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Fuzzy c-means clustering is introduced to fuzzify the continuous attributes of fault features in an attempt to decline information loss during the course of discretization. Clustering valid analysis is utilized to obtain the optimal number of clusters, and by this way, the shortcoming of current approaches that number of clusters need to be determined artificially is overcome. Experiments of fault...
Acoustic wave signals, radiated from moving vehicles, can be used for automatic vehicle classification as an effective source of information. Acoustic wave signals are processed by self-correlation analysis in frequency domain based on Welch spectrum estimation. Original feature vectors of the linear power spectrum are obtained. Principal component analysis (PCA), aiming to reduce data dimension,...
An approach of power spectrum estimation is utilized to extract the feature vectors from acoustic signal radiated from different types of moving vehicles. A method of feature selection based on principal component analysis (PCA) is proposed to reconstruct effective feature vectors via dimension reduction. The classification of three typical targets is achieved by supported vector machine (SVM). Experiment...
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