Remaining Useful Life Prediction of Capacitor Based on Genetic Algorithm and Particle Filter

Since lithium-ion batteries have been used in a wide range of fields, such as transportation industry, household appliances, and national defence industry. In order to avoid the unnecessary loss resulting from its sudden failure, it is necessary to timely predict the remaining useful life (RUL) of lithium-ion battery. In this paper, we present a novel remaining useful life estimation method for lithium-ion batteries, which depends on exemplar-based conditional particle filter (EC-PF). Differently from traditional particle filter, in the update phase, exemplar-based conditional particle filter combines historical data of multiple batteries with filtering stage of a single battery to compute the weights with respect to particles. This method can make the weights of particles more accurate, which results in improving the prediction accuracy. To verify the effectiveness and efficiency of the proposed method, a public data set is selected for validating prediction accuracy of RUL of battery. The results show that the proposed method improves the performance of the traditional particle filter method.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Efficient equestrian sports venue intelligent system based on particle filter object motion detection is designed and implemented in the paper. Traditional particle filtering often requires the participation of a large number of particles, and the particle depletion problem caused by resampling will affect the estimation accuracy. In this paper, the cluster optimization algorithm is used to improve the particle filter, which can improve the particle quality and ensure the particle diversity. When data needs to be sent, the table directly looks up the available routes, which greatly reduces the end-to-end delay. However, active routing protocols need to maintain routing information of the entire network anytime, anywhere, and nodes need to then constantly exchange control information and at this stage, the motion detection camera is further applied to construct the efficient equestrian sports venue intelligent system. Through the testing on the camera performance, the effectiveness is then proven to be efficient.

The position is one of the foremost imperative properties of an object. The Inertial Measurement Unit (IMU) development has enabled the system to continuously decrease in size, reduce power consumption, and become more and more universal. Although IMU meets the real-time needs of the localization system, it faces accumulative error and drifting problems. This paper presents an error constraint enhanced particle filter algorithm using quantum particle swarm optimization (EC-QPF), which achieves high-precision position estimation of target tracking with IMU. First, we proposed a quantum particle swarm optimization-based resampling method taking the place of the traditional weight-based resampling method in the particle filter, which avoids the particle impoverishment problem and keeps the particles’ diversity. The theoretical foundation of error constraint was raised applied to enhance the performance of the proposed particle filter. The error constraint is established utilizing the known estimated center and confidence scale to achieve particle screening based on the geometrical position of particles. Numerical experimental results show that the proposed EC-QPF has a 67% improvement instead of other modified particle filters and more efficiently eliminates the cumulative error. Furthermore, higher accuracy and stability are obtained under the exact condition, demonstrating better performance than traditional particle filter methods.

Particle filter is commonly used in various indoor positioning schemes, but sample impoverishment and weight degradation generally exist in particle filter. To solve this problem, this article uses the firefly algorithm to optimize particle filter. Using the global optimal value in the particle swarm to guide the remaining particles to update their positions, the particles tend to move to the high likelihood area, which can more accurately describe the true state of the target observation. At the same time, to avoid particles falling into local optimum and oscillating repeatedly at the extreme point, the mathematical model of the firefly algorithm is reconstructed in this article, which is named as adaptive optimization firefly algorithm (AOFA). The experimental results show that the positioning scheme can provide the positioning accuracy with an average error of less than 0.5 m. Compared with the traditional particle filter, the positioning accuracy is improved by 120%.

The human respiratory movement seriously affects the precision of the robotized radiotherapy treatment for lung cancer. In order to reduce the influence of respiratory movement, the most effective method is real-time tracking of respiratory tumor motion, which adopts tracking algorithm to predict the location of the tumor, and adjust the treatment beam to catch the motion of the target. This paper utilizes the particle filter as tracking algorithm to predict the tumor motion trajectory. Firstly, the principle and process of particle filter algorithm are introduced; Secondly, the probability correlation model based on UT transform are proposed as the observation model of the particle filter; Finally, the conventional particle filter and the UT transform modeling are used to predict the location of the tumor with respiratory motion. The effectiveness and accuracy of particle filter algorithm based on UT transform is verified by error analysis. The experiment results show that the particle filter algorithm based on UT transform has stronger robustness and higher prediction precision compared with the linear estimation and the traditional particle filter, so it can meet the requirements of real-time tumor tracking for radiotherapy robots.

