在目前经典的变化检测算法中,后验概率空间变化向量分析(CVAPS)方法广泛用于遥感影像的变化检测。然而,基于支持向量机(SVM)的CVAPS法无法有效处理高分一号影像中等分辨率遥感影像中的混合像元问题,且难以有效保证变化检测的精度。因此...在目前经典的变化检测算法中,后验概率空间变化向量分析(CVAPS)方法广泛用于遥感影像的变化检测。然而,基于支持向量机(SVM)的CVAPS法无法有效处理高分一号影像中等分辨率遥感影像中的混合像元问题,且难以有效保证变化检测的精度。因此,本文通过引入空间信息,使用空间模糊C均值聚类(Spatial Fuzzy C Means, SFCM)有效地实现高分一号影像混合像元的分解,并结合简单贝叶斯网络(SBN),提出一种新的后验概率空间变化向量分析法SFCM-SBN-CVAPS。实验结果表明,本文算法的总体精度和Kappa系数均高于基于普通模糊C均值聚类(Fuzzy C Means, FCM)的CVAPS算法,且耗时更短,本文所提出的算法有助于提高遥感影像变化检测的精度和效率。展开更多
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Con...To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.展开更多
For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring st...For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.展开更多
文摘在目前经典的变化检测算法中,后验概率空间变化向量分析(CVAPS)方法广泛用于遥感影像的变化检测。然而,基于支持向量机(SVM)的CVAPS法无法有效处理高分一号影像中等分辨率遥感影像中的混合像元问题,且难以有效保证变化检测的精度。因此,本文通过引入空间信息,使用空间模糊C均值聚类(Spatial Fuzzy C Means, SFCM)有效地实现高分一号影像混合像元的分解,并结合简单贝叶斯网络(SBN),提出一种新的后验概率空间变化向量分析法SFCM-SBN-CVAPS。实验结果表明,本文算法的总体精度和Kappa系数均高于基于普通模糊C均值聚类(Fuzzy C Means, FCM)的CVAPS算法,且耗时更短,本文所提出的算法有助于提高遥感影像变化检测的精度和效率。
基金The National Natural Science Foundation of China(No60672056)Open Fund of MOE-MS Key Laboratory of Multime-dia Computing and Communication(No06120809)
文摘To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.
基金Supported by the National Natural Science Foundation of China (61074079)Shanghai Leading Academic Discipline Project (B054)
文摘For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.
