• 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.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.4
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• pp.492-502
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• 1995

This paper describes the relationship between the behavior of the snakehead ( Channa arps) of 44cm long and the environmental noise levels due to the piling work. The experiment is conducted in the aquafarm located near Asan lake, Pyongtaek in 1993. The fish trajectory is obtained by a biotelemetry system in which a pulsed ultrasonic pinger attached onto the dorsal is tracked three dimensionally, and the noise and the vibration levels both in air and in water are measured. The results of this study are as follows: 1) The noise levels in water and in air and the vibration level measured at a distance of 90m from the noise source, increased by 36.5dB $(re\;l{\mu}Pa)$, 2308$(re\;0.0002{\mu}bar)$ and $5.9{\mu}m$ repectively compared to the levers before piling. 2) The highest variation of the swimming speed was observed right after the piling works and the width of variation decreased with the elapsed time. The average speeds of the fish before and during the works were measured as 0.8 times and 1.1 times of the body length, respectively. 3) It is found that the fish escapes into the mud of the aquafarm when a heavy shock wave occurred. Consequently, the heavy shock by the piling works could produce a considerably unfavorable effect to the fish.

• 대한공업교육학회지
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• v.35 no.2
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• pp.224-237
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• 2010

When the underwater structure sails high speed, noise and vibration propagate to the sensor in the nose of the dome. In this paper, to avoid this kind of noise and vibration CRP(Carbon Reinforced Plastic) material and SNORE ring(Self-NOise REduction Ring) are attached at the curved structure and simulates its isolation characteristics using commercial software. Vibration displacement and stress are calculated at the planar sensor array. The material of the curved structure is aluminum and maximum outer diameter is 53Omm, 215mm in length, 270mm in planar diameter, respectively. Based on the simulation results, reduction ratio of the received normal stress at the sensor is above 95% at the frequency of 12kHz and 15kHz. At the mid point of the planar sensor the normal stress is higher than 20mm and 40mm apart. This results can be used to increase the sensitivity of the acoustic sensor as a basic data.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.1
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• pp.41-49
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• 1999

This paper describes to analyze the underwater ambient noise and biological noise of cultivating fishes in the fish farm cages at the seawater Tongyong-kun, KyongNam and lake of Chungju, Chech'on, ChungBuk from 10 to 19 Oct. 1997, in order to find out the characteristics of these noises. The results obtained were as follows; (1) The ambient noise around the fish farm cages at lake of Chungju was 10~200Hz frequency range, 70~105dB spectrum level. The central frequency was 50~70Hz, changing of ambient noise was getting bigger than 10~200Hz in 200Hz~2kKz frequency by wind, water current. (2) The frequency of noise source around the fish farm cage at the seawater of Tongyong-kun was 20~200Hz, spectrum level was 80~100dB while feed factory was working around the fish farm cage. When feed factory did not work, noise source was 10~600Hz frequency range, 70~90dB spectrum level. It was 10dB less than that of while feed factory was working, and then the central frequency was 70Hz. (3) The vessel noise of excursion ship had changed largely at 100dB spectrum level in 10~500Hz frequency band, and the fishing boat had 20Hz~2kHz frequency range. (4) The biological noise in the fish farm cage at lake of Chungju, which was feeding of Cyprinus carpio, 2was 10~30Hz frequency, 70~104dB spectrum level. The central frequency was 75Hz. The biological noises in the fish farm cage at the seawater of Tongyong-kun, which were feeding and swimming noise, had very different spectrum pattern by species, and the frequency band was 10~800Hz.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.10 no.4
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• pp.227-235
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• 1977

