• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.472-482
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• 2019

This paper describes the stabilization of underwater TAS winch system Deploy/Recover operation performance. TAS winch installed on the stern of submarine performs to deploy/recover sensor, towing cable and rope tail which is deployed from the stern and separated from submarine itself. Also TAS winch provides transmission path of power to the sensor and data transmitting/receiving path which data are acquired from underwater environment like sound, depth and temperature. At the step of TAS winch evaluation test, sporadic standstill and rotating speed oscillation phenomenon were occurred. Winch motor provides the available torque to deploy/recover TAS and root cause analysis to the winch motor was done to find exact reason to sporadic malfunction. When winch motor was disassembled, eccentricity of rotor, slip-ring and the other composition part for winch motor were found. These might cause magnetic field distortion. To make TAS winch system more stable and block magnetic field distortion, this paper suggests methods to enhance fixing status installed in winch motor. For reliable data acquisition for TAS winch operation, the deploy/recover function of the improved type of TAS winch was verified in LBTS making similar condition with sea status. At the end of stage, improved type of TAS winch was tested on some functions not only deploy/recover function, but sustainability of TAS operation on specific velocity, steering angle of submarine in the sea trial. Improved type of TAS winch was verified in accordance with design requirement. Also, validity of suggested methods were verified by the sea trial.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.4 no.3
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• pp.170-179
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• 1999

The SUS (Signal, Underwater Sound)-OAT experiment was carried out in the Ulleung Basin of the East Sea on 3 June 1997. The SUS-OAT system consisted of aircraft deployed shots as sources and a vertical line array (VLA) tethered by a receiver ship was used to survey a large area where a mesoscale warm eddy appears frequently. The experiment was carried out such that explosive charges set to detonate at 800 ft depth were dropped in a rectangular ($120{\times}120$ km). Sources were a rapidly deployable SUS charge (MK 61 MOD 0), and receiver is a fixed VLA, 90 m in length (150-240 m in receiver depth), composed of 10 elements equally spaced. The reference ray paths are computed by range-dependent acoustic model in canonical ocean based on the historical data. The singular value decomposition (SVD) method is used to obtain the horizontal perturbation of the temperature fields. Horizontal distributions of temperature fields at 150 m and 200 m depth show a weak warm eddy observed by AXBT and the inversely estimated temperature shows similar patterns in terms of the location of the warm eddy. In conclusion, the SUS-OAT experiment has been successful to estimate the position of warm eddy and its temperature field in the East Sea of Korea.

• 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.