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 ...
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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.
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 sp...
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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.
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 ...
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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
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...
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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...
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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.
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 o...
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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...
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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.
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 econ...
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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...
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To scientifically and reasonably assess the development level of China's new power system, this study first establishes an evaluation index system, with the main dimensions being "safety and reliability, flex...
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ISBN:
(纸本)9798350303896
To scientifically and reasonably assess the development level of China's new power system, this study first establishes an evaluation index system, with the main dimensions being "safety and reliability, flexibility and efficiency, friendly interaction, and low-carbon economy", comprising 11 primary indicators and 45 secondary indicators. The core of this research lies in the application of the comprehensive evaluation algorithm of the cloud model improved by the bayesian algorithm. Through the entropy weight-order relation method, specific weights are assigned to the index system. Using data from a certain province in China from 2021 as an example, the improved cloud model successfully determined the province's development level as "good to excellent" and proposed strategies for indicators with lower development levels. This validates the effectiveness and superiority of the cloud model improved by the bayesian algorithm in evaluating the development level of the new power system.
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