• 한국지구과학회:학술대회논문집
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• 2001.09a
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• pp.55-55
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• 2001

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.1-2
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.185-186
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1607-1608
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• 2011

• The Journal of the Acoustical Society of Korea
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• v.30 no.1
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• pp.9-16
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• 2011

When a hydrofoil ship plies at high speed, there exist possibilities of collision with ocean mammals dwelling near the surface. An active sonar located within the immersed hydrofoil structure that provides the lift for the vessel, can be used for early warning of their presence. The proper functioning of the active sonar system depends on its ability to reject noise and pick up the target signal. In this article, we measured the noise on a hydrofoil of an operating ship with two flush-mounted hydrophones. The measurements were conducted for the purpose of (1) identifying the effect of operating state of machinery likes engine, cooler and generator (2) observing the change of noise depending on the measuring position (3) observing the change of noise with increasing ship speed. To verify our experiment, experiments were performed three times and the measured results are compared with other investigations and they show similarity to each other. The results are analyzed with frequency domain in order to apply to operating active sonar detecting system and focus on high frequency band within sonar's operating frequency region. Through these experiments and analysis, it is expected that we can identify the generated noise around hydrofoil where active sonar is installed and these results lead us to design active sonar that could distinguish target signal from noise more effectively.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.41-50
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• 2013

To analyze the distribution pattern of flood sediment deposits near the mouth of Chucheoncheon (river), side scan sonar images and seafloor sediment properties were investigated in the offshore area within about 50 m deep in water. Based on the analysis result of the sonar images, the seafloor of the study area is divided into three areas of basement, sandy-mud, and dispersed flood sediment. The colors of sonar images in each area are represented by dark black, light grey, and greyish black, respectively. The sediment composition in the grey black area shows 33.73% of gravel, 62.88% of sand, 3.37% of silt, and 0.02% of clay. On the other hand, the composition of the light grey area is 10.31% of sand, 56.42% of silt, and 33.27% of clay. Especially the sediment of the grey black area contains the considerable amount of burned plant fragments in black color, which could distinctly be differentiated from those in the offshore. The distribution pattern of the flood sediment deposits suggests that the land-originated detrital sediments seem to be transported from the Chucheon river into offshore along the shore rather than transversely. In conclusion, the longshore current of the study area is probably dominant to affect the spatial distribution of bottom features.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.472-483
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• 2013

Actual maritime exercise for improving the capability of submarine sonar operator leads to a lot of cost and constraints. Sonar simulator maximizes the capability of sonar operator and training effect by solving these problems and simulating a realistic battlefield environment. In this study, a passive sonar simulator algorithm is suggested, where the simulator is divided into three modules: maneuvering module, noise source module, and sound propagation module. Maneuvering module is implemented in three-dimensional coordinate system and time interval is set as the rate of vessel changing course. Noise source module consists of target noise, ocean ambient noise, and self noise. Target noise is divided into modulated/unmodulated and narrowband/broadband signals as their frequency characteristics, and they are applied to ship radiated noise level depending on the vessel tonnage and velocity. Ocean ambient noise is simulated depending on the wind noise considering the waveguide effect and other ambient noise. Self noise is also simulated for flow noise and insertion loss of sonar-dome. The sound propagation module is based on ray propagation, where summation of amplitude, phase, and time delay for each eigen-ray is multiplied by target noise in the frequency domain. Finally, simulated results based on various scenarios are in good agreement with generated noise in the real ocean.

• 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.40 no.4
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• pp.330-336
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• 2021

In bistatic sonar operation, the scattering strength of a sonar target is characterized by the probe signal frequency, the aspect angle and the bistatic angle. Therefore, the target detection and identification performance of the bistatic sonar may vary depending on how the positions of the target, sound source, and receiver are changed during sonar operation. In this study, it was evaluated which variable is advantageous to change by comparing the target identification performance between the case of changing the aspect angle and the case of changing the bistatic angle during the operation. A scenario of identifying a hollow sphere and a cylinder was assumed, and performance was compared by classifying two targets with a support vector machine and comparing their accuracy using a finite element method-based acoustic scattering simulation. As a result of comparison, using the scattering strength defined by the frequency and the bistatic angle with the aspect angle fixed showed superior average classification accuracy. It means that moving the receiver to change the bistatic angle is more effective than moving the sound source to change the aspect angle for target identification.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.