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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Yang, Jin Cui, Xuerong Li, Juan Li, Shibao Liu, Jianhang Chen, Haihua China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

The standard particle filter (PF) algorithm has the issue of particle diversity loss caused by particle degradation and resampling, which makes it impossible for particle samples to accurately represent the true distribution of state probability density function. particle swarm optimization (PSO) algorithm can effectively improve the particle degradation problem of particle filter namely, PSO-PF, but its fitness function is greatly affected by the variance of measurement noise, and is easy to fall into local optimal, which greatly limits the filtering accuracy. Therefore, this paper proposes an algorithm that combines genetic algorithm (GA) and PSO algorithm to improve particle filtering, namely, GA-PSO-PF. This algorithm combines the fast convergence speed of particle swarm optimization with the strong global searching ability of genetic algorithm to increase the diversity of particles while ensuring the effectiveness of superior particles, and improve the speed and accuracy of finding the optimal solution. Experimental results show that the filtering performance of the proposed algorithm is better than PF and PSO-PF, and the positioning and tracking accuracy is improved by 54.44% compared with PF and 27.20% compared with PSO-PF. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.

关键词： particle filter algorithm particle swarm optimization genetic algorithm target tracking and location

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Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved particle filter algorithm

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IEEE ACCESS 2023年 11卷 3062-3079页

作者： Zhang, Tianyu Wang, Qingfeng Shu, Yue Xiao, Wang Ma, Wensheng Beijing Univ Chem Technol Beijing Key Lab Hlth Monitoring & Selfrecovery Hi Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Mech & Elect Engn Beijing 100029 Peoples R China Hefei Gen Machinery Res Inst Co Ltd Hefei 230031 Anhui Peoples R China Western Branch Natl Pipe Network Grp United Pipel Urumqi Xinjiang Peoples R China

Due to the importance of bearings in modern machinery, the prediction of the remaining useful life (RUL) of rolling bearings has been widely studied. When predicting the RUL of rolling bearings in engineering practice, the RUL is usually predicted based on historical data, and as the historical data increases, the prediction results should be more accurate. However, the existing methods usually have the shortcomings of low prediction accuracy, large cumulative error and failure to dynamically give prediction results with the increase of historical data, which are not suitable for engineering *** address the above problems, a novel RUL prediction method is proposed. The proposed method consists of 3 parts: First, the multi-scale entropy-based feature - namely "average multi-scale morphological gradient power spectral information entropy (AMMGPSIE) " - from the rolling bearings as the Health indicator (HI) is extracted to ensure all the fault-related information is well-included;Then, the HI is processed with the enhanced Hodrick Prescott trend-filtering with boundary lines (HPTF-BL) to ensure good performance and small fluctuation on the HI;Finally, the deterioration curve is predicted using an LSTM neural network and the improved particle filter algorithm that we proposed. The proposed method is validated using the experimental bearing degradation dataset and the casing data of a centrifugal pump bearing from an actual industrial site. Comparing the results with other recent RUL prediction methods, the proposed method achieved state-of-the-art feasibility and effectiveness, conform to the needs of practical application of the project.

关键词： Entropy-based features particle filter algorithm rolling bearing RUL prediction

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Research on Fusion Monitoring Method of Turning Cutting Tool Wear Based on particle filter algorithm

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IEEE ACCESS 2021年 9卷 85903-85917页

作者： Wang, Jun Zhou, Tingting Qilu Univ Technol Sch Mech & Automot Engn Shandong Acad Sci Jinan 250353 Peoples R China Shandong Inst Mech Design & Res Jinan 250031 Peoples R China

The monitoring of cutting tool wear has a great significance for the processing quality and stability. To overcome the difficulty to reflect all the wear mechanisms for the monitoring method based on finite element model or the excessive dependence on data extraction quality for the monitoring method based on sensor data, this paper proposes a model and data fusion method based on particle filter algorithm. Two different kinds of materials AISI 1045 and AISI 4340 are chosen to carry out the turning experiments. The mean absolute percentage error (MAPE) of the fusion method is 0.6%similar to 4.1% lower and the coefficient of determination (R-2) is more close to 1 compared with the finite element model method or sensor data method individually. Experimental results verify the feasibility and the superiority of the fusion method.

关键词： Tool wear monitoring particle filter algorithm model and data fusion finite element model sensor data

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Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application

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IET WIRELESS SENSOR SYSTEMS 2012年第4期2卷 394-401页

作者： Yang, P. Univ Bedfordshire Fac Creat Arts Technol & Sci Luton Bedfordshire England

Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particle filter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particle filter localisation algorithm.

关键词： nonlinear filters Computational complexity 2D range-only simultaneous localisation and mapping application SLAM applications particle filter algorithm Interpolation and function approximation (numerical analysis) filtering methods in signal processing computational complexity SLAM (robots) Kalman filters extended Kalman filter particle filtering (numerical methods) Robotics Signal processing theory range measurement EKF ultrasonic sensor

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Remaining Useful Life Prediction of Lithium-Ion Batteries Using particle filter algorithm in Communication Power Supply

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Russian Electrical Engineering 2024年第10期95卷 870-879页

作者： Zichen Wu Wang, Yicheng Zhang, Yunxiang Gu, Bin Xu, Mi State Grid Jiangsu Electric Power Co. LTD. Information and Communication Branch Jiangsu Province Nanjing City China

