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Research of the accelerated fatigue experiment method based on the particle filtering algorithm method and the theory of crack growth

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THEORETICAL AND APPLIED FRACTURE MECHANICS 2023年第1期124卷

作者： Sun, SongSong Hou, Yu Gong, Xiaolin Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China

For the critical engine parts such as the crankshafts, the fatigue property is necessary for the guidance in actual engineering applications. At present, this property is usually evaluated by the standard fatigue experiment, which is time-consuming and expensive. In this paper, an accelerated fatigue experiment method was developed to shorten the period of the crankshaft fatigue experiment. First the residual fatigue life of the crankshaft was predicted based on the particle filtering algorithm method. Then the system state-space equations 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 result showed that this method could provide nearly the same statistical analysis result with that obtained from the standard experiment process (the relative difference is less than 2%), and the experiment period was cut down by 20% or more, which makes it feasible for actual engineering applications.

关键词： Crankshaft Accelerated fatigue experiment particle filtering algorithm Fatigue limit load

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Lithium-ion Battery End-of-discharge Time Prediction Using particle filtering algorithm

Lithium-ion Battery End-of-discharge Time Prediction Using P...

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Prognostics and System Health Management Conference (PHM- Hunan)

作者： Zhou, Zhenwei Huang, Yun Lu, Yudong Shi, Zhengyu Li, Xin Wu, Jiliang Li, Hui China Elect Prod Reliabil & Environm Testing Res 110 Dongguanzhuang Rd Guangzhou Guangdong Peoples R China Wuhan Zhongyuan Changjiang Sci & Technol Dev Co L Wuhan Hubei Peoples R China

ISBN: (纸本)9781479979585

The particle filtering (PF) algorithm is employed to predict Lithium-ion battery end-of-discharge time. The work voltage degradation model with six states is presented in the nonlinear state-space form, and the states such as model unknown parameters and work voltage are estimated by PF algorithm. Then the end-of-discharge time with probability distribution is further given by PF algorithm. The experimental example demonstrates the effectiveness of the proposed approach.

关键词： Lithium-ion battery end-of-discharge time probability distribution particle filtering algorithm

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A New particle filtering algorithm for Multiple Target Tracking with Non-linear Observations 17

A New Particle Filtering algorithm for Multiple Target Track...

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17th International Conference on Information Fusion (FUSION)

作者： Jiang, Lan Singh, Sumeetpal S. Yildirim, Sinan Univ Cambridge Dept Engn Cambridge CB2 1TN England Univ Bristol Sch Math Bristol BS8 1TH Avon England

ISBN: (纸本)9781479916344

In this paper, we present a multiple target tracking (MTT) algorithm for time-varying number of targets with linear state dynamics and a non-linear observation model. The algorithm uses a particle filter to target the joint posterior of data association and target states given observations. Target states are inferred by a Rao-Blackwellised particle filter which integrates out the velocity part of a target state, leaving only its position part to be sampled. We also design an efficient Markov chain Monte Carlo (MCMC) kernel to rejuvenate target positions in the spirit of the resample-move algorithm. Simulation results show that Rao-Balckwellisation of the velocity component and the additional MCMC move lead to a notable improvement over the standard particle filter for MTT.

关键词： Clutter Heuristic algorithms Hidden Markov models Kernel Random variables Target tracking Vectors Target tracking multiple target tracking clutter particle filters Heuristic algorithms Random variable Hidden Markov models Targets Kernel velocity component particle filtering algorithm Seeds

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An improved subpixel measurement method for piezoelectric ceramic driving characteristics based on particle filter

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MEASUREMENT 2025年 242卷

作者： Yang, Liu He, He Cheng, Jiajia Li, Dongjie Harbin Univ Sci & Technol Sch Automat Harbin 150080 Heilongjiang Peoples R China

Microscopic vision displacement calculation is crucial for its high precision and non-contact nature in measuring piezoelectric ceramic displacement. However, the inefficient global search limits its effectiveness. This paper proposes an improved sub-pixel algorithm (GKF-PFBM) based on particle filtering. Firstly, particle filtering (PF) is combined with block matching (BM) to enhance matching efficiency and accuracy by replacing global search with particle state prediction and update. Then, subpixel interpolation using a Gaussian kernel function (GKF) is better adapted to nonlinear variations, handling high-frequency variations while preserving clear edges, thereby improving interpolation accuracy. Finally, a high-precision experimental platform is used to measure the driving characteristics of piezoelectric ceramics. Experimental results show that the mean errors of the GKF-PFBM algorithm and the bilinear interpolation algorithm (BI-BM) are 0.0032pixels and 0.1269pixels, respectively. The average hysteresis displacement error was measured to be approximately 12 nm by this method, verifying its precise non-contact measurement capability.

