Application of Affinity Propagation Clustering Algorithm in Fault Diagnosis of Metro Vehicle Auxiliary Inverter

2014 ◽  
pp. 3-9
Author(s):  
Junwei Gao ◽  
Zengtao Ma ◽  
Yong Qin ◽  
Limin Jia ◽  
Dechen Yao
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Affinity Propagation Clustering

Feature Selection of Sudden Failure Based on Affinity Propagation Clustering

2012 ◽  
Vol 586 ◽  
pp. 241-246
Author(s):  
Li Min Li ◽  
Zhong Sheng Wang
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Feature Space ◽  
Failure Diagnosis ◽  
Affinity Propagation ◽  
Svm Classifier ◽  
Incorrect Answer ◽  
Sudden Failure ◽  
Affinity Propagation Clustering ◽  
Selection Of

When diagnosing sudden mechanical failure, in order to make the result of classification more accurate, in this article we describe an affinity propagation clustering algorithm for feature selection of sudden machinery failure diagnosis. General methods of feature selection select features by reducing dimension of the features, at the same time changing the data in the feature space, which would result in incorrect answer to the diagnosis. While affinity propagation method is based on measuring similarity between features whereby redundancy therein is removed, and selecting the exemplar subset of features, while doesn't change the data in the feature space. After testing on clustering and taking the result of PCA and affinity propagation clustering as input of a same SVM classifier, we get the conclusion that the latter has lower error than the former.

scholarly journals A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering

2020 ◽  
Author(s):  
Sayed Moustafa ◽  
Farhan Khan ◽  
Mohamed Metwaly ◽  
Eslam A.Elawadi ◽  
Nassir Al-Arifi
Keyword(s):  
Urban Areas ◽  
Clustering Algorithm ◽  
Site Response ◽  
Seismic Hazard Assessment ◽  
Spectral Ratio ◽  
Site Effect ◽  
Affinity Propagation ◽  
Site Classification ◽  
Site Specific ◽  
Affinity Propagation Clustering

Abstract Investigations made to evaluate the site effect characteristics and develop a reliable site classification scheme have received the paramount importance for the planning of urban areas and for a reliable site-specific seismic hazard assessment. This paper presents a new approach for site classification based on affinity propagation (AP) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves inside King Saud University (KSU) campus. Measurements of the ambient vibrations were performed to cover the entire campus area by about 307 stations with 20 minutes recording length and sample rate of 128 Hz for each station to satisfy the criteria for reliable and unambiguous HVSR results. Predominant period values were used for identifying of site response and subsequent site classification. Empirical equations from the literature relating frequency of HVSR peak to average shear wave velocity in the upper 30m, commonly used as a proxy for site classification, were found to be unreliable, making site classification difficult. To overcome this problem, Affinity propagation clustering algorithm is used. The obtained results illustrated that microtremors spectral ratios can be remarkably robust tool in determining site effects. The survey results concluded to the preliminary seismic site classification map for the mapped area, which would be useful for future safe design of structures. Finally, the results presented in this study are encouraging prolongation of this type of study in other parts of Saudi Arabia using the microtremors data and site response functions.

Map/Reduce Affinity Propagation Clustering Algorithm

2014 ◽  
Vol 3 (4) ◽  
Author(s):  
Wei-Chih Hung ◽  
Chun-Yen Chu ◽  
Yi-Leh Wu ◽  
Cheng-Yuan Tang
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Map Reduce ◽  
Affinity Propagation Clustering
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