移动对象的语义行为模式挖掘是当前移动对象研究中关注的热点,有益于诸多应用场景,如朋友推荐系统、轨迹破案领域和个性化服务等.目前语义行为模式挖掘方法没有考虑移动对象在停留点的停留时间,不能准确地分辨出移动对象之间的不同行为...移动对象的语义行为模式挖掘是当前移动对象研究中关注的热点,有益于诸多应用场景,如朋友推荐系统、轨迹破案领域和个性化服务等.目前语义行为模式挖掘方法没有考虑移动对象在停留点的停留时间,不能准确地分辨出移动对象之间的不同行为模式.为了解决上述问题,提出了一种基于停留时间的语义行为模式挖掘(discovering common behavior using staying duration on semantic trajectory,DSTra)方法,首先挖掘每个移动对象的频繁语义行为模式,然后定义语义行为模式之间的相似性度量方法,最后采用层次聚类的方法对移动对象进行聚类,找出具有相似行为模式的移动对象群体.实验结果表明:该方法不仅具有合理性和有效性,同时还具有较高的准确率和较好的效率.展开更多
A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,wher...A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.展开更多
文摘移动对象的语义行为模式挖掘是当前移动对象研究中关注的热点,有益于诸多应用场景,如朋友推荐系统、轨迹破案领域和个性化服务等.目前语义行为模式挖掘方法没有考虑移动对象在停留点的停留时间,不能准确地分辨出移动对象之间的不同行为模式.为了解决上述问题,提出了一种基于停留时间的语义行为模式挖掘(discovering common behavior using staying duration on semantic trajectory,DSTra)方法,首先挖掘每个移动对象的频繁语义行为模式,然后定义语义行为模式之间的相似性度量方法,最后采用层次聚类的方法对移动对象进行聚类,找出具有相似行为模式的移动对象群体.实验结果表明:该方法不仅具有合理性和有效性,同时还具有较高的准确率和较好的效率.
基金Projects(61273163,61325015,61304121)supported by the National Natural Science Foundation of China
文摘A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.
