• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.207-212
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• 2008

Bathymetry, side scan sonar, and magnetic survey data for the summit area of Dokdo obtained by Korea Ocean Research & Development Institute in 1999, 2004, and 2007 were analyzed to investigate the geophysical characteristics of the summit. Bathymetry and topographic data for the summit of Dokdo show uneven seabed and irregular undulations from costal line to -90 m in water depth, indicating the effects of partial erosions and taluses. The stepped slope in the bathymetry is supposed to be a coastal terrace suggesting repetition of transgressions and regressions in the Quaternary. The bathymetry and the side scan sonar data show a small crater, assumed to be formed by post volcanisms, at depth of $-100\;{\sim}\;-120\;m$ in the northeastern and the northwestern parts of the survey area. Except some areas with shallow sand sedimentary deposits, there are rocky seafloor and lack of sediments in the side scan sonar images of the survey area, dominantly. The analytic signal of the magnetic anomaly coincides with other geophysical results regarding to the location of the residual crater. The geophysical constraints of the summit of Dokdo propose that the islets and the rocky seabed elongated northeastward and northwestward from the islets might be the southern crater of the Dokdo volcano.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.