A novel complexity-based mode feature representation for feature extraction of ship-radiated noise using VMD and slope entropy

Li, Yuxing; Tang, Bingzhao; Yi, Yingmin

doi:10.1016/j.apacoust.2022.108899

Cited by 79 publications

(52 citation statements)

References 31 publications

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“…Once the acoustic signal propagates through the conduit, its waveform may be distorted and attenuated. The intrinsic mode decomposition [ 37 , 38 ] of the acoustic signal reveals that its frequency characteristics generally remain unchanged. By analyzing the instantaneous frequency of the received signal, a new time-frequency relationship can be derived, from which, it can be seen that the offset between the two time-frequency relationships is the acoustic TOF.…”

Section: Principle Of Acoustic Ranging and Thermometrymentioning

confidence: 99%

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

Liu

Zhou

Zhang

et al. 2022

Sensors

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Accurate ranging and real-time temperature monitoring are essential for metrology and safety in electrical conduit applications. This paper proposes an acoustic time-of-flight (TOF) estimation method based on the digital lock-in filtering (DLF) technique for conduit ranging and thermometry. The method establishes the relationship between the frequency and the time domain by applying a linear frequency modulated Chirp signal as the sound source and using the DLF technique to extract the first harmonic of the characteristic frequencies of the transmitted and received signals. Acoustic TOF estimation in the conduit is then achieved by calculating the mathematical expectation of the time difference between each characteristic frequency in the time-frequency relationship of the two signals. The experimental results with enhanced noise interference on different conduit lengths and various temperature conditions, proved that the proposed DLF method can establish a robust linear time-frequency relationship according to the characteristics of the Chirp signal, and the measurement accuracy of TOF has also been confirmed. Compared to the conventional method, the DLF method provides the lowest absolute error and standard deviation for both distance and temperature measurements with an enhanced robustness.

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Section: Principle Of Acoustic Ranging and Thermometrymentioning

confidence: 99%

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

Liu

Zhou

Zhang

et al. 2022

Sensors

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“…As there are many kinds of decomposition algorithms, wavelet decomposition is only one of them, so other decomposition algorithms can be used to verify the effectiveness of DB wavelet decomposition. Considering that the variational modal decomposition (VMD) has a strict mathematical theory and is an adaptive and completely nonrecursive modal variation and signal processing method, the adaptive decomposition of the target signal can be achieved by iteratively searching for the optimal solution of the variational model, the decomposition effect is good, and this algorithm and its improved algorithm have been widely used in many fields [39,40], so the optimal decomposition effect of VMD algorithm is compared with that of the algorithms adopted in this study.…”

Section: Decomposition Of Subsidence Monitoring Datamentioning

confidence: 99%

Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise

Han

2022

Shock and Vibration

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Considering that it is easily disturbed by various engineering factors such as weather, hydrology, and construction during engineering monitoring, the collected subsidence data contain various noises. In order to reduce the influence of engineering noise on the accuracy of subsidence prediction, it is proposed to use the Daubechies (DB) wavelet to decompose the original subsidence time series; the items with the low-frequency trend, after decomposition, are predicted using long short-term memory (LSTM) model, items with high-frequency noise used the autoregressive (AR) time series model to make predictions, and the prediction results of the low-frequency trend term and the high-frequency noise term are summed to obtain the total time series predicted value. Combining the actual engineering subsidence monitoring data of the old goaf, compared with the prediction results of the LSTM and RNN models without DB wavelet decomposition and the gray model GM (1,1), the results show that the DB wavelet has an obvious improvement effect in reducing the influence of measurement data noise on prediction error. Compared with the single prediction model LSTM, RNN, and GM (1,1), the proposed prediction model has higher prediction accuracy, smaller error, and better trend. It can be used as a calculation method to improve the prediction accuracy of surface subsidence in old goaf.

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“…Using the entropy theory [ 2 , 3 ] as a feature to extract the radiation noise of a ship is one of the most common mathematical modeling methods. Additionally, a critical approach to recognition is to analyze the peaks of the spectrum to obtain the physical features, such as the propeller speed cavitation noise of the engine [ 4 , 5 ]. The spectrum will be distorted because of the Doppler effect when the ship moves toward the hydrophone receivers [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-Learning Method with Channel Attention Mechanism for Underwater Target Recognition

Lingzhi

Zeng

Jin

2022

Sensors

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The core of underwater acoustic recognition is to extract the spectral features of targets. The running speed and track of the targets usually result in a Doppler shift, which poses significant challenges for recognizing targets with different Doppler frequencies. This paper proposes deep learning with a channel attention mechanism approach for underwater acoustic recognition. It is based on three crucial designs. Feature structures can obtain high-dimensional underwater acoustic data. The feature extraction model is the most important. First, we develop a ResNet to extract the deep abstraction spectral features of the targets. Then, the channel attention mechanism is introduced in the camResNet to enhance the energy of stable spectral features of residual convolution. This is conducive to subtly represent the inherent characteristics of the targets. Moreover, a feature classification approach based on one-dimensional convolution is applied to recognize targets. We evaluate our approach on challenging data containing four kinds of underwater acoustic targets with different working conditions. Our experiments show that the proposed approach achieves the best recognition accuracy (98.2%) compared with the other approaches. Moreover, the proposed approach is better than the ResNet with a widely used channel attention mechanism for data with different working conditions.

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A novel complexity-based mode feature representation for feature extraction of ship-radiated noise using VMD and slope entropy

Cited by 79 publications

References 31 publications

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise

A Novel Deep-Learning Method with Channel Attention Mechanism for Underwater Target Recognition

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