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Joint SOH and RUL estimation for lithium-ion batteries via optimal deep belief network with bayesian algorithm

JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Zheng, Ruyi Yang, Bo Qian, Yucun Li, Hongbiao Gao, Dengke Jiang, Lin Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China Shanghai KeLiang Informat Technol Co Ltd Shanghai 201103 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

This article proposes an innovative method for assessing the state of health (SOH) and remaining useful life (RUL) of lithium batteries. The innovation lies in the integration of an optimal deep belief network with a bayesian algorithm (ODBN-BA) for joint estimation, coupled with intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) for in-depth feature extraction. Through precise parameter tuning using BA, the model achieves significant enhancements in prediction accuracy, robustness, and generalization capabilities. The experiment was validated using publicly available data from the national aeronautics and space administration (NASA) center for excellence in forecasting and the center for advanced life cycle engineering (CALCE), and compared with various advanced algorithms. This article uses SimuNPS for simulation verification, the results showed that ODBN-BA method proposed in this paper performed well in both SOH and RUL estimation, with high accuracy, strong robustness, and good generalization. Especially when dealing with noisy data, this method can still maintain excellent estimation performance, providing an effective solution for online monitoring of lithium battery SOH and accurate prediction of RUL. In the experimental data, SOH estimated MAE value of B0005 battery was as low as 9.7261E-05, and RUL estimated AE value was 0, further proving the excellent ability of ODBN-BA model in reducing estimation errors and improving estimation accuracy, demonstrating its huge potential for application in battery health management.

关键词： Lithium-ion battery bayesian algorithm Deep belief network State of health Remaining useful life SimuNPS

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bayesian algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

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SENSORS 2009年第2期9卷 731-755页

作者： Sarigiannis, Dimosthenis A. Karakitsios, Spyros P. Gotti, Alberto Papaloukas, Costas L. Kassomenos, Pavlos A. Pilidis, Georgios A. Commiss European Communities Joint Res Ctr Inst Hlth & Consumer Protect Phys & Chem Exposure Unit PCE I-21020 Ispra Va Italy Univ Ioannina Dept Biol Applicat & Technol Lab Bioinformat GR-45110 Ioannina Greece Univ Ioannina Dept Phys Lab Meteorol GR-45110 Ioannina Greece Univ Ioannina Dept Biol Appl & Technol Environm Chem Lab GR-45110 Ioannina Greece

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

关键词： bayesian algorithm ANN PBPK benzene exposure

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bayesian algorithm for high-resolution DOA estimation

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ELECTRONICS LETTERS 2015年第21期51卷 1701-U106页

作者： Zhang, Yin Huang, Yulin Yang, Jianyu Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Sichuan Peoples R China

A maximum a posteriori (MAP) algorithm based on bayesian criterion for high-resolution direction-of-arrival (DOA) estimation in array signal processing is proposed. The generalised Gaussian distribution was considered as the prior information of sources distribution for its wide applicability. The statistic parameter of the generalised Gaussian function can be transformed according to different sources distribution conditions. Only the sparse signal recovery problem is considered. It is found that with the generalised Gaussian sparse constraint, the proposed MAP algorithm provides high DOA estimation performance in the case of limited snapshots. Simulation results are given to verify the effectiveness and efficiency of the proposed algorithm.

关键词： Bayes methods Gaussian distribution direction-of-arrival estimation bayesian algorithm bayesian criterion array signal processing generalised Gaussian distribution generalised Gaussian sparse constraint high-resolution DOA estimation high-resolution direction-of-arrival estimation maximum a posteriori algorithm sources distribution sparse signal recovery problem statistic parameter

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bayesian algorithm Based Traffic Prediction of Big Data Services in Open Flow Controlled Optical Networks 2

Bayesian Algorithm Based Traffic Prediction of Big Data Serv...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Chong.Zhao Hongwei.Chang Quanchun Liu Beijing Smart Chip Microelectronics Company Limited

ISBN: (纸本)9781509036202

In the big data era,the next generation optical networks technology for big data has attracted more and more attention,with increasing challenges of optical *** the Open Flow controlled optical network,it is a great problem to predict long-term traffic characteristics only according to short-term traffic *** it may fail to actively support diversified ***,the so-called bayesian algorithm Based Traffic Prediction Scheme is presented in this article,which makes full used of the bayesian algorithm to make long-term traffic *** result shows that it can achieve quite high accuracy.

关键词： Software Defined Optical Network Big Data bayesian algorithm Traffic Prediction

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bayesian algorithm Matching Analysis of Battery Charging Pile and Battery Charging Cable of Battery Vehicle 3

Bayesian Algorithm Matching Analysis of Battery Charging Pil...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Jia, Taihua College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798350317022

The matching analysis of battery charging piles and battery charging cables of battery car rooted in particle swarm algorithm has the matching problem of battery charging piles and battery charging cables, which is of great significance for improving battery charging efficiency, alleviating battery charging pressure and improving user experience. Rooted in bayesian algorithm, this paper deeply studies the matching analysis of battery charging piles and battery charging cables for battery car. The results show that the matching analysis method rooted in bayesian algorithm can quickly and accurately determine the suitable combination of battery charging cable and battery charging pile, so as to improve battery charging efficiency, reduce user waiting time and battery charging difficulty. In addition, due to the flexibility and adaptability of bayesian algorithm, the method can also be dynamically adjusted according to different user needs and environmental changes to further improve the battery charging experience. Therefore, the research results of the paper provide strong support for the optimization and intelligence of battery car charging facilities. © 2023 IEEE.

