Fuzzy similarity measures for detection and classification of defects in CFRP

A Fuzzy Similarity-Based Approach to Classify Numerically Simulated and Experimentally Detected Carbon Fiber-Reinforced Polymer Plate Defects

This paper presents an eddy current approach for testing, estimating, and classifying CFRP plate sub-surface defects, mainly due to delamination, through specific 2D magnetic induction field amplitude maps. These maps, showing marked fuzziness content, require the development of a procedure based on a fuzzy approach being efficiently classified. Since similar defects produce similar maps, we propose a method based on innovative fuzzy similarity formulations. This procedure can collect maps similar to each other in particular defect classes. In addition, a low-cost analysis system, including the probe, has been implemented in hardware. The developed tool can detect and evaluate the extent of surface defects with the same performance as a hardware tool of higher specifications, and it could be fruitfully employed by airline companies to maintain aircraft in compliance with safety standards.

A Feature Extraction Algorithm of Brain Network of Motor Imagination Based on a Directed Transfer Function

Aiming at the feature extraction of left- and right-hand movement imagination EEG signals, this paper proposes a multichannel correlation analysis method and employs the Directed Transfer Function (DTF) to identify the connectivity between different channels of EEG signals, construct a brain network, and extract the characteristics of the network information flow. Since the network information flow identified by DTF can also reflect indirect connectivity of the EEG signal networks, the newly extracted DTF features are incorporated into the traditional AR model parameter features and extend the scope of feature sets. Classifications are carried out through the Support Vector Machine (SVM). The classification results show the enlarged feature set can significantly improve the classification accuracy of the left- and right-hand motor imagery EEG signals compared to the traditional AR feature set. Finally, the EEG signals of 2 channels, 10 channels, and 32 channels were selected for comparing their different effects of classifications. The classification results showed that the multichannel analysis method was more effective. Compared with the parameter features of the traditional AR model, the network information flow features extracted by the DTF method also achieve a higher classification effect, which verifies the effectiveness of the multichannel correlation analysis method.

Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification

Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to the metasubclasses which is used to learn the classifier. How to reduce the error caused by the noncompetent classifiers under diversity big enough is urgent for ECOC classification. The weighted decoding strategy can be used to reduce the error caused by the noncompetence contradiction through relearning the weight coefficient matrix. To this end, a new weighted decoding strategy taking the classifier competence reliability into consideration is presented in this paper, which is suitable for any coding matrix. Support Vector Data Description is applied to compute the distance from an instance to the metasubclasses. The distance reflects the competence reliability and is fused as the weight in the base classifier combination. In so doing, the effect of the competent classifiers on classification is reinforced, while the bias induced by the noncompetent ones is decreased. Reflecting the competence reliability, the weights of classifiers for each instance change dynamically, which accords with the classification practice. The statistical simulations based on benchmark datasets indicate that our proposed algorithm outperforms other methods and provides new thought for solving the noncompetence problem.