• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.528-533
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• 2005

A mono-static high frequency acoustic target strength analysis scheme was developed for underwater targets, based on the far-field Kirchhoff approximation. Au adaptive triangular beam method and a concept of virtual surface were adopted for considering the effect of hidden surfaces and multiple reflections of an underwater target, respectively. A test of a simple target showed that the suggested hidden surface removal scheme is valid. Then some numerical analyses, for several underwater targets, were carried out; (1) for several simple underwater targets, like sphere, square plate, cylinder, trihedral corner reflector, and (2) for a generic submarine model, The former was exactly coincident with the theoretical results including beam patterns versus azimuth angles, and the latter suggested that multiple reflections have to be considered to estimate more accurate target strength of underwater targets.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.23 no.4
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• pp.189-197
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• 1987

As the basic research for the application of a parametric acoustic source to fish finding, the characteristics of beam patterns and parametric gains of the acoustic source were investigated and target strengths of fish, grey mullet, with the acoustic source were measured. The mean primary frequency of the acoustic source was 200KHz and the produced sounds by difference-frequencies were 5KHz, 10KHz, 16KHz and 20KHz. For measurement of target strength in yaw (coronal) plane of fish the to be target was 34cm in length, the pulse duration of the source was 0.3m/sec and the difference frequency was 10KHz in consideration of the length of fish and of parametric gain of the acoustic source. The results obtained are as follow: 1. Beam widths(down 3 dB) of the parametric acoustic source excited at frequencies of 5KHz, 10KHz, 16KHz, and 20KHz were 4.3$^{\circ}$, 2.2$^{\circ}$, 3.0$^{\circ}$ and 2.5$^{\circ}$ respectively. 2. Parametric gains of the parametric acoustic source excited at frequencies of 5KHz, 10KHz, 16KHz and 20KHz were -41 dB, -45 dB, -60 dB and -68 dB respectively. 3. Target strengths of a fish in head and tail aspect using the parametric acoustic source were 5 dB lower than those using 200KHz single frequency sound, but those in side aspect were similar. 4. Target strengths of two or three fish with the parametric acoustic source were 1-3 dB lower than those in head and tail aspect using 200KHz single frequency sound.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.2
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• pp.272-278
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• 2015

The aim of this study is to find out the behavior and acoustic backscattering of the large jellyfish Nemopliema nomurai using hydroacoustics in situ. N. nomurai was distributed at depths ranging from 10~15 m during the day. Regarding the behavior of N. nomurai, there was no significant change in depth, and 3D tortuosity was not high. The vertical direction was ${\pm}10^{\circ}$ from the horizontal, and moving speed was $0.9{\sim}1.5\;m\;s^{-1}$. With regard to hydro-acoustical characteristics, the mean TS of N. nomurai ranged from -69.6~-56.0 dB at 38 kHz and -69.4~-54.5 dB at 120 kHz. TS variation (Max TS-Min TS) at 38 and 120 kHz was 0~10.2 dB and 0.2~16.0 dB, respectively. Mean TS and TS variation (Max TS-Min TS) of N. nomurai were higher at 120 kHz than at 38 kHz. The results showed that the use of hydroacoustics was effective in estimating the distribution depth, behavior, and acoustic characteristics of the target.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1110-1115
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• 2004

Acoustic behavior in gas turbine combustor with acoustic resonator is investigated numerically by adopting linear acoustic analysis. Helmholtz-type resonator is employed as acoustic resonator to suppress acoustic instability passively. The tuning frequency of acoustic resonator is adjusted by varying its length. Through harmonic analysis, acoustic-pressure responses of chamber to acoustic excitation are obtained and the resonant acoustic modes are identified. Acoustic damping effect of acoustic resonator is quantified by damping factor. As the tuning frequency of acoustic resonator approaches the target frequency of the resonant mode to be suppressed, mode split from the original resonant mode to lower and upper modes appears and thereby complex patterns of acoustic responses show up. Considering mode split and damping effect as a function of tuning frequency, it is desirable to make acoustic resonator tuned to broad-band frequencies near the maximum frequency of those of the possible upper modes.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.1 s.232
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• pp.95-102
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• 2005