关键词： Battery Car Battery Charging Cables Battery Charging Piles bayesian algorithm Matching Analysis

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Recursive bayesian algorithm for Identification of Systems with Non-uniformly Sampled Input Data

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International Journal of Automation and computing 2018年第3期15卷 335-344页

作者： Shao-Xue Jing Tian-Hong Pan Zheng-Ming Li School of Electrical Information and Engineering Jiangsu University Department of Electrical Engineering Huaian College of Information and Technology

To identify systems with non-uniformly sampled input data, a recursive bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.

关键词： Parameter estimation discrete time systems Gaussian noise bayesian algorithm covariance resetting.

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E-Commerce data classification in the cloud environment based on bayesian algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 5819-5826页

作者： Xu, Bing Chongqing Jiaotong Univ Sch Econ & Management Chongqing Peoples R China

In the process of e-commerce transactions, a large amount of data will be generated, whose effective classification is one of current research hotspots. An improved feature selection method was proposed based on the characteristics of bayesian classification algorithm. Due to the long training and testing time of modern large-scale data classification on a single computer, a data classification algorithm based on Naive Bayes was designed and implemented on the Hadoop distributed platform. The experimental results showed that the improved algorithm could effectively improve the accuracy of classification, and the designed parallel bayesian data classification algorithm had high efficiency, which was suitable for the processing and analysis of massive data.

关键词： E-commerce transactions cloud environment bayesian algorithm

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Dynamic Evolution of Financial Technology and Artificial Intelligence for Economy Based on bayesian algorithm in Cyber-Physical System

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2024年 14卷

作者： Wang, Xu Shandong Womens Univ Sch Econ Jinan Shandong Peoples R China

With the rapid development of information technology and the popularity of big data, the demand for data analysis has become increasingly large. In recent years, data mining technology has received extensive attention. It is a technology that converts a large amount of data into useful information and finds the relationship between data and information. The classification algorithm is one of the important research contents in data mining. It mainly divides the data items in the dataset into a fixed category according to various classifiers. From the perspective of the development trend of the world economy, technological progress, consumption upgrading and institutional change are mutually reinforcing and integrating. In financial technology, the evolution of technology is reshaping the financial industry. Data intelligence technology based on machine learning and other algorithms is changing rapidly. Data is the largest means of production, the most important factor of production of artificial intelligence (AI), and the core factor of production. Big data, cloud computing, AI and other emerging technologies build different data structures for processing through deep learning and other learning algorithms to achieve accurate analysis, rapid decision-making and other characteristics. At the same time, it also provides financial institutions with more accurate and comprehensive data services. It has established long-term cooperation with financial institutions, promoting the accelerated integration of finance and technology and jointly creating a more intelligent financial system. bayesian algorithm is a classical algorithm in classification algorithms. It is a possibility reasoning method based on probability statistics. bayesian algorithm calculates a posteriori probability through a priori probability. Because of its small error rate, it has been widely used in various fields. This paper will analyze banking financial technology and AI economy based on cyber-physic

关键词： Financial Technology Artificial Intelligence Economy Cyber-Physical System bayesian algorithm Dynamic Evolution

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Neural network using bayesian algorithm for estimation of petroleum reservoir

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KYBERNETES 2013年第7期42卷 1044-1058页

作者： Alaei, Hesam Odin Komari Yazdizadeh, Alireza Power & Water Univ Technol Tehran Iran Power & Water Univ Technol Dept Elect Engn Tehran Iran

Purpose - This paper is concerned with the estimation of reservoir parameters in the presence of noise and outliers using neural network (NN) and bayesian algorithm. The paper aims to discuss these issues. Design/methodology/approach - Outlier detection is of great importance to prediction of time series data. A reliable predictive methodology is proposed based on NN and bayesian algorithm to efficiency estimates of the parameters of a petroleum reservoir. This strategy is applied to estimate the parameters of Marun reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Findings - For an evaluation purpose, the performance and generalization capabilities of Bayes-ANN are compared with the common technique of back propagation (BP). Practical implications - The experimental results demonstrate that the proposed hybrid Bayes-NN algorithm is able to reveal a better performance than conventional BP NN algorithms. Originality/value - Helped oil and gas companies to estimation of petroleum reservoir parameters more accurate than other methods in the presence of noise and outliers.

关键词： bayesian algorithm Neural network Well log data Reservoir Outlier Back propagation

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Error correction of semantic mathematical expressions based on bayesian algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第6期19卷 5428-5445页

作者： Wang, Xue Yang, Fang Liu, Hongyuan Shi, Qingxuan Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Peoples R China Hebei Univ Inst Intelligent Image & Document Informat Proc Baoding 071002 Peoples R China

The semantic information of mathematical expressions plays an important role in information retrieval and similarity calculation. However, a large number of presentational expressions in the presentation MathML format contained in electronic scientific documents do not reflect semantic information. It is a shortcut to extract semantic information using the rule mapping method to convert presentational expressions in presentation MathML format into semantic expressions in the content MathML format. However, the conversion result is prone to semantic errors because the expressions in the two formats do not have exact correspondences in grammatical structures and markups. In this study, a bayesian error correction algorithm is proposed to correct the semantic errors in the conversion results of mathematical expressions based on the rule mapping method. In this study, the expressions in presentation MathML and content MathML in the NTCIR data set are used as the training set to optimize the parameters of the bayesian model. The expressions in presentation MathML in the documents collected by the laboratory from the CNKI website are used as the test set to test the error correction results. The experimental results show that the average F-1 value is 0.239 with the rule mapping method, and the average F-1 value is 0.881 with the bayesian error correction method, with the average error correction rate is 0.853.

关键词： error correction mathematical expressions bayesian algorithm presentation MathML content MathML

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