Presents the optimal preventive maintenance model established with the target function given through technical economic analysis, and failure rate and delay time distribution estimated from subjective data, which desc...Presents the optimal preventive maintenance model established with the target function given through technical economic analysis, and failure rate and delay time distribution estimated from subjective data, which describes the relationship between the total downtime and the preventive maintenance and can be used to determine the rational inspection interval and to minimize the total expected downtime per unit time.展开更多
In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the...In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.展开更多
联合国可持续发展目标(Sustainable Development Goals,SDGs)对全球、全行业的发展带来深远影响,也为高校图书馆学科服务带来了新的发展机遇。开展与SDGs相关的学科服务,尤其是学科数据分析工作,有助于图书馆数字资源的深度利用,进而推...联合国可持续发展目标(Sustainable Development Goals,SDGs)对全球、全行业的发展带来深远影响,也为高校图书馆学科服务带来了新的发展机遇。开展与SDGs相关的学科服务,尤其是学科数据分析工作,有助于图书馆数字资源的深度利用,进而推动图书馆事业的发展。文章概述了图书馆开展学科数据分析服务的现状和困境,阐明并分析了图书馆开展基于SDGs的学科数据分析服务的价值及其组成要素,并提供了天津师范大学图书馆的服务实践。图书馆开展面向联合国SDGs的数据分析工作,顺应了国家政策导向,符合当前科学活动的发展趋势,促进了图书馆学科数据分析服务能力的提升。展开更多
When longitudinal data contains outliers, the classical least-squares approach is known to be not robust. To solve this issue, the exponential squared loss (ESL) function with a tuning parameter has been investigated ...When longitudinal data contains outliers, the classical least-squares approach is known to be not robust. To solve this issue, the exponential squared loss (ESL) function with a tuning parameter has been investigated for longitudinal data. However, to our knowledge, there is no paper to investigate the robust estimation procedure against outliers within the framework of mean-covariance regression analysis for longitudinal data using the ESL function. In this paper, we propose a robust estimation approach for the model parameters of the mean and generalized autoregressive parameters with longitudinal data based on the ESL function. The proposed estimators can be shown to be asymptotically normal under certain conditions. Moreover, we develop an iteratively reweighted least squares (IRLS) algorithm to calculate the parameter estimates, and the balance between the robustness and efficiency can be achieved by choosing appropriate data adaptive tuning parameters. Simulation studies and real data analysis are carried out to illustrate the finite sample performance of the proposed approach.展开更多
文摘Presents the optimal preventive maintenance model established with the target function given through technical economic analysis, and failure rate and delay time distribution estimated from subjective data, which describes the relationship between the total downtime and the preventive maintenance and can be used to determine the rational inspection interval and to minimize the total expected downtime per unit time.
文摘In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.
文摘联合国可持续发展目标(Sustainable Development Goals,SDGs)对全球、全行业的发展带来深远影响,也为高校图书馆学科服务带来了新的发展机遇。开展与SDGs相关的学科服务,尤其是学科数据分析工作,有助于图书馆数字资源的深度利用,进而推动图书馆事业的发展。文章概述了图书馆开展学科数据分析服务的现状和困境,阐明并分析了图书馆开展基于SDGs的学科数据分析服务的价值及其组成要素,并提供了天津师范大学图书馆的服务实践。图书馆开展面向联合国SDGs的数据分析工作,顺应了国家政策导向,符合当前科学活动的发展趋势,促进了图书馆学科数据分析服务能力的提升。
文摘When longitudinal data contains outliers, the classical least-squares approach is known to be not robust. To solve this issue, the exponential squared loss (ESL) function with a tuning parameter has been investigated for longitudinal data. However, to our knowledge, there is no paper to investigate the robust estimation procedure against outliers within the framework of mean-covariance regression analysis for longitudinal data using the ESL function. In this paper, we propose a robust estimation approach for the model parameters of the mean and generalized autoregressive parameters with longitudinal data based on the ESL function. The proposed estimators can be shown to be asymptotically normal under certain conditions. Moreover, we develop an iteratively reweighted least squares (IRLS) algorithm to calculate the parameter estimates, and the balance between the robustness and efficiency can be achieved by choosing appropriate data adaptive tuning parameters. Simulation studies and real data analysis are carried out to illustrate the finite sample performance of the proposed approach.