The noise pressure scattered underwater on account of the engine revolution of a pole and liner, Kwan-Ak-San(G. T. 234.96), was measured at the locations of Lat. $34^{\circ}47'N$, Long. $128^{\circ}53'E$ on the 16th of August 1976 and Lat. $34^{\circ}27'N$, Long. $128^{\circ}23'E$ on the 28th of July, 1977. The noise pressure passed through each observation point (Nos. 1 to 5), which was established at every 10m distance at circumference of outside hull was recorded when the vessel was cruising and drifted. In case of drifting, the revolution of engine was fixed at 600 r. p. m. and the noise was recorded at every 10 m distance apart from observation point No. 3 in both horizontal and vertical directions with $90^{\circ}$ toward the stern-bow line. In case of cruising, the engine was kept in a full speed at 700 r.p.m. and the sounds passed through underwater in 1 m depth were also recorded while the vessel moved back and forth. The noise pressure was analyzed with sound level meter (Bruel & Kjar 2205, measuring range 37-140 dB) at the anechoic chamber in the Institute of Marine Science, National Fisheries University of Busan. The frequency and sound waves of the noise were analyzed in the Laboratory of Navigation Instrument. From the results, the noise pressure was closely related to the engine revolution shelving that the noise pressure marked 100 dB when .400 r. p. m. and increase of 100 r. p. m. resulted in 1 dB increase in noise pressure and the maximum appeared at 600 r. p. m. (Fig.5). When the engine revolution was fixed at 700 r. p. m., the noise pressures passed through each observation point (Nos. 1 to 5) placed at circumference of out side hull were 75,78,76,74 and 68 dB, the highest at No.2, in case of keeping under way while 75,76,77,70 and 67 dB, the highest at No.3 in case of drifting respectively (Fig.5). When the vessel plyed 1,400 m distance at 700 r.p.m., the noise pressure were 67 dB at the point 0 m, 64 dB at 600m and 56 dB at 1,400m on forward while 72 at 0 m, 66 at 600 m and 57 dB at 1,400 m on backward respectively indicating the Doppler effects 5 dB at 0 m and 3 dB at 200 m(Fig.6). The noise pressures passed through the points apart 1,10,20,30,40 and 50 m depth underwater from the observation point No.7 (horizontal distance 20 m from the point No.3) were 68,75,62,59,55 and 51 dB respectively as the vessel was being drifted maintaining the engine revolution at 600 r. p. m. (Fig. 8-B) whereas the noise pressures at the observation points Nos.6,7,8,9 and 10 of 10 m depth underwater were 64,75,55,58,58 and 52 dB respectively(Fig.8-A).

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.30-38
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• 2017

In this paper, we propose an underwater acoustic sensor fault detection method for passive sonar systems. In general, a passive sonar system displays processed results of array signals obtained from tens of the acoustic sensors as a two-dimensional image such as displays for broadband or narrowband analysis. Since detection result display in the operation software is to display the accumulated result through the array signal processing, it is difficult to determine the possibility where signal may be contaminated by the fault or failure of a single channel sensor. In this paper, accordingly, we propose a detection method based on the analysis of RMSCR (Root Mean Square Crossing-Rate), and the processing techniques for the faulty sensors are analyzed. In order to evaluate the performance of the proposed method, the precision of detecting fault sensors is measured by using signals acquired from real array being operated in several coastal areas. Besides, we compare performance of fault processing techniques. From the experiments, it is shown that the proposed method works well in underwater environments with high average RMS, and mute (set to zero) shows the best performance with regard to fault processing techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.878-886
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• 2018

This study quantified the sounds of riffles and pools in natural rivers and conducted a comparative analysis of the frequency and sound pressure per flow velocity. The surveyed area was Namdaecheon basin in Yangyang-gun, Gangwon-do and the sounds of a total of 23 sites were analyzed. A hydro microphone was used to measure the sound and analyze the data using an acoustic analysis program. The location was also selected at places with minimal ambient noise and the measurement points were the depth of riffles and pools. The results revealed an average difference of 0.515 m/s for flow velocity at 8 riffles and 15 pools. The difference in sound pressure occurred due to the flow velocity. In the case of sound pressure, it was measured at an average of 176.8 dB for riffles and 168.2 dB for pools, demonstrating a difference of approximately 8.6 dB. Furthermore, in the case of maximum sound pressure, riffles showed a constant range between 200 Hz and 250 Hz, while the pools exhibited maximum sound pressure at various frequencies from 200 Hz to 1,000 Hz. This revealed the ecological stream reproduction, development of preferred sound sources for aquatic life, and design of structures.