• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.10-20
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• 2014

To study the water-entry impact noise, on-board experiment using a small launcher firing various objects was performed in the Yellow Sea. As the launcher fires a cylindrical object from the ship vertically, generated noise is measured with a hydrophone on the starboard of Chung-hae, Marine surveyor. Three types of cylindrical objects, which have noses of flat-faced, conical, and hemisphere, were used during the experiment. The measured noise exhibits a time-dependency which can be divided into three phases: (1) initial impact phase, (2) open cavity flow phase, (3) cavity collapse and bubble oscillation phase. In most cases, the waveform of bubble oscillation phase is dominant rather than that of initial impact phase. Pinch-off time, where a cavity begins to collapse, occurs at 0.18 ~ 0.2 second and the average lasting time of bubble was 0.9 ~ 1.3 second. The energy of water-entry impact noise is focused in the frequency region lower than 100 Hz, and the generated noise is influenced by the nose shapes, object mass, and launching velocity. As a result, energy spectral density on the bubble frequency is higher in the order of flat-faced, conical, hemisphere nose, and the increase of initial energy raises the energy spectral density on the bubble frequency in the cylinder body of same shape. Finally, we compare the measurements with the simulated signals and spectrum based on the bubble explosion physics, and obtain satisfactory agreements between them.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.214-221
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• 2019

In this paper, a temperature dependent acoustic receiving characteristics of underwater acoustic sensor is studied by theoretical and experimental investigations. Two different types (low mid frequency sensor and high frequency sensor) of underwater acoustic sensors are designed with different configuration of baffle and conditioning plate. The temperature dependent characteristics of the acoustic sensors are investigated within the temperature range from $-2^{\circ}C$ to $35^{\circ}C$. The material properties of the piezoelectric ceramics, molding and baffle, which are the primary materials of the acoustic sensors, are measured with temperature change. The temperature dependent RVS (Receiving Voltage Sensitivity) characteristics of the acoustic sensors are simulated by using the measured material properties. The RVS changes of the acoustic sensors are measured by changing temperature in the watertank where the acoustic sensors are installed. The measured and the simulated data show that the temperature dependent characteristics of the acoustic sensors are mainly dependent for the sound speed changes of the molding material.

• 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.