关键词： Piezoelectric ceramics Non-contact measurementl particle filtering algorithm Sub-pixel interpolation Gaussian kernel function

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Enhancing Human Motion Prediction through Joint-based Analysis and AVI Video Conversion

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MOBILE NETWORKS & APPLICATIONS 2023年第5期28卷 1673-1686页

作者： Xiong, Yi Moqurrab, Syed Atif Ahmad, Awais Hubei Univ Technol Phys Educ Inst Wuhan 430068 Peoples R China Gachon Univ Sch Comp 1342Seongnam DaeroSujeong Gu Seongnam 13120 South Korea Imam Muhammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Riyadh Saudi Arabia

Human joint motion exhibits a high degree of freedom, with different joints capable of moving and rotating in various directions. Consequently, accurately capturing the features of posture motion becomes challenging, resulting in lower prediction accuracy. To address this issue, this paper proposes a novel method for predicting human motion based on joints using AVI video conversion. The foreground of human motion images in AVI videos is extracted using a Gaussian background model, and the AVI video is converted into a 3D video by fusing the foreground and background images. The spatiotemporal weighted attitude motion features of the 3D video frames are extracted and utilized as input for a CNN algorithm. Motion feature vectorization is employed to reduce motion edge detection errors through a spatiotemporal weighted adaptive interpolation method. Subsequently, the motion basis is generated after processing the fusion of attitude edge features. The particle filter algorithm is utilized to establish the human joint motion model, and joint-based motion prediction is conducted based on the motion basis. Experimental results demonstrate that the 3D conversion enhances the background depth of the 2-dimensional AVI video. Additionally, the proposed method extracts motion bases with clear performance, accurate actions, smooth outlines, and non-redundant backgrounds. The prediction results of human movement based on joints exhibit accuracy, with the error in comparison to actual movement falling within a controllable range.

关键词： Sports Convolutional neural network Joint motion Motion prediction AVI video conversion particle filtering algorithm

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Analysis of physical activity effects on plasma glucose-insulin system dynamics: A mathematical model

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第14期43卷 3272-3281页

作者： Ntaganda, Jean Marie Minani, Froduald Banzi, Wellars Mpinganzima, Lydie Niyobuhungiro, Japhet Dusabejambo, Vincent Kambutse, Immaculate Rutaganda, Eric Univ Rwanda Coll Sci & Technol Sch Sci Dept Math Kigali 3900 Rwanda Univ Rwanda Coll Med & Hlth Sci Sch Med & Pharm Kigali Rwanda King Faisal Hosp Kigali Kigali Rwanda

The main objective of this paper is to understand and analyse the effect of physical activity on plasma glucose and plasma insulin levels through mathematical modelling. Energy for the human body during physical activity is provided by glucose as sugar, while insulin as hormone supports the absorption of this glucose. Furthermore, glucose and insulin are interrelated physiologically by some parameters that we estimate mathematically by using nonlinear optimization and data collected in Rwanda. Research in this direction has been done by, for example Anirban and colleagues, who developed a dynamic model of exercise effect on plasma glucose and insulin levels. As a benchmark, the results of numerical simulation obtained in this paper have been compared with those of Anirban and colleagues to test the efficiency of our mathematical model. We have concluded that the results of those two separate mathematical models are in good accordance and that the proposed mathematical model allows further investigation of the effects of physical activity on the dynamics of the glucose-insulin system. Moreover, we have implemented a particle filtering algorithm for estimation of glucose and insulin in internal parts of the body such as heart and liver by using measurements from peripheral tissues as noisy data because taking blood samples from all parts of the body is practically and clinically impossible.

关键词： Glucose insulin particle filtering algorithm physical activity Roy's mathematical model parameter identification

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Application of Cloud Video Information Processing Technology in Alleviating the Food Safety Trust Crisis

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第2期34卷

作者： Dai, Yu Yellow River Conservancy Tech Inst Sch Marxism Kaifeng Peoples R China

In view of the current consumer trust crisis in food safety, some researchers proposed to build a reliable food safety traceability system solution. Because the food safety traceability system requires storing and processing of massive video data, this paper proposes to build a reliable food safety traceability system by introducing cloud storage technology into video surveillance system. Hadoop platform is built to store and process massive monitoring of video data. In addition, in order to make use of Map Reduce framework for parallel computing, this paper optimizes the traditional particle filter target tracking algorithm, and proposes using the parallel computing framework Map Reduce in Hadoop to achieve the parallel computing of all particles in particle filter. Finally, the simulation results show that when the number of videos is large, the time of parallel processing is obviously shorter than that of single-machine mode. And as the video data becomes larger and larger, the advantages of parallel processing become more and more obvious.

