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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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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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TF-IDF combined rank factor Naive bayesian algorithm for intelligent language classification recommendation systems

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SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Luo, Yonglian Lu, Cailin Jinzhong Univ Network Informat Ctr Jinzhong 030024 Peoples R China

With the continuous improvement of smart language systems, a large amount of language text data has emerged. How to efficiently and accurately process these text data has become an important challenge. Therefore, a speech classification recommendation system based on improved Naive bayesian algorithm is proposed. The system first adopts the traditional bayesian algorithm to classify language texts. The Term Frequency-Inverse Document Frequency and rank factor are combined to increase the weight of feature languages. Then, the classified language texts are combined with the improved algorithm for language classification recommendation. Finally, performance testing and simulation applications are conducted on the system. From the results, in the Gutenberg corpus, the research algorithm had the highest accuracy and completeness, with 98.5 % and 91.6 %, respectively, and the lowest values were 92.6 % and 89.4 %. The average values were 95.5 % and 91.1 %, with an F1 value of about 92.6 %. In the Brown corpus, the average accuracy, completeness, and F1 value of the designed algorithm were 96.2 %, 91.2 %, and 93.2 %, respectively. When the number of online customers reached 1000, the response time of the designed Chinese system was 1.15 s, the classification recommendation accuracy was 95 %, and the system stability was about 83 % on average. The response time of the English system was 0.64 s, the classification recommendation accuracy was 96 %, and the system stability was about 90 % on average. It shows that the designed method can significantly enhance the operation accuracy of the classification recommendation system.

关键词： Classification recommendation system bayesian algorithm TF-IDF Rank factor Language text

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The Effect of RHA and GBFS on the Mechanical and Physical Properties of Cementitious Composites with High Early Age Compressive and Flexural Strength: bayesian algorithm for the Design

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2024年第1期48卷 97-115页

作者： Bayraktar, Oguzhan Yavuz Fello, Fadel Mohamed Ali Kastamonu Univ Civil Engn Dept TR-37150 Kastamonu Turkiye Kastamonu Univ Dept Mat Sci & Engn TR-37150 Kastamonu Turkiye

In recent years, cementitious composites with a compressive strength of more than 100 MPa have become popular for tall earthquake-resistant buildings. However, in these composites, attention is paid to sustainability criteria along with strength. In this study, physical, mechanical, and microstructural properties of cementitious composites with high early-age strength were investigated by using industrial wastes such as RHA (rice husk ash) and GBFS (ground blast furnace slag). In cementitious composites, the cement dosage was chosen as 1000 kg and the w/c (water/cement) or w/b (water/binder) ratio was 0.22. RHA was used instead of cement at the rate of 5 and 10%, and GBFS at the rate of 5, 10, and 15%. Cementitious composites were subjected to three different cures: normal water (WC), hot water (HWC), and steam (SC). As the RHA content increased, the flow diameter of the mixtures decreased. GBFS relatively improved the workability of cementitious composites. While the porosity of WC applied mixtures varies between 2.7 and 4.3%, the porosity of HWC or SC applied mixtures decreases up to 1.3%. The water absorption of all cementitious composites is less than 2%. The 3-day compressive strengths of the mixtures are between 53.4 and 90.6 MPa, and the 90-day compressive strengths are between 100.2 and 123.3 MPa. In addition, the 3-day flexural strength of the mixtures exceeds 7 MPa. On the 90th day, cementitious composites with a flexural strength of approximately 18 MPa were produced. As the RHA content increased, it decreased the 3-day flexural and compressive strengths but improved the mechanical properties on the 90th day. Dense needle-like CSH gels were observed in SEM examinations. With the developed ANN model, it has been determined that the material quantities (RHA and GBFS content) and curing conditions will be predicted with high accuracy for optimum compressive strength. As a result, it has been determined that a compressive strength higher than 120 MPa and a fl

关键词： bayesian algorithm Ground blast furnace slag Recycling Rice husk ash Sustainability

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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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CT perfusion extended window ischemic core estimation: bayesian algorithm versus oscillation index singular value decomposition

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JOURNAL OF NEUROIMAGING 2023年第3期33卷 381-386页

作者： Morelli, Nicola Immovilli, Paolo Giacopazzi, Elena Rota, Eugenia Spallazzi, Marco Zaino, Domenica Colombi, Davide Guidetti, Donata Michieletti, Emanuele Guglielmo da Saliceto Hosp Neurol Unit Piacenza Italy Guglielmo da Saliceto Hosp Radiol Unit Piacenza Italy Hosp Parma Neurol Unit Parma Italy San Giacomo Hosp Novi Ligure Neurol Unit Alessandria Italy Guglielmo da Saliceto Hosp Neurol Unit Via Taverna 49 I-29121 Piacenza Italy Guglielmoda Saliceto Hosp Radiol Unit Via Taverna 49 I-29121 Piacenza Italy

