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 expe...
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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.
The particlefiltering (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...
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ISBN:
(纸本)9781479979585
The particlefiltering (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.
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...
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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.
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, ...
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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.
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. Th...
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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 particlefiltering. Firstly, particlefiltering (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.
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 activ...
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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.
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 pro...
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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.
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 f...
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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.
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. Predi...
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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.
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...
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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 filteringalgorithm. 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.
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