关键词： Cloud video map reduce frame food safety particle filtering algorithm

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A NOVEL particle filtering algorithm BASED ON STATE FUSION

A NOVEL PARTICLE FILTERING ALGORITHM BASED ON STATE FUSION

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IET International Radar Conference

作者： H.B.Yu G.H.Wang Q.Cao Y.Sun Institute of Information Fusion Naval Aeronautical and Astronautical University

ISBN: (纸本)9781629931340

To address the limitations of the particle filter algorithm (PF),we propose the fusioned particle filter (FPF). In this new method, the importance density function is generated by state fusion of the extended Kalman filter algorithm (EKF) and the unscented Kalman filter algorithm (UKF). To construct the importance density of samples, the state of each particle is predicted according to the EKF and the UKF, successively. And the feedback of state estimation from the last step is used to implement the update of particles. Thus, using the most of recent measurements and the additional feekback information,FPF can obtain an accurate approximation to the nonlinear non-Gaussian system and as a result, improve the estimation performance. An application example is given to draw a comparison between the FPF and the existing particle filter algorithms. The simulation results show the efficiency of this new approach.

关键词： particle filtering algorithm state fusion recent measurements feedback information nonlinear non-Gaussian system.

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Predictive Maintenance Using GPU-Accelerated Partially Observable Markov Decision Process

Predictive Maintenance Using GPU-Accelerated Partially Obser...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Naman Sharma Chen-Khong Tham Dept. of ECE National University of Singapore Singapore

ISBN: (纸本)9781728125848

The Industrial IoT era has seen an outburst of areas benefiting from collecting more data. This includes Industry 4.0 and predictive maintenance, which have benefited from advancements in edge and fog computing. Predictive maintenance aims to minimize the downtime due to maintenance of machinery, while simultaneously minimizing the risk of unforeseen failures. This paper proposes a method to aid industries to make maintenance scheduling decisions that can be adopted in a distributed factory environment. The Partially Observable Markov Decision Process (POMDP) approach is used to determine the optimal time for maintenance for a machine. We first put forward an offline method for learning the Markov model parameters using historical sensor data. To allow for continual learning, an algorithm based on particle filters is proposed to provide online estimation of parameters of a Partially Observable MDP model. The particle filter algorithm allows the framework to adapt uniquely to each machine. The relative benefits of the POMDP model over a standard MDP model in the presence of noisy sensor data are evaluated through simulations which show significant improvements in revenue and reduced downtime. The POMDP and particle filter computations are executed on GPU-accelerated edge devices which achieve a speed-up of around 4 times compared to the CPU implementation.

关键词： Predictive maintenance Industrial IoT Partially observable MDP particle filter GPU Predictive maintenance particle filters Markov chain Sensor Data downtime GRAPPER PICK UP Graphics Processing Unit particle filtering algorithm

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Master-Slave Guidewire and Catheter Robotic System for Cardiovascular Intervention

Master-Slave Guidewire and Catheter Robotic System for Cardi...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yujia Xiang Hao Shen Le Xie Hesheng Wang School of Biomedical Engineering Shanghai Jiao Tong University Shanghai CHN Institute of Forming Technology & Equipment Shanghai Jiao Tong University Shanghai CHN Institute of Forming Technology & Equipment School of Biomedical Engineering Shanghai Jiao Tong University Shanghai CHN Department of Automation Shanghai Jiao Tong University Shanghai CHN

ISBN: (纸本)9781728126234

Cardiovascular disease remains a primary cause of morbidity globally. Percutaneous coronary intervention plays a crucial role in the treatment. The radiation exposure of surgeons during the cardiovascular intervention can be avoided by master-slave surgical robots. This paper introduces a master- slave guidewire and catheter robotic system to protect the surgeons from X ray radiation to the most extent. And the jitters of master manipulators are mitigated by Kalman filtering algorithm. With two master manipulators, it helps to retain the surgeon's traditional operating habits. Also, a vascular model trial was conducted to validate that this interventional robotic system could complete the alternate progress and rotation of interventional guidewire and catheter.

关键词： Surgeons Master-slave Catheters Guidewire Jitter Radiation Exposure Cardiovascular Diseases Manipulators filtering algorithms X-rays image filtering algorithm particle filtering algorithm

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