Particle filter (PF) based object tracking methods have been widely used in computer vision. However, traditional particle filter trackers cannot effectively distinguish the target from the background in complex scenes since they only exploit appearance information of observation to determine the target region. In this paper, we present an adaptive particle filter based on energy field (EPF), which makes good use of moving information of previous frames adaptively to track the target. Besides, we present the mechanism of result rectification to ensure the target region is accurate. Experiment results on several challenging video sequences have verified that the adaptive EPF method is compared very robust and effective with the traditional particle filter in many complicated scenes.KeywordsTrackingparticle filterprobabilistic approximationimage sequence analysisdynamic scenes

Fatigue crack growth prognosis is important for prolonging service time, improving safety, and reducing maintenance cost in many safety-critical systems, such as in aircraft, wind turbines, bridges, and nuclear plants. Combining fatigue crack growth models with the particle filter (PF) method has proved promising to deal with the uncertainties during fatigue crack growth and reach a more accurate prognosis. However, research on prognosis methods integrating on-line crack monitoring with the PF method is still lacking, as well as experimental verifications. Besides, the PF methods adopted so far are almost all sequential importance resampling-based PFs, which usually encounter sample impoverishment problems, and hence performs poorly. To solve these problems, in this paper, the piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The deterministic resampling PF (DRPF) is proposed to be used in fatigue crack growth prognosis, which can overcome the sample impoverishment problem. The proposed method is verified through fatigue tests of attachment lugs, which are a kind of important joint component in aerospace systems.

ABSTRACTSatellite observations of surface chlorophyll have been assimilated into a coupled biophysical model of the Indian Ocean with particle filter (PF). Following the approach advocated in an earlier study, we have operated in a low-dimensional error subspace. Two different versions of PF have been implemented. In the traditional PF, the ensemble is augmented with two biological parameters, which are resampled along with state vector, followed by perturbation of the parameters with zero-mean Gaussian noise. In the other version, called guided PF, there is additional guidance of the ensemble towards future observations. While the traditional PF has not been able to outperform the standard run, the guided PF has been able to do so quite emphatically. This superior performance has been reflected in the time series of averaged chlorophyll in two sub-regions as well as in the spatial distribution of averaged chlorophyll in the same two sub-regions.

Traditional particle filter which uses simple geometric shapes for representation cannot track objects with complex shape accurately. In this paper, we propose a refined particle filter method for contour tracking based on a binary level set model. In contrast with other previous work, the computational efficiency is greatly improved due to the simple form of the level set function. In addition, we perform curve evolution in the update step to make good use of the observation at current time. Finally, we consider some appearance information as well as the energy function to measure the weight for particles, which can identify the target more accurately. Experiment results on several challenging video sequences have verified the proposed algorithm is efficient and effective in many complicated scenes.

Accurate prediction of the remaining useful life of lithium-ion batteries plays a significant role in various devices and many researchers have focused on lithium-ion battery reliability and prognosis. A particle filter (PF) is an effective filter for estimation and prediction of time series data where model structure is available. The prediction accuracy of a PF depends on two key factors: parameter initialization and the state equation. In this paper, parameters are estimated using a PF and two empirical exponential models, i.e. the exponential model and improved exponential model, are used to track the battery capacity degradation; each model uses a different state equation. Experiments were performed to compare prediction accuracy using the related parameters estimation model with that using the capacity decline model; this paper compares the effects of the different state equations on the lithium-ion battery remaining useful life prediction. The experimental results show the merits of the capacity decline model based on particle filtering. The capacity decline model PF is more suitable for estimating the battery capacity trend in the long term.

In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with<i> ε- greedy</i> exploration εPSO), is used to tackle the object tracking. In the modified εPSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The εPSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new εPSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our εPSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.

The INS/GPS navigation system is obvious nonlinear under the large initial condition errors. To tackle the accuracy of integrated navigation under nonlinear model, a improved particle filter named cubature particle filter (CPF) is applied to INS/GPS integrated navigation. For this, the nonlinear state model based on the platform misalignment angle and the observation model described by the velocity error and position error is established. The CPF that utilizes the current observation information in the prior updating phase develops the importance proposal distribution function by cubature kalman filter (CKF) to relieve particle sample degeneracy and impoverishment, so the improved particle algorithm significantly improves the accuracy of the integrated navigation system than traditional particle filter (PF).

We present a novel particle filter approach in this paper for robust object tracking using multi-cue association. In contrast to conventional particle trackers that are based on the single space, which does not provide enough information of good foreground/background discrimination, mapping the object into multiple spaces can help to seize the most informative properties of distinguishing the target from background. Moreover, the proposed algorithm commands all the spaces and their corresponding judgments in a global boosting view, which is implemented in an incremental manner to strengthen the ability of separating the target and background classes. The implicit appearance model is then used in updating step of the particle filter to measure the target region. Experimental results on several challenging video sequences have verified that the proposed method is compared very robust and effective with the traditional particle filter in many complicated scenes.

In order to solve the problem of particle dilution in traditional particle filter (PF) estimation of Ni-Cd Alkaline battery state of charge (SOC), a bat algorithm optimized particle filter was proposed to estimate SOC. The particles are represented as bat individuals, and the predatory process of bat population is simulated to solve the problem of particle dilution in particle filter technology; the theoretical model of battery state space is constructed by combining the second-order Thevenin battery model, and the related parameters of battery are identified; the SOC is estimated by using BA-PF algorithm and standard PF algorithm under pulse current condition. The experimental results show that, compared with the traditional PF algorithm, the SOC estimation accuracy of Ni-Cd alkaline battery based on BA-PF is less than 2%, and it has good adaptability and stability for nonlinear and non-Gaussian characteristics of Ni-Cd alkaline battery.