Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy

Bearings are crucial components in rotating machines,which have direct effects on industrial productivity and safety.To fast and accurately identify the operating condition of bearings,a novel method based on multi⁃scale permutation entropy(MPE)and morphology similarity distance(MSD)is proposed in this paper.Firstly,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization.Then,the MSD was employed to measure the distance among test samples from different fault types and the reference samples,and achieved classification with the minimum MSD.Finally,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,respectively.Different categories were considered for the two experiments and high classification accuracies were obtained.The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.

A generalized evidence distance

How to efficiently measure the distance between two basic probability assignments（BPAs） is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.

KnoE:A Web Mining Tool to Validate Previously Discovered Semantic Correspondences

The problem of matching schemas or ontologies consists of providing corresponding entities in two or more knowledge models that belong to a same domain but have been developed separately. Nowadays there are a lot of techniques and tools for addressing this problem, however, the complex nature of the matching problem make existing solutions for real situations not fully satisfactory. The Google Similarity Distance has appeared recently. Its purpose is to mine knowledge from the Web using the Google search engine in order to semantically compare text expressions. Our work consists of developing a software application for validating results discovered by schema and ontolog2/ matching tools using the philosophy behind this distance. Moreover, we are interested in using not only Google, but other popular search engines with this similarity distance. The results reveal three main facts. Firstly, some web search engines can help us to validate semantic correspondences satisfactorily. Secondly there are significant differences among the web search engines. And thirdly the best results are obtained when using combinations of the web search engines that we have studied.

Social network users clustering based on multivariate time series of emotional behavior

It is known that the social network is an excellent source for gathering the emotions of people. There are thousands of micro-blogs posted in every second and every micro-blog that may contain a variety of user＇s emotions. The users＇ collective emotional behaviors are with great impacts on today＇s societies, so it is good to find groups for society management based on users＇ emotional behavior. This article focuses on analyzing multivariate emotional behavior of users in social network and the goal is to cluster the users from a fully new perspective-emotions. The following tasks are completed： firstly, the multivariate emotion of Chinese micro-blog with vector is analyzed, and multivariate time series to describe the user＇s emotional behavior are constructed. Seconedly, considering principal component analysis （PCA） in similarity and distance similarity, the similarity of the multivariate emotion time series is measured. The contribution could be summarized as follows： groups of users though different emotions in social network are discovered. The emotional fluctuation and intensity of users are considered as well. Experiment in clustering effectively illustrates the emotional behavior characteristics of the Users in different groups.