scholarly journals Survey of Marine Organisms Based on Passive Acoustic Technology

2020 ◽  
Vol 15 (5) ◽  
pp. 729-737
Author(s):  
Gong Chen ◽  
Lei Cai ◽  
Lv Zong ◽  
Yan Wang ◽  
Xin Yuan
Keyword(s):  
Feature Extraction ◽  
Acoustic Signal ◽  
Extraction Method ◽  
Acoustic Signals ◽  
Marine Organisms ◽  
Decomposition Algorithm ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
Fish Detection ◽  
Passive Acoustic

Passive acoustic technology (PAT) is an important tool to acquire the passive acoustic signals from marine organisms. In this paper, PAT fish detection is introduced at great length, including the relevant instruments, signal processing methods, and workflow. Focusing on the key tasks of PAT fish detection, the authors proposed a sparse decomposition algorithm that extracts coherent ratio of passive fish acoustic signal, and designed a feature extraction method for that signal based on speech imitation technology. Experimental results demonstrate that the proposed sparse decomposition algorithm can detect fish acoustic signal accurately at low signal-to-noise ratios (SNRs), and the proposed feature extraction method can effectively extract fish acoustic signals from the marine background. The research results shed important new light on the protection and management of fishery resources in the seas and oceans.

scholarly journals Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Canyi Du ◽  
Fei Jiang ◽  
Kang Ding ◽  
Feng Li ◽  
Feifei Yu
Keyword(s):  
Finite Element ◽  
Feature Extraction ◽  
Finite Element Model ◽  
Extraction Method ◽  
Element Model ◽  
Pass Filter ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
Automotive Engine ◽  
Impulse Function

Engine vibration signals are easy to be interfered by other noise, causing feature signals that represent its operating status get submerged and further leading to difficulty in engine fault diagnosis. In addition, most of the signals utilized to verify the extraction method are derived from numerical simulation, which are far away from the real engine signals. To address these problems, this paper combines the priority of signal sparse decomposition and engine finite element model to research a novel feature extraction method for engine misfire diagnosis. Firstly, in order to highlight resonance regions related with impact features, the vibration signal is performed with a high-pass filter process. Secondly, the dictionary with clear physical meaning is constructed by the unit impulse function, whose parameters are associated with engine system modal characteristics. Afterwards, the signals that indicate the engine operating status are accurately reconstructed by segmental matching pursuit. Finally, a series of precise simulation signals originated from the engine dynamic finite element model, and experimental signals on the automotive engine are used to verify the proposed method’s effectiveness and antinoise performance. Additionally, comparisons with wavelet decomposition further show the proposed method to be more reliable in engine misfire diagnosis.

scholarly journals A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 61 ◽  
Author(s):  
Yuxing Li ◽  
Xiao Chen ◽  
Jing Yu ◽  
Xiaohui Yang
Keyword(s):  
Acoustic Signal ◽  
Extraction Method ◽  
Acoustic Signals ◽  
Permutation Entropy ◽  
Chaotic Oscillator ◽  
Underwater Acoustic ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Underwater Acoustic Signal ◽  
Frequency Feature

In order to effectively extract the frequency characteristics of an underwater acoustic signal under sensor measurement, a fusion frequency feature extraction method for an underwater acoustic signal is presented based on variational mode decomposition (VMD), duffing chaotic oscillator (DCO) and a kind of permutation entropy (PE). Firstly, VMD decomposes the complex multi-component underwater acoustic signal into a set of intrinsic mode functions (IMFs), so as to extract the estimated center frequency of each IMF. Secondly, the frequency of the line spectrum can be obtained by using DCO and a kind of PE (KPE). DCO is used to detect the actual frequency of the line spectrum for each IMF and KPE can determine the accurate frequency when the phase space track is in the great periodic state. Finally, the frequency characteristic parameters acted as the input of the support vector machine (SVM) to distinguish different types of underwater acoustic signals. By comparing with the other three traditional methods for simulation signal and different kinds of underwater acoustic signals, the results show that the proposed method can accurately extract the frequency characteristics and effectively realize the classification and recognition for the underwater acoustic signal.

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