Background and PurposeIschemic core estimation by CT perfusion (CTp) is a diagnostic challenge, mainly because of the intrinsic noise associated with perfusion data. However, an accurate and reliable quantification of the ischemic core is critical in the selection of patients for reperfusion therapies. Our study aimed at assessing the diagnostic accuracy of two different CTp postprocessing algorithms, that is, the bayesian Method and the oscillation index singular value decomposition (oSVD). MethodsAll the consecutive stroke patients studied in the extended time window (>4.5 hours from stroke onset) by CTp and diffusion-weighted imaging (DWI), between October 2019 and December 2021, were enrolled. The agreement between both algorithms and DWI was assessed by the Bland-Altman plot, Wilcoxon signed-rank test, Spearman's rank correlation coefficient, and the intraclass correlation coefficient (ICC). ResultsTwenty-four patients were enrolled (average age: 72 +/- 15 years). The average National Institutes of Health Stroke Scale was 14.42 +/- 6.75, the median Alberta Stroke Program Early CT score was 8.50 (interquartile range [IQR] = 7.75-9), and median time from stroke onset to neuroimaging was 7.5 hours (IQR = 6.5-8). There was an excellent correlation between DWI and oSVD (rho = .87, p-value < .001) and DWI and bayesian algorithm (rho = .94, p-value < .001). There was a stronger ICC between DWI and bayesian algorithm (.97, 95% confidence interval [CI]: .92-.99, p-value < .001) than between DWI and oSVD (.59, 95% CI: .26-.8, p-value < .001). DiscussionThe agreement between bayesian algorithm and DWI was greater than between oSVD and DWI in the extended window. The more accurate estimation of the ischemic core offered by the bayesian algorithm may well play a critical role in the accurate selection of patients for reperfusion therapies.

关键词： bayesian algorithm CT perfusion extended window ischemic core singular value decomposition stroke

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Digital Media Art Design Model Based on Semantic Guided bayesian algorithm 3

Digital Media Art Design Model Based on Semantic Guided Baye...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Jiao, Xiangting Yang, Chunhui Jincheng College of Art and Design Chengde College of Applied Technology Hebei Chengde China City University Malaysia Petaling Jaya Malaysia

ISBN: (纸本)9798350380989

Digital media art design plays an important role in the field of contemporary art, but traditional art design models face the limitations of low accuracy and low stability, and a digital media art design model based on semantically guided bayesian algorithm provides a potential solution. This study uses a digital media art design model based on semantically guided bayesian algorithm as the research object. By experimenting and tuning the algorithm, the article evaluates the computational efficiency, stability, and accuracy of the model. The research results show that the computational efficiency of this method is between 83 % and 92%. The digital media art design model based on semantically guided bayesian algorithm has potential and prospects. It can generate high-quality, expected artistic design works, which can enhance user satisfaction and wide application. © 2024 IEEE.

关键词： Art Design bayesian algorithm Digital Media Semantic Guidance

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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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Concrete Crack Image Recognition System Based on bayesian algorithm 1

Concrete Crack Image Recognition System Based on Bayesian Al...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Xiu, Yu School of Environmental Arts&architectural Engineering Heilongjiang University of Technology China

ISBN: (纸本)9798350316469

Concrete crack detection is an important link in the construction of water conservancy projects, which has a significant impact on dam safety. Therefore, it is particularly important to study a fast, accurate and economical crack identification system. bayesian algorithm is a probabilistic algorithm based on prior information, which has a high recognition accuracy. The article first preprocesses the concrete crack image, and then uses the bayesian algorithm to identify cracks on the processed image. The system can realize real-time and automatic identification of concrete cracks, and can display the identification results through the human-computer interaction interface. The test results show that the highest recognition accuracy of the system can reach 91.6%. This method provides an effective solution for crack detection and monitoring. The high recognition accuracy achieved in the test further verifies the effectiveness of the system. Future research can focus on expanding the system's ability to handle more complex crack patterns and improving the efficiency of its large-scale applications. © 2023 IEEE.

关键词： bayesian algorithm Concrete Crack Human-Computer Interaction Image Recognition

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Application and Practice of bayesian algorithm in Experimental Teaching of Applied Mathematics 3

Application and Practice of Bayesian Algorithm in Experiment...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Hongsong Shandong Polytechnic College Shandong Province Jining City272000 China

ISBN: (纸本)9798350319125

Since the 21st century, people have made great progress in the research of artificial intelligence. As an important method to express uncertain knowledge, bayesian network has become one of the hot spots and important achievements in artificial intelligence research. Building an effective bayesian network structure is also the basis and core of learning and applying bayesian network. This research aims to introduce the application of bayesian algorithm (BA) in mathematics teaching. BA can be used to supplement or replace the standard learning method of mathematics. In order to enable students to learn more effectively, teachers need to know which methods are effective and how to apply these methods to improve students' learning. This paper aims to apply bayesian method to experimental learning environment. The study provides findings, conclusions and recommendations. © 2023 IEEE.

关键词： bayesian algorithm Experimental teaching mathematics

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