• Ocean and Polar Research
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• v.34 no.3
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• pp.325-335
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• 2012

It is well known that sound waves in the sea propagates under the influence of sea surface and bottom roughness, the sound speed profile, the water depth, and the density of sea floor sediment. In particular, an abrupt change of sound speed with depth can greatly affect sound propagation through an eddy. Eddies are frequently generated in the East Sea near the Korean Peninsula. A warm eddy with diameter of about 150 km is often observed, and the sound speed profile is greatly changed within about 400 m of water depth at the center by the eddy around the Ulleung Basin in the East Sea. The characteristics of low-frequency sound propagation across a warm eddy are investigated by a sound propagation model in order to understand the influence of warm eddies. The acoustic rays and propagation losses are calculated by a range-dependent acoustic model in conditions where the eddy is both present and absent. We found that low-frequency sound propagation is affected by the warm eddy, and that the phenomena dominate the upper ocean within 800 m of water depth. The propagation losses of a 100 Hz frequency are variable within ${\pm}15$ dB with depth and range by the warm eddy. Such variations are more pronounced at the deep source near the sound channel axis than the shallow source. Furthermore, low-frequency sound propagation from the eddy center to the eddy edge is more affected by the warm eddy than sound propagation from the eddy edge to the eddy center.

• Ocean and Polar Research
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• v.29 no.2
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• pp.113-121
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• 2007

The sound wave in the sea propagates under the effect of water depth, sound speed structure, sea surface roughness, bottom roughness, and acoustic properties of bottom sediment. In shallow water, the bottom sediments are distributed very variously with place and the sound speed structure varying with time and space. In order to investigate the seasonal propagation characteristics of low-frequency sound wave in the Yellow Sea, propagation experiments were conducted along a track in the middle part of the Yellow Sea in spring, summer, and autumn. In this paper we consider seasonal variations of the sound speed profile and propagation loss based on the measurement results. Also we quantitatively investigate variation of bottom loss by dividing the propagation loss into three components: spreading loss, absorption loss, and bottom loss. As a result, the propagation losses measured in summer were larger than the losses in spring and autumn, and the propagation losses measured in autumn were smaller than the losses in spring. The spreading loss and the absorption loss did not show seasonal variations, but the bottom loss showed seasonal variations. So it was thought that the seasonal variation of the propagation loss was due to the seasonal change of the bottom loss and the seasonal variation of the bottom loss was due to the change of the sound speed profile by season.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.213-220
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• 2002

The sound wave in the sea propagates under the effect of water depth, sound velocity structure, sea surface and bottom roughness, and bottom sediment distribution. In particular the sound velocity structure in shallow water varies with time and space, an? the sediment distributes very variedly with place. In order to investigate the seasonal variation of low-frequency sound propagation in the Yellow Sea, the propagation experiments were conducted along the same track in the middle part of the Yellow Sea at various seasons of spring. summer, and autumn. In this paper we consider the measurement results on the propagation loss with the sound velocity structure, and investigate the seasonal variation of the propagation loss. As a result, the propagation losses measured in summer were larger than the losses in spring and autumn. And the propagation losses measured in autumn were smaller than the losses in spring. The seasonal change of the propagation loss increased with the rise of sound frequency and the propagation range.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.64-73
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• 1987

Low frequency sound propagation in a range-dependent shallow water environment of the Korea Strait has been studied by using the adiabatic coupled mode, ADIAB. The range-dependent environment is unique in terms of horizontal variations of sound velocity profiles, sediment thickness and attenuation coefficients and water depths. For shallow source and receiver depths, the most important mechanism involved in the propagation loss is the depth changing character of mode functions that strongly depends on the local sound velocity profile. Application of the adiabatic coupled mode theory to shallow water environment is reasonable when higher modes are attenuated due to bottom interaction effects. Underwater sound propagation in a range-dependent shallow-water environment.

• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.402-407
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• 2013

In this paper, an analysis of sound propagation between two rooms with different mediums is discussed. Statistical energy analysis (SEA) is used to consider energy equilibrium among subsystems associated with the sound pressure levels in two rooms and the vibration level of the wall between rooms. Effect of the sound radiation from the structure-borne noise of the wall on sound pressure level of the receiving room is investigated. For a numerical example, sound propagation between engine room and water tank joined by a steel plate whose size is $8.4{\times}4$ m, is considered. It is found that when the critical frequency of the plate is above the frequency range of interest, the sound pressure level in the water tank is dominated by sound transmission through the plate, while sound radiation from the structure-borne noise of the plate is negligible except low frequency range below 63 Hz.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.798-803
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• 2004

Leak noise is a good source to identify the exact location of a leak point of underground water pipelines. Water leak generates broadband sound from a leak location and this sound propagation due to leak in water pipelines is not a non-dispersive wave any more because of the surrounding pipes and soil. However, the necessity of long-range detection of this leak location makes to identify low-frequency acoustic waves rather than high frequency ones. Acoustic wave propagation coupled with surrounding boundaries including cast iron pipes is theoretically analyzed and the wave velocity was confirmed with experiment. The leak locations were identified both by the acoustic emission (AE) method and the cross-correlation method. In a short-range distance, both the AE method and cross-correlation method are effective to detect leak position. However, the detection for a long-range distance required a lower frequency range accelerometers only because higher frequency waves were attenuated very quickly with the increase of propagation paths. Two algorithms for the cross-correlation function were suggested, and a long-range detection has been achieved at real underground water pipelines longer than 300m.

• The Journal of the Acoustical Society of Korea
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• v.6 no.2
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• pp.48-53
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• 1987

The depth dependent sound fields have been calculated for a single frequency source to reveal the fluctuating sound energy at both near the surface and the bottom of the water layer. Those fluctuation are mainly due to the mode function behavior along the depth where the sound-speed gradient acts like trapping lower mode sound energy in those medium.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.162-167
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• 2024

Low frequency noise emitted by wind turbines is one of the most noise complaints. In this study, the reliability of the models was examined by comparing the measured sound pressure levels with the predicted levels based on Denish model and commercial programs of the SounPLAN and the ENPro based on ISO 9613. As a result of applying it to representative 3 MW wind turbines, on lnad, the measured and the predicted values differed within a maximum of 5 dB in the frequency range of 12.5 Hz to 80 Hz. It may be due to the change in the acoustic power levels because the wind turbines have been in operation for more than 7 years. However, considering that the Boundary Element Method (BEM) predicted value, which is known to be the most accurate in the low frequency band, the predicted values are well matched within 2.5 dB, the models of this study are expected to be used as deviation within 3 dB.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.2
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• pp.84-92
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• 1995

To understand how low-frequency sound waves propagate axially in drilling boreholes, the propagation modes and speeds including the effect of interaction among layers are obtained by analyzing an infinitely-long, uniform, and cylindrically multi-layered waveguide which is consisted of fluid layers and solid layers. Assuming low frequency(wave length considered is very long compared to the borehole diameter), axisymmetry, non-viscosity, and etc., analytical solutions are obtained. Also, sound reflection due to the changes in the cross section is analyzed. Results for typical drilling boreholes show the usefulness of the method developed in this research, and are compared with FEM results showing good agreements.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1045-1048
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• 2000

This paper presents the application of the wavelet transform analysis and the neural network method to the phonocardiogram (PCG) signal. Heart sound is a acoustic signal generated by cardiac valves, myocardium and blood flow and is a very complex and nonstationary signal composed of many source. Heart sound can be discriminated normal heart sound and heart murmur. Murmurs have broader frequency bandwidth than the normal ones and can occur at random position of cardiac cycle. In this paper, we classified the group of heart sound as normal heart sound(NO), pre-systolic murmur(PS), early systolic murmur(ES), late systolic murmur(LS), early diastolic murmur(ED). And we used the wavelet transform to shorten artifacts and strengthen the low level signal. The ANN system was trained and tested with the back- propagation algorithm from a large data set of examples-normal and abnormal signals classified by expert. The best ANN configuration occurred with 15 hidden layer neurons. We can get the accuracy of 85.6% by using the proposed algorithm.