This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and desi...This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format.Subsequently,a remote data transmission link using 4G signals and data processing methods is introduced.The advantages of remote diagnosis are analyzed,and common methods such as correlation analysis,fault diagnosis,and fault prediction are explained in detail.Then,challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed,along with development prospects in technological innovation,algorithm optimization,and application promotion.This research provides ideas for remote analysis and diagnosis based on TCMS data,contributing to the safe and efficient operation of rail vehicles.展开更多
为了将列车数据应用到实际运营工作中,本文设计了一种车辆分析系统。该系统实现了对网络控制系统(Train Control and Management System,TCMS)数据的接收、校验、存储、计算、展示等全生命周期管理,并将数据用于分析统计、故障预警等,...为了将列车数据应用到实际运营工作中,本文设计了一种车辆分析系统。该系统实现了对网络控制系统(Train Control and Management System,TCMS)数据的接收、校验、存储、计算、展示等全生命周期管理,并将数据用于分析统计、故障预警等,为地面运营工作提供辅助决策支撑。展开更多
目的探究卵巢储备功能低下(decline in ovarian reserbe,DOR)患者的中医体质、中医证型分布特点及吴克明教授临床治疗的用药规律。方法遵循流行病学调查方法,采用《中医体质分类与判定表》对DOR患者进行打分和体质分类,参考《中医妇科...目的探究卵巢储备功能低下(decline in ovarian reserbe,DOR)患者的中医体质、中医证型分布特点及吴克明教授临床治疗的用药规律。方法遵循流行病学调查方法,采用《中医体质分类与判定表》对DOR患者进行打分和体质分类,参考《中医妇科常见病诊疗指南》及《中医妇科学》中“不孕症”“月经病”等证候分类,进行辨证分型,并收集吴克明教授门诊治疗DOR的中药处方信息。采用中医传承辅助平台(V2.5)进行挖掘分析相关用药规律。结果在所收集124例DOR患者的体质信息中,阳虚质36例,占29.03%;血瘀质19例,占15.32%;平和质16例,占12.9%;而气郁质、阴虚质、气虚质等占较少比例。中医证型分类中肾虚血瘀证共48例、脾肾阳虚证27例、肝郁肾虚证22例、气血两虚证14例、肝肾阴虚证13例。使用中医传承辅助平台对124例门诊处方进行数据挖掘,得出核心组方为:肉苁蓉、菟丝子、淫羊藿、枸杞子、覆盆子、黄精、当归、山萸肉、熟地黄、牡丹皮、香附。结论通过中医辅助平台对临床收集124例DOR患者的体质类型及其中药处方进行分析,阳虚质、血瘀质和肾虚血瘀证、脾肾阳虚证患者占比最多,而核心组方药物主要为肉苁蓉、菟丝子、淫羊藿、当归等,其中肉苁蓉、菟丝子、淫羊藿补肾精温肾阳,当归养血活血调经,初步揭示了吴克明教授在补肾养血活血法指导下临床治疗DOR的用药规律,并为不同体质类型、中医证型分类下DOR的临床治疗提供一定参考依据。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
文摘This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format.Subsequently,a remote data transmission link using 4G signals and data processing methods is introduced.The advantages of remote diagnosis are analyzed,and common methods such as correlation analysis,fault diagnosis,and fault prediction are explained in detail.Then,challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed,along with development prospects in technological innovation,algorithm optimization,and application promotion.This research provides ideas for remote analysis and diagnosis based on TCMS data,contributing to the safe and efficient operation of rail vehicles.
文摘目的探究卵巢储备功能低下(decline in ovarian reserbe,DOR)患者的中医体质、中医证型分布特点及吴克明教授临床治疗的用药规律。方法遵循流行病学调查方法,采用《中医体质分类与判定表》对DOR患者进行打分和体质分类,参考《中医妇科常见病诊疗指南》及《中医妇科学》中“不孕症”“月经病”等证候分类,进行辨证分型,并收集吴克明教授门诊治疗DOR的中药处方信息。采用中医传承辅助平台(V2.5)进行挖掘分析相关用药规律。结果在所收集124例DOR患者的体质信息中,阳虚质36例,占29.03%;血瘀质19例,占15.32%;平和质16例,占12.9%;而气郁质、阴虚质、气虚质等占较少比例。中医证型分类中肾虚血瘀证共48例、脾肾阳虚证27例、肝郁肾虚证22例、气血两虚证14例、肝肾阴虚证13例。使用中医传承辅助平台对124例门诊处方进行数据挖掘,得出核心组方为:肉苁蓉、菟丝子、淫羊藿、枸杞子、覆盆子、黄精、当归、山萸肉、熟地黄、牡丹皮、香附。结论通过中医辅助平台对临床收集124例DOR患者的体质类型及其中药处方进行分析,阳虚质、血瘀质和肾虚血瘀证、脾肾阳虚证患者占比最多,而核心组方药物主要为肉苁蓉、菟丝子、淫羊藿、当归等,其中肉苁蓉、菟丝子、淫羊藿补肾精温肾阳,当归养血活血调经,初步揭示了吴克明教授在补肾养血活血法指导下临床治疗DOR的用药规律,并为不同体质类型、中医证型分类下DOR的临床治疗提供一定参考依据。
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.