Abstract: With the development of communication technologies, the technology of power support, particularly the technology of lithium-ion batteries plays an important role in the communication area. The evaluation for the remaining useful life of lithium-ion batteries is a hot issue, to provide the maintenance plans and ensure the reliability and safety of system. Besides, existing methods lack to analyze the influence factors of prediction of the particle filter algorithm. In this paper, a method using particle filter algorithm for the remaining useful life prediction of lithium-ion batteries in communication power supply is carried out, and the comparisons of prediction effect with the least square method are investigated. From the results, it is clearly seen that the proposed particle filter algorithm presents a better prediction accuracy and stability than the least square method. In addition, the proposed particle filter algorithm presents a weak data dependence, and some cases with high prediction error are because of the starting points of prediction belong to the outlier of the fitting line. For a better prediction result, the number of data to be predicted by the proposed particle filter algorithm accounts about 55% of the total number of the cycle time, which is smaller than that accounts 70% by the least square method. This paper provides a technology support for the remaining useful life prediction of lithium-ion batteries. © Allerton Press, Inc. 2024.

关键词： communication power supply lithium-ion batteries particle filter algorithm remaining useful life prediction

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particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization

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Procedia Computer Science 2021年 187卷 206-211页

作者： Jin Yang Xuerong Cui Juan Li Shibao Li Jianhang Liu Haihua Chen College of computer science and technology China University of Petroleum(East China) Qingdao 266580 China College of Oceanography and Space Informatics China University of Petroleum(East China) Qingdao 266580 China

关键词： particle filter algorithm particle swarm optimization genetic algorithm target tracking location

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Day-ahead PV Power Forecast by Hybrid ANN Compared to the Five Parameters Model Estimated by particle filter algorithm 25th

Day-ahead PV Power Forecast by Hybrid ANN Compared to the Fi...

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25th International Conference on Artificial Neural Networks (ICANN)

作者： Ogliari, Emanuele Bolzoni, Alberto Leva, Sonia Mussetta, Marco Politecn Milan Dept Energy Via Lambruschini 4 I-20156 Milan Italy

ISBN: (纸本)9783319447810;9783319447803

A comparison between the hybrid method (PHANN - Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These methods have been employed to perform the day-ahead forecast of the output power of a photovoltaic plant. The aim of this work is to assess the forecast accuracy of the two methods.

关键词： Day-ahead energy forecast Artificial neural networks particle filter algorithm

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Speeding-Up the particle filter algorithm for Tracking Multiple Targets Using CUDA Programming

Speeding-Up the Particle Filter Algorithm for Tracking Multi...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Zhang, Jinhua Guan, Wenkai Ababei, Cristinel Medeiros, Henry Povinelli, Richard J. Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53233 USA

ISBN: (纸本)9781665458412

Object detection and tracking are essential tasks in many computer vision applications. One of the most popular tracking algorithms is the particle filter, which is widely used for real-time object tracking in live video streams. While very popular, the particle filter algorithm suffers from increased computational runtimes for high-resolution frames and large numbers of particles. In this paper, we investigate the use of CUDA programming as a method to parallelize portions of the particle filter algorithm in order to speed-up its execution time on compute systems that are equipped with NVIDIA GPUs. Experiments that compare a CPU sequential version, as the base case, with the CUDA parallelized version demonstrate an achievable speed-up of up to 7.5x for a 3840x2160 video resolution, and 9216 particles on a computer equipped with an NVIDIA Tesla K40c GPU.

关键词： object tracking particle filter algorithm GPU CUDA programming

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Influence of Reference Map Resolution on Indoor Terminal Positioning with the use of RSSI Measurements and particle filter algorithm 13

Influence of Reference Map Resolution on Indoor Terminal Pos...

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13th European Conference on Antennas and Propagation (EuCAP)

作者： Kawecki, Robert Korbel, Piotr Hausman, Slawomir Lodz Univ Technol Inst Elect Lodz Poland

ISBN: (纸本)9788890701887

The article discusses the factors affecting the performance of indoor positioning system employing three main components: Bluetooth Low Energy (BLE) transmitters, received signal strength maps obtained by measurements, and a particle filter algorithm. The results of experimental analysis of the impact of the resolution of the received power distribution reference maps on the positioning accuracy has been shown. The relation between the resolution of the maps and processing speed and accuracy of the positioning algorithms has been presented, showing that careful selection of the map resolution can result in significant reduction of the computation time while maintaining the accuracy of the positioning system.

关键词： indoor positioning particle filter algorithm received signal strength

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Iterated Unscented Kalman particle filter algorithm based on a variable-step adaptive artificial fish swarm optimization 35

Iterated Unscented Kalman Particle Filter algorithm based on...

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35th Chinese Control Conference, CCC 2016

作者： Li, Min Chen, Zhimei Shao, Xuejuan School of Electronic and Engineering Taiyuan University of Science and Technology Taiyuan030024 China

ISBN: (纸本)9789881563910

Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman particle filter algorithm, self-adaptive artificial fish swarm algorithm with changing step optimized Iterated Unscented Kalman particle filter algorithm was presented in this paper. It uses the alternation of behaviors of preying and swarming in the self-adaptive artificial fish swarm algorithm with changing step to optimize the re-sampling process firstly, which makes prior particles move towards the high likelihood region, the problem of sample impoverishment was resolved. As a result, the estimation accuracy of the system state was improved. Simulation results proves the effectiveness of the proposed algorithm. © 2016 TCCT.

关键词： a new self-adaptive artificial fish swarm algorithm with changing step Iterated Unscented Kalman particle filter algorithm particle filter algorithm

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