Acoustic behavior in gas turbine combustor with acoustic resonator is investigated numerically by adopting linear acoustic analysis. Helmholtz-type resonator is employed as acoustic resonator to suppress acoustic instability passively. The tuning frequency of acoustic resonator is adjusted by varying its length. Through harmonic analysis, acoustic-pressure responses of chamber to acoustic excitation are obtained and the resonant acoustic modes are identified. Acoustic damping effect of acoustic resonator is quantified by damping factor. As the tuning frequency of acoustic resonator approaches the target frequency of the resonant mode to be suppressed. mode split from the original resonant mode to lower and upper modes appears and thereby complex patterns of acoustic responses show up. Considering mode split and damping effect as a function of tuning frequency, it is desirable to make acoustic resonator tuned to broad-band frequencies near the maximum frequency of those of the possible upper modes.

• Phonetics and Speech Sciences
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• v.4 no.4
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• pp.139-146
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• 2012

This study was designed to examine comfort levels of pitch and loudness with target matching and their effects on electroglottographic (EGG) and acoustic measures. Twelve speakers, six males and six females, were instructed to produce /a/ sustained vowel for three seconds at a comfortable pitch and loudness level without any instruction and with a target matching procedure of either a certain f0 or SPL separately with visual and auditory feedback. The range of pitch for females and males were presented by progressing up and down randomly at intervals of 5Hz from 150 Hz to 310 Hz (total 33 frequency targets) and from 85 Hz to 190 Hz (total 22 frequency targets), respectively. The loudness levels were 65, 75, 85, 95 dB (total of four intensity targets) for both males and females. Subjective estimations of comfortable levels were obtained using a 10-point equal-appearing interval rating scale following each phonation. The results showed that males and females demonstrated similar trends in loudness levels with greatest comfort at 75 dB, whereas pitch comfort ratings showed a greater variability with females having a wider range with target matching. In the comfort levels of individuals, most male and female speakers rated higher comfort at soft, rather than loud phonations. On the other hand, most male speakers perceived highest comfort levels below the comfort pitch levels they phonated under natural conditions. Higher frequency ranges, however, were perceived to be more comfortable than those of natural condition in most female speakers, although the comfortable pitch levels in spontaneous phonations were within the comfort level ranges determined by targeted phonations. When comparing acoustic (%jitter, %shimmer, SNR) and EGG measures (CQ%) between spontaneous comfortable phonations and targeted phonations produced by the same subject at similar f0 and intensity, no significant differences were observed (p>0.05). Thus, target matching procedures may be considered a compatible and alternative method to reduce the variability of comfortable pitch and loudness levels by eliciting consistent comfortable phonations.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.3
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• pp.239-248
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• 2003

This paper was conducted as an attempt in order to construct the data bank of target strength for acoustic estimation of fish length in the coastal waters of Korea. The fish length dependence of acoustic target strength for 13 large yellow croakers (Pseudosciaena crocea) at 75 kHz was investigated and the prediction of the target strength by using the Kirchhoff-Ray Mode model (KRM model) was compared with target strength measurements. The results obtained are summarized as follows; 1. In the averaged target strength pattern for 13 large yellow croakers the maximum target strength was -35.13 dB at $-13.35^{\circ}$ on a tilted angle. 2. The relationship between fork length(L, cm) and averaged target strength(TS, dB) was expressed as follows; TS=23. 76log (L) -73.45 (r=0.47) TS=20log(L) -67.35 From this result, the conversion coefficient was -73.45 dB and 6.1 dB lower than the coefficient -67.35 dB where the value of the slope of the regression equation is forced to be 20. 3. Averaged target strength and a length conversion coefficient derived from a target strength histogram for 13 large yellow croakers of mean length 25.59 cm were -41.23 dB, -69.72 dB, respectively. 4. In the range of $$2;{\ll} L (fish length /{\lambda}(wave length);{\ll}40$$, the prediction of the averaged target strength by the KRM model increased gradually with the increasing of $L/{\lambda}$ and was lower than the measured target strength.