A multi-level feature fusion artificial neural network for classification of acoustic emission signals.

In this paper, we introduce FUSION-ANN, a novel artificial neural network (ANN) designed for acoustic emission (AE) signal classification. FUSION-ANN comprises four distinct ANN branches, each housing an independent multilayer perceptron. We extract denoised features of speech recognition such as linear predictive coding, Mel-frequency cepstral coefficient, and gammatone cepstral coefficient to represent AE signals.

Development of a Sandwiched Piezoelectric Accelerometer for Low-Frequency and Wide-Band Seismic Exploration.

A sandwiched piezoelectric accelerometer is developed and optimized for acquiring low-frequency, wide-band seismic data. The proposed accelerometer addresses the challenges of capturing seismic signals in the low-frequency range while maintaining a broad frequency response through the design of multi-stage charge amplifiers and a sandwiched structure. The device's design, fabrication process, and performance evaluation are discussed in detail.

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications.

Seismic Facies Analysis Using the Multiattribute SOM-K-Means Clustering.

An accurate seismic facies analysis (SFA) can provide insight into the subsurface sedimentary facies and has guiding significance for geological exploration. Many machine learning algorithms, including unsupervised, supervised, and deep learning algorithms, have been developed successfully for SFA over the past decades. However, SFA and facies classification are still challenging tasks due to the complex characteristics of geological and seismic data.