Fault detection of Substation Power Transformer by Non-contact measurement is important for the safety of machines, instruments, and human beings. To make non-contact measurement as convenient as possible, it is desirable that efficient algorithms based on AE (acoustic emission) discrimination are developed. This paper presents a system for quick and effective fault detection of substation power transformer, based on AE signals collected by non-contact single microphones. In the experiment, collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (Convolutional Neural Network (CNN), support vector machine (SVM), and k-nearest neighbors (KNN) algorithm) trained and tested by a tenfold cross-validation technique. After comparison among the designed classifiers, the results show the two-dimensional principal component analysis (2DPCA) preprocess combined with SVM achieved the best comprehensive effectiveness.

The sound field distribution in the vocal tract with a single sound source in the glottis and the transfer function of the supraglottal vocal tract of the horseshoe bat, Rhinolophus pusillus, have been obtained using the finite-element method (FEM) technique. The models of vocal tracts used for FEM calculation are constructed by tomography scanning. These models are used to set up a finite-element model for calculating the sound field distribution by loading the unit sound source in the glottis. By changing the frequency of the unit sound source, the frequency response was figured out and the acoustic role of vocal tract chambers was examined by obtaining the transfer function and sound pressure distribution before and after filling the chambers using voxels. Sound pressures in the trachea and nostrils are recorded and some analysis of the acoustics of the subglottal and vocal tract was made to find the function of the construction in the vocal tract and subglottal parts. The results show nasal chambers can effectively improve the Q (quality factor) value near the second harmonic, and alternate the sound distribution in the supraglottal part. Whereas the tracheal chambers can reduce the amplitude second harmonic in the subglottal part, its function is like a notch filter which can block the second harmonic component of the back propagation sound under the glottis.

Manifold learning is a nonlinear data dimension reduction method. It can look for the essence of things from the observed phenomena, and find the inherent law of data. Traditional MFCC feature will lead a slower learning speed on account of it has high dimension and useless noise. Therefore, a speech feature extraction method based on manifold learning is proposed. Firstly, we use the manifold learning dimension reduction algorithm for the dimension reduction of Mel features and then for vowels classification. In order to further demonstrate the effectiveness of manifold learning feature in speech recognition, we propose a fusion speech feature extraction method and apply it to the identification of Chinese isolated words. Experiments prove that the fusion feature extraction method has achieved a better result than that of traditional MFCC feature extraction method.

In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy. However, it is unrealistic to increase the size of the image patch size without restriction. Arbitrarily extending the patch size will change the local stereo matching method into the global stereo matching method, and the matching accuracy will be saturated. We simplified the existing Siamese convolutional network by reducing the number of network parameters and propose an efficient CNN based structure, namely adaptive deconvolution-based disparity matching net (ADSM net) by adding deconvolution layers to learn how to enlarge the size of input feature map for the following convolution layers. Experimental results on the KITTI2012 and 2015 datasets demonstrate that the proposed method can achieve a good trade-off between accuracy and complexity.

Echolocating bats locate a target by sonar. The performance of this system is related to the shape of the binaural conformation in bats. From numerical predictions, it was found that in a central frequency band, the orientation of a strong sidelobe is shifted nearly linearly in the vertical direction. Inspired by this, the authors built an accurate wide-scope elevation estimation system by constructing a pair of erect artificial pinnae and realized simultaneous elevation and azimuth estimation by constructing a pair of orthogonal pinnae. By demonstrating the simplicity of spatial target echolocation, the authors showed that only two independent single-output neural networks were needed for either elevation or azimuth estimation. This method could be applied to imitate any other mammal species with similar pinna directivity patterns to facilitate and improve an artificial echolocation system.