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40th Chinese Control Conference (CCC)

作者： Niu, Buzhao Li, Liwei Xu, Chao Yu, Fei CRRC Qingdao Sifang Co Ltd Dept Qual Management Qingdao 266111 Shandong Peoples R China Qingdao Univ Weihai Innovat Inst Weihai 264200 Shandong Peoples R China Qingdao Univ Sch Elect Engn Qingdao 266071 Shandong Peoples R China China Railway Guangzhou Grp Co Ltd Guangzhou 511483 Guangdong Peoples R China

ISBN: (纸本)9789881563804

With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health (SOH) is of great significance in the safe and reliable operation of battery management system and the reduction of maintenance cost. Based on the analysis of the traditional particle filter algorithm, the genetic factor of genetic algorithm (GA) is introduced into the particle filter and improved by adaptive mutation. In order to predict the SOH of lithium-ion battery, the health index (HI) is extracted from the measurable parameters of lithium-ion battery. The mapping model between HI index and SOH is established and applied to the observation of state space model. In this paper, a battery SOH estimation method based on improved particle filter algorithm is proposed. The experimental results show that the proposed method is superior to the traditional particle filter (PF) algorithm and has good accuracy in estimating the degradation process of lithium-ion batteries.

关键词： particle battery SOH estimation genetic algorithm particle filter algorithm health indicator

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Multi-pedestrian Tracking for Far-infrared Pedestrian Detection On-board Using particle filter 12

Multi-pedestrian Tracking for Far-infrared Pedestrian Detect...

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IEEE International Conference on Imaging Systems and Techniques (IST)

作者： Xu, Ruilin Liu, Qiong South China Univ Technol Sch Software & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781479986330

Target tracking is important for pedestrian detection in on-board vision application preventing traffic accidents effectively. Facing complex traffic scene including background change, various pedestrian appearance and multi-targets etc., existing target tracking algorithms such as Kalman and particle filters expose shortcomings in accuracy, robustness and availability. This paper proposes an improved particle filter algorithm for multi-target tracking in far-infrared (FIR) pedestrian detection, where a heuristic tracking scheme including feature model learning and target tracking iteratively is used. Partial least squares regression (PLSR) and heuristic computation are adopted to learn and update feature models for each pedestrian. The proposed particle filter algorithm combines adaptive searching region and double feature models, to achieve higher target tracking performance. Experiment on several FIR video sequences demonstrates the improved scheme outperforms comparing with other particle filter algorithms when multi-pedestrian tracking, even with partial occlusion, scale and posture variation.

关键词： particle filter algorithm multi-pedestrian tracking multiple feature models partial least squares regression far-infrared pedestrian detection

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A novel hybrid information-based PF-WOA algorithm for gas source localization in 3D space

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ROBOTICA 2024年第11期42卷 3748-3767页

作者： Wang, Li Ren, Ziyu Fan, Shurui Zhang, Yong Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particle filter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the Z-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particle filter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.

关键词： gas source localization whale optimization algorithm information entropy particle filter algorithm

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Dynamic Opponent Modelling in Fictitious Play

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COMPUTER JOURNAL 2010年第9期53卷 1344-1359页

作者： Smyrnakis, Michalis Leslie, David S. Univ Bristol Dept Math Bristol BS8 1TW Avon England

Distributed optimization can be formulated as an n-player coordination game. One of the most common learning techniques in game theory is fictitious play and its variations. However, fictitious play is founded on an implicit assumption that opponents' strategies are stationary. In this paper we present a new variation of fictitious play in which players predict opponents' strategy using a particle filter algorithm. This allows us to use a more realistic model of opponent strategy. We used pre-specified opponents' strategies to examine if our algorithm can efficiently track the strategies. Furthermore, we have used these experiments to examine the impact of different values of our algorithm parameters on the results of strategy tracking. We then compared the results of the proposed algorithm with those of stochastic and geometric fictitious play in three different strategic form games: a potential game and two climbing hill games, one with two players and the other with three players. We also tested our algorithm in two different distributed optimization scenarios, a vehicle-target assignment game and a disaster management problem. Our algorithm converges to the optimum faster than both the competitor algorithms in the strategic form games and the vehicle-target assignment game. Hence by placing a greater computational demand on the individual agents, less communication is required between the agents. In the disaster management scenario we compared the results of particle filter fictitious play with the ones of Matlab's centralized algorithm bintprog and the centralized pre-planning algorithm of (Gelenbe, E. and Timotheou, S. (2008) Random neural networks with synchronized interactions. Neural Comput., 20(9), 2308-2324). In this scenario our algorithm performed better than the pre-planning algorithm in two of the three performance measures we used.

关键词： fictitious play particle filter algorithm Aladdin special issue learning in games distributed optimization

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Online Fault Diagnosis of Electromagnetic Launch System via Time Series Anomaly Detection

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2024年第8期52卷 3285-3293页

作者： Zeng, Delin Lu, Junyong Naval Univ Engn Natl Key Lab Electromagnet Energy Wuhan 430033 Peoples R China

As a special nonperiodic transient system, the electromagnetic launch system realizes the conversion of ultrahigh power of energy in a few seconds, which is harmful when the system fails. It is urgent to study the online fault diagnosis method of the system to stop the launch in time. Fault diagnosis based on online detection of abnormal waveform of time series in launch period is an important direction to solve the problems. Compared with traditional waveforms anomaly detection, the time series data points of electromagnetic launch system are very large, the time distortion is serious, and the abnormal waveform characteristics are not obvious. Therefore, the traditional methods can not realize online anomaly detection and location. This article analyzes the characteristics of electromagnetic launch time series and proposes a novel named FWSSP-TSAD anomaly detection method. To verify the performance of the proposed method, multiple discharge tests were conducted based on an electromagnetic launch system, and the obtained PFN voltage time series dataset was used as an algorithm input. The results show that the proposed algorithm accurately identifies all abnormal waveforms and extracts all abnormal sub waveforms, achieving fault diagnosis and localization. The average calculation time is less than the window time, which meets the requirements of online fault diagnosis.

