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In recent years, many alternative methodologies and techniques have been proposed to perform nondestructive inspection and maintenance operations of moving structures. In particular, ultrasonic techniques have shown to be very promising for automatic inspection systems. From the literature, it is evident that the neural paradigms are considered, by now, the best choice to automatically classify ultrasound data. At the same time, the most appropriate preprocessing technique is still undecided. The aim of this paper is to propose a new and innovative data preprocessing technique that converts real-valued ultrasonic data into complex-valued signals. This allows analysis using phase synchrony, a robust tool that has been previously employed in brain science for establishing robust features in noisy data. Synchrony estimation is achieved using complex extensions of empirical mode decomposition, a data-driven algorithm for detecting temporal scales, thus facilitating the modeling of nonlinear and nonstationary signal dynamics. Experimental tests aiming to detect defective areas in composite materials are reported, and the effectiveness of the proposed methodology is illustrated. [ABSTRACT FROM PUBLISHER]

In this study, seventeen samples were created for classifying internal, surface, and corona partial discharges (PDs) in a high voltage lab. Next, PDs were measured experimentally to provide a dictionary comprising the types. Due to the huge size of the recorded dataset, a new and straightforward preprocessing method based on signal norms was used to extract the appropriate features of various samples. The new sparse representation classifier (SRC) was computed using ?1 and stable ?1-norm minimization by means of Primal-Dual Interior Point (PDIP) and Basis Pursuit De-noise (BPDN) algorithms, respectively. The pattern recognition was also performed with an artificial neural network (ANN) and compared with the sparse method. It is shown that both methods have comparable performance if training process, tuning options, and other tasks for finding the best result from ANN are not taken into account. Even with this assumption, it is shown that SRC still performs better than ANN in some cases. In addition, the SRC technique presented in this paper converges to a fixed result, while the results after training the ANN vary with every run due to random initial weights. [ABSTRACT FROM AUTHOR]

Dimensionality reduction and incremental learning have recently received broad attention in many applications of data mining, pattern recognition, and information retrieval. Inspired by the concept of manifold learning, many discriminant embedding techniques have been introduced to seek low-dimensional discriminative manifold structure in the high-dimensional space for feature reduction and classification. However, such graph-embedding framework-based subspace methods usually confront two limitations: 1) since there is no available updating rule for local discriminant analysis with the additive data, it is difficult to design incremental learning algorithm and 2) the small sample size (SSS) problem usually occurs if the original data exist in very high-dimensional space. To overcome these problems, this paper devises a supervised learning method, called local discriminant subspace embedding (LDSE), to extract discriminative features. Then, the incremental-mode algorithm, incremental LDSE (ILDSE), is proposed to learn the local discriminant subspace with the newly inserted data, which applies incremental learning extension to the batch LDSE algorithm by employing the idea of singular value-decomposition (SVD) updating algorithm. Furthermore, the SSS problem is avoided in our method for the high-dimensional data and the benchmark incremental learning experiments on face recognition show that ILDSE bears much less computational cost compared with the batch algorithm. [ABSTRACT FROM PUBLISHER]

A maritime automatic target recognition system is developed that performs ship classification using one-dimensional high resolution range profiles. Five physically based features are defined and are extracted from both VV and HH polarizations resulting in a 10-dimensional feature vector. A nonlinear classifier combination approach involving a neural network combiner along with three individual classifiers (Bayes, nearest neighbor, and neural network) is proposed. A decision confidence measure based on the classifier discriminants is developed using a nonparametric estimation approach. The confidence measure enables the system to reject samples that have a low decision confidence. The performance of the proposed neural network based combination is compared with individual classifiers and a number of other combination rules. The results show that this combination can provide high recognition accuracy along with a high probability of declaration. The performance in the presence of samples from not-before-seen classes is also investigated. A new nearest neighbor confidence thresholding approach is developed to aid the neural network combiner in rejecting these samples. [ABSTRACT FROM AUTHOR]