• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1149-1153
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• 2000

All critical equipment installed aboard naval ships and submarines is required to be shock-qualified by tests on the MIL-S-901 shock test machines where testing is practical. The intent of the shock requirements is to produce combat vessels which are resistant to the underwater explosion weapon attack. To efficiently design equipment for passing a series of shock tests, the shock environment of the shock test machine should be clearly identified. In this paper, the shock characteristics of the MIL-S-901 Medium Weight Shock Machine(MWSM) are reviewed, based on the existing test data. An analytical model for the MWSM is also discussed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.930-938
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• 2018

Underwater noise radiated from submarines is directly related to the probability of being detected by the sonar of an enemy vessel. Therefore, minimizing the noise of a submarine is essential for improving survival outcomes. For modern submarines, as the speed and size of a submarine increase and noise reduction technology is developed, interest in flow noise around the hull has been increasing. In this study, a noise analysis technique was developed to predict flow noise generated around a submarine shape considering the free surface effect. When a submarine is operated near a free surface, turbulence-induced noise due to the turbulence of the flow and bubble noise from breaking waves arise. First, to analyze the flow around a submarine, VOF-based incompressible two-phase flow analysis was performed to derive flow field data and the shape of the free surface around the submarine. Turbulence-induced noise was analyzed by applying permeable FW-H, which is an acoustic analogy technique. Bubble noise was derived through a noise model for breaking waves based on the turbulent kinetic energy distribution results obtained from the CFD results. The analysis method developed was verified by comparison with experimental results for a submarine model measured in a Large Cavitation Tunnel (LCT).

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.765-771
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• 2019

With the increase in the speed of ships and the size of ocean structures, the importance of flow noise has become increasingly critical in meeting regulatory standards. However, unlike active investigations in aeroacoustics fields for airplanes and trains, which are based on acoustic analogy methods for tonal and broadband frequency noise, only the discrete blade passing frequency noise from propellers is considered in marine fields. In this study, prediction methods for broadband noise in marine propellers and underwater appendages are investigated using FW-H Formulation1B, which can consider the mechanism of primary noise generation of trailing edge noise. The original FW-H Formulation 1B is based on the pressure correlation function tolackitsgeneralityandaccuracy. To overcome these limitations, wall-pressure spectrum models are adopted to improve the generality in fluid mediums. The comparison of the experimental results obtained in air reveals that the proposed model exhibits a higher accuracy within 5 dB. Furthermore, the prediction procedures for broadband noise for hydrofoils are established, and the estimation of broadband noise is conducted based on the results of the computational fluid dynamics.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.73-81
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• 2018

The quality of side scan sonar images is determined by the frequency of a sonar. A side scan sonar with a low frequency creates low-quality images. One of the factors that lead to low quality is a high-level noise. The noise is occurred by the underwater environment such as equipment noise, signal interference and so on. In addition, in order to compensate for the transmission loss of sonar signals, the received signal is recovered by TVG (Time-Varied Gain), and consequently the side scan sonar images contain non-homogeneous noise which is opposite to optic images whose noise is assumed as homogeneous noise. In this paper, the SSCS (Structural Sparsity based Compressive Sensing) is proposed for removing non-homogeneous noise. The algorithm incorporates both local and non-local models in a structural feature domain so that it guarantees the sparsity and enhances the property of non-local self-similarity. Moreover, the non-local model is corrected in consideration of non-homogeneity of noises. Various experimental results show that the proposed algorithm is superior to existing method.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.455-471
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• 2013

Active sonar has been commonly used to detect targets existing in the shallow water. When a signal is transmitted and returned back from a target, it has been distorted by various properties of acoustic channel such as multipath arrivals, scattering from rough sea surface and ocean bottom, and refraction by sound speed structure, which makes target detection difficult. It is therefore necessary to consider these channel properties in the target signal simulation in operational performance system of active sonar. In this paper, a monostatic active sonar system is considered, and the target echo, reverberation, and ambient noise are individually simulated as a function of time, and finally summed to simulate a total received signal. A 3-dimensional highlight model, which can reflect the target features including the shape, position, and azimuthal and elevation angles, has been applied to each multipath pair between source and target to simulate the target echo signal. The results are finally compared to those obtained by the algorithm in which only direct path is considered in target signal simulation.