Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, D...
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Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, DC serial arc faults are more and more dangerous to battery system. Therefore, a binary classification model based on machine learning algorithm was proposed to detect DC serial arc faults effectively in this study. It was optimised according to the characteristic signals of the arc to be satisfied with different loads for higher detection accuracy and robustness. In the simulative experiments for the power system electric vehicle, while the loads changing to the motor, the resistor or the inverter, it will all reach a highly successful detection rate, respectively.
In recent years, Convolutional Neural Network (CNN) has achieved a great success in computer vision. However, at present, for an image classification task, there is no CNN model that can perform 100% accurately due to...
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ISBN:
(纸本)9798350319910
In recent years, Convolutional Neural Network (CNN) has achieved a great success in computer vision. However, at present, for an image classification task, there is no CNN model that can perform 100% accurately due to insufficient or excessive feature learning. Once a CNN model deployed to perform tasks online, misclassified samples might lead the system with the CNN model deployed to enter an unsafe state such as collisions. To assess the performance of such online models, we, in this paper, propose Parallel Signal Routing Paths (PSRP) method to identify misclassified samples by extracting execution paths for each sample and comparing inherent feature differences in terms of CNN nodes between misclassified and well-classified samples, for the ultimate aim of addressing the challenge of test data not having ground-truth labels in online environment where the CNN models are deployed, and give availability results for applying PSRP on 3 public datasets and 3 typical CNN models.
作者：
Han, HuilianHan, HuizhiJilin Univ
Northeast Asian Studies Coll 2699Qianjin St Changchun 130012 Jilin Peoples R China Heilongjiang Minzu Coll
1 Hanan 15th Rd Core Area Hanan Ind New Town Harbin 150066 Heilongjiang Peoples R China
This study attempts to show the influencing factors that affect the residence intention of the ethnic minority floating population, explores the ways to promote their citizenization, improves the "quality" o...
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This study attempts to show the influencing factors that affect the residence intention of the ethnic minority floating population, explores the ways to promote their citizenization, improves the "quality" of the new type of urbanization, and achieves communication and integration of all ethnic groups in the city. Results show that the male's intention is higher than that of the female, a stable marriage relationship is beneficial to the intention to stay in the city, and the Hukou system still exerts a significant influence on the residential intention of the population. Signing a labor contract has a positive impact on the intention to stay in the city, which is stronger among the minority floating population in the province than that of inter-provincial migration population. Social interaction and psychological adaptation further strengthen the intention of the minority floating population to stay in the city. Therefore, in promoting the urbanization of minority floating population, efforts are needed to deepen the household registration system, improve the level of social security and pay more attention to social interaction and psychological adaptation, so as to forge a strong sense of community among the Chinese people.
In this research, we conducted a preprocessing step on pure-tone audiometry image data to determine the presence or absence of hearing loss in patients, and designed machine learning models for hearing loss classifica...
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ISBN:
(纸本)9798350367164;9798350367157
In this research, we conducted a preprocessing step on pure-tone audiometry image data to determine the presence or absence of hearing loss in patients, and designed machine learning models for hearing loss classification using the preprocessed data. The dataset utilized consisted of a total of 18,530 data (9,265 normal, 9,265 hearing loss), and detailed parameter configuration was performed using randomly extracted training and validation data. This research involved converting image data into a CSV file format during the preprocessing stage, and the preprocessed data was then utilized to design and construct the Logistic Regression and Decision Tree Classifier machine learning models for patient classification. These models achieved an accuracy of 85.61% and 85.25%, respectively, in automatically determining the presence or absence of hearing loss.
Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct ...
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Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.
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