it. The primary aim of precision agriculture (PA) is to increase crop growth and yield thereby decreasing production costs and emissions. Various developmental factors influence potential yield, including soil properties, irrigation, weather, fertilizer maintenance, and topography. CYP is critical to worldwide agricultural production. To improve the global food security, policymakers depend on reliable forecasts to make timely exports and imports decision. The main aim of this paper is to predict the crop yield using machine-learning technique. The collected data are pre-processed using kalman filter algorithm and then certain features are extracted using the Linear Discriminant Analysis (LDA). The CYP is done using the improved extreme learning machine (IELM). The output weight of the extreme learning machine is improved using the chimp optimization algorithm (COA). The implementation tool used in this method is PYTHON and the dataset used for the CYP is farm yield prediction. The experimental results showed an accuracy of 99.99% which is better when compared to existing methods.

关键词： kalman filter algorithm linear discriminant analysis improved extreme learning machine CYP chimp optimization algorithm

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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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Dynamic modeling of biotechnology adoption with individual versus social learning: An application to US corn farmers

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AGRIBUSINESS 2023年第1期39卷 148-166页

作者： Yoo, Do-il Chavas, Jean-Paul Seoul Natl Univ Dept Agr Econ & Rural Dev 200-82271 Gwanak Ro Seoul 08826 South Korea Univ Wisconsin Dept Agr & Appl Econ Madison WI USA

Relative roles of individual versus social learning on biotechnology adoption are investigated with an empirical focus on the adoption of Genetically Modified (GM) corn in the US Corn Belt. Relying on a kalman filter algorithm, the unobservable learning process is parameterized in a dynamic programming problem, and parameters are estimated using a minimum-distance estimator. Estimates show that farmers are risk-averse and that both individual and social learning affect GM technology adoption with more importance on individual learning than social learning, whose statistical significances are confirmed by hypothesis testing. Sensitivity analysis results show that social learning contributes to lower GM adoption rates, reflecting a strategic delay in the presence of information externalities in the early and middle diffusion stages. [EconLit Citations: C61, D83, O33].

关键词： biotechnology adoption dynamic programming individual versus social learning information externalities kalman filter algorithm

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Output Current Identification Based on the kalman filtering algorithm for Magnetic-Coupling Excitation System 1

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International Conference on Wireless Power Transfer (ICWPT)

作者： Huang, Dongxiao Lin, Xianhong Hong, Zequan Yu, Xinhong Wang, Fengxiang Chinese Acad Sci Hai Xi Inst Quanzhou Inst Equipment Mfg Natl Local Joint Engn Res Ctr Elect Drives & Powe Quanzhou 362000 Peoples R China Fuzhou Univ Fuzhou 350108 Peoples R China Fujian Univ Technol Fujian Key Lab Intelligent Machining Technol & Eq Fuzhou 350118 Peoples R China

ISBN: (数字)9789819708734

ISBN: (纸本)9789819708758;9789819708734;9789819708727

Magnetic-coupling excitation system is one of the research highlights in the field of brushless Electrically Excited Synchronous Motor (EESM), which contains the primary energy emission device and a secondary receiving device that rotates synchronously with the motor rotor. Primary-side control estimation is widely applied in Wireless Power Transmission (WPT) system to detect the output current of the secondary device to reduce the weight of rotor and achieve the merit of communication-free. A magnetizing current estimation method based on the kalman filter algorithm is proposed for a novel system combined with EESM and WPT. The proposed method adopts the kalman filter algorithm to the magnetizing current estimation model, which eliminates the higher-order harmonics in the sampling of inverter current and transmitting coil current. In addition, the output current control is achieved by applying the PI controller. The simulation result of proposed system in Matlab/Simulink proves the enhancement of proposed method on the accuracy, the transient response speed and the tracking performance of magnetizing current estimation.

关键词： Wireless Power Transmission Electrically Excited Synchronous Motor Magnetizing Current Estimation kalman filter algorithm

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