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Reliability Assessment for the Solenoid Valve of a High-Speed Train Braking System under small sample size

Chinese Journal of Mechanical Engineering 2018年第3期31卷 189-199页

作者： Jian-Wei Yang Jin-Hai Wang Qiang Huang Ming Zhou Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles Beijing University of Civil Engineering Architecture School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University Locomotive and Car Research Institute China Academy of Railway Science

Reliability assessment of the braking system in a high?speed train under small sample size and zero?failure data is veryimportant for safe operation. Traditional reliability assessment methods are only performed well under conditions of large sample size and complete failure data,which lead to large deviation under conditions of small sample size and zero?failure data. To improve this problem,a new Bayesian method is proposed. Based on the characteristics of the solenoid valve in the braking system of a high?speed train,the modified Weibull distribution is selected to describe the failure rate over the entire lifetime. Based on the assumption of a binomial distribution for the failure probability at censored time,a concave method is employed to obtain the relationships between accumulation failure prob?abilities. A numerical simulation is performed to compare the results of the proposed method with those obtained from maximum likelihood estimation,and to illustrate that the proposed Bayesian model exhibits a better accuracy for the expectation value when the sample size is less than 12. Finally,the robustness of the model is demonstrated by obtaining the reliability indicators for a numerical case involving the solenoid valve of the braking system,which shows that the change in the reliability and failure rate among the di erent hyperparameters is small. The method is provided to avoid misleading of subjective information and improve accuracy of reliability assessment under condi?tions of small sample size and zero?failure data.

关键词： Zero?failure data Modified Weibull distribution small sample size Bayesian method

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Metal Corrosion Rate Prediction of small samples Using an Ensemble Technique

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工程与科学中的计算机建模（英文） 2023年第1期134卷 267-291页

作者： Yang Yang Pengfei Zheng Fanru Zeng Peng Xin Guoxi He Kexi Liao State Key Laboratory of Oil Gas Reservoir Geology and Exploitation Southwest Petroleum UniversityChengdu610500China School of Earth Sciences and Technology Southwest Petroleum UniversityChengdu610500China Spatial Information Technology and Big Data Mining Research Center School of Earth Sciences and TechnologySouthwest Petroleum UniversityChengdu610500China Sichuan Xinyang Anchuang Technology Co. Ltd.Chengdu610500China SichuanWater Conservancy College Chengdu610500China CCDC Safety EnvironmentQuality Supervision&Testing Research InstituteGuanghan618300China

Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline *** this study,a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real *** approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners.A total of 99 data were collected and split into training and test set with a 9:1 *** training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search,and the test set was used to determine the performance of the *** results showed that theMean Absolute Error(MAE)of this framework is 28.06%of the traditional model and outperforms other ***,the proposed framework is suitable formetal corrosion prediction under small sample conditions.

关键词： Oil pipeline bagging KNN ensemble learning small sample size

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Reliability Assessment Method Based on Interference Variable for Stress-Strength Model

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Journal of Donghua University(English Edition) 2015年第6期32卷 1047-1051页

作者：吴琼杨建中王晶燕满剑锋孙国鹏 Institute of Spacecraft System Engineering China Academy of Space Technology College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics

Stress-strength model is a basic and important tool for reliability *** are few methods to assess the confidence limit of interference reliability when the distribution parameters of stress and strength are all unknown.A new assessment method of interference reliability is proposed and the estimates of the distribution parameters are accordingly *** lower confidence limit of interference reliability with given confidence can be obtained with the method even though the parameters are all *** studies and an engineering application are conducted to validate the method,which suggest that the method provides precise estimates even for sample size of approximately.

关键词： interference reliability stress-strength mode(SSM) lower confidence limit of reliability small sample size highly reliable product

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Classifier Design Using Nearest Neighbor samples

Classifier Design Using Nearest Neighbor Samples

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2000年第16届世界计算机大会

作者： Yoshihiro Mitani Yoshihiko Hamamoto Yamaguchi Junior College Hofu747-1232Japan Faculty of Engineering Yamaguchi UniversityUbe755-8611Japan

＜正＞A considerable amount of effort has been devoted to design a classifier in practical *** this paper, a simple nonpaxametric classifier is proposed. The proposed classifier uses nearest neighbor training samples from a pattern to be classified and its performance is compared with that of the k-NN classifier in terms of the error rate,particularly in small training sample size *** results show that the proposed classifier is promising in practical situations.

关键词： classifier design nearest neighbor samples dimensionality small sample size

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A review on medical brain image segmentation methods based on deep learning

A review on medical brain image segmentation methods based o...

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第十七届中国体视学与图像分析学术会议

作者：赵雷阳黄建军 Shenzhen University

Medical brain image segmentation is of great importance for the diagnosis of brain *** the wide application of deep learning in the medical field,brain image segmentation methods based on deep learning have achieved better performance than traditional segmentation *** this review,we review the brain image segmentation methods based on deep learning,introduce brain imaging methods and brain image preprocessing methods from the perspective of the main problems targeted by such methods,such as the existence of insufficient labels,small sample size and complex structure of medical brain image data,summarize and analyze the basic ideas,model structures and technical means of different segmentation methods,and give the advantages and disadvantages of some methods and basic ideas for ***,federated learning is introduced and its application in brain image segmentation is prospected.

关键词： deep learning brain image segmentation insufficient labels small sample size federated learning

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