关键词： Electromagnetics Time series analysis Anomaly detection Fault diagnosis Process control Electromagnetic scattering Transient analysis Electromagnetic launch system online fault diagnosis particle filter algorithm shapelets clustering time series anomaly detection

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Microtunneling decision support system (MDS) using Neural-Autoregressive Hidden Markov Model

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5801-5808页

作者： Leu, Sou-Sen Adi, Tri Joko Wahyu NTUST Dept Construct Engn Taipei 10672 Taiwan Sepuluh Nopember Inst Technol ITS Dept Civil Engn Surabaya 60111 Indonesia

Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use. This paper presents a general model for microtunneling decision support system (MDS) that can be used as a basis for developing more effective microtunneling design and construction. The model objectives are to: (1) develop a description of local geology that reflects the uncertainty of the information on which it is based and (2) provide the input data necessary for other decision support systems. MDS is composed of two main modules: (1) geology prediction model (GPM) module which is based on Neural-Autoregressive Hidden Markov Model and (2) excavation method selection module to select appropriate excavation method based on GPM result. In order to validate the proposed model, a microtunneling project: Zhong-he drainage water tunnel in Taiwan, was used as a case study. The result shows that the MDS model achieves these objectives to a satisfactory degrese. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Decision support system Geological prediction model Autoregressive Hidden Markov Model particle filter algorithm Tunneling

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Quantifying Streamflow Prediction Uncertainty Through Process-Aware Data-Driven Models

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HYDROLOGICAL PROCESSES 2024年第11期38卷

作者： Roy, Abhinanda Kasiviswanathan, K. S. Indian Inst Technol Roorkee Dept Water Resources Dev & Management Roorkee India Indian Inst Technol Roorkee Mehta Family Sch Data Sci & Artificial Intelligenc Roorkee India

The hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state-of-the-art data-driven models (or DDMs) in quantifying the prediction uncertainty of process-based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian-based data assimilation (DA) technique is used to quantify uncertainty in process-based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process-based hydrological models: HBV and HyMOD. particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process-aware DDMs: HBV-PF-RF, HBV-PF-SVM, HyMOD-PF-RF and HyMOD-PF-SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash-sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process-bas

关键词： data-driven models hydrological models particle filter algorithm predictive uncertainty streamflow simulation

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A new crankshaft bending fatigue test method: both residual life prediction and statistical analysis

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2023年第3期6卷 347-355页

作者： Liu, Jinyan Sun, Songsong Gong, Xiaolin Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China

In modern engineering application, for the metal engine parts such as the crankshafts and some other related objects, sufficient high-cycle fatigue strength is necessary to guarantee the system reliability during the working period. The traditional resonant crankshaft fatigue test bench can determine the load-life relationship of the given crankshaft directly, but the experiment process usually lasts tens of days, which may result in the large consumption of time. In this paper, the fatigue test speed was accelerated based on the prediction of the residual fatigue life during the experiment process. Then the system state-space equation was modified based on the theory of fracture mechanics to improve the accuracy of the predictions. Finally, the statistical analysis based on the predicted data was adopted to determine the fatigue limit load of the crankshaft. The main conclusion of this paper is that the combination of the particle filter algorithm and the dynamic response signal can predict the residual fatigue life of the crankshaft conveniently based on the modified sampling range, and thus is able to shorten the experiment process and has very wide popularization and application prospects in actual engineering.

关键词： Inherent frequency Crankshaft Bending fatigue Residual fatigue life prediction particle filter algorithm

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Fusing Fixed and Hint Landmarks on Crowd Paths for Automatically Constructing Wi-Fi Fingerprint Database

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China Communications 2015年第1期12卷 11-24页

作者： HUANG Zhengyong XIA Jun YU Hui GUAN Yunfeng GAN Xiaoying LIU Jing Department of Electronic Engineering Shanghai Jiao Tong University The State Key Laboratory of Integrated Service Networks Xidian University

In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their *** this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd ***,the accuracy degradation problem may be introduced as crowd customers are not professional trained and ***,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint *** techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces ***,the particle filter algorithm is also introduced to smooth the sample points in crowd *** implemented the approach on off-the-shelf smartphones and evaluate the *** results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.

关键词： indoor localization fingerprint database construction fixed landmarks hint landmarks particle filter algorithm

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Research on adaptive selection method of radiation sources in passive radar based on GNSS signal

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第6期23卷 2825-2833页

作者： Tang, Huatao Cha, Hao Liu, Feng Tian, Bin Zuo, Lei Naval Univ Engn Coll Elect Engn Wuhan 430033 Hubei Peoples R China

Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power.

关键词： Passive radar GNSS signal CRLB particle filter algorithm target tracking

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