• Journal of KSNVE
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• v.4 no.4
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• pp.397-403
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• 1994

본 고에서 제시한 타이어 소음발생기구에 대한 수학적 모델은 실제활용을 목적으로 정립된 것이다. 이 모델은 프로그램화 되어 타이어 패턴 소음 예측을 위한 컴퓨터 시뮬레이션에 활용되었다. 시뮬레이션에서는 전체 소음도 뿐만아니라 스펙 트럼 특성까지도 예측할 수 있으며, 몇가지 실추결과와 비교하여 만족스러운 수준 의 일치를 확인하였다. 이 기술의 장점은 저소음 타이어 개발에 따르는 시간과 돈을 크게 절약할 수 있다는 것이다. 즉 타이어 설계자가 구상하고 있는 패턴을 컴퓨터에 입력함으로서 타이어의 기본기능을 손상하지 않는 범위에서 최선의 소음특성을 갖는 패턴을 결정할 수 있다. 선진 타이어 업체들은 이미 이같은 방법을 사용하고 있는 것으로 알려져 있으며, 이제 우리도 그 수준에 도달했다고 자부할 수 있다. 그러나 모든 과학기술 이론이 그러하듯이 이 글에서 제시한 모델 역시 결코 완벽하다고 볼 수는 없다. 이 글의 내용이 국내 관심있는 학자들에게 시작할 수 있는 계기가 될 것을 기대하며, 국산 자동차의 저소음화 노력이 더욱 활성화 되기를 희망한다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1074-1083
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• 2011

Noise of digital camera has been noticeable to its users. Particularly, noise of a barrel assembly module in zoom in/zoom out operation is recorded while taking a video. Reduction of barrel noise becomes crucial but there are not many studies on noise of digital camera due to its short history of use. In this study, experiment-based analyses are implemented to identify sources of noise and vibration because of complexity and compactness of the barrel system. Output noise is acquired in various operation conditions using synchronization for spectral analysis. Noise sources of a barrel assembly in zoom operating are first identified by the comparison with gear frequency analysis and then correlation analysis between noise and vibration is applied to confirm the generation path of noise. Analysis on noise transfer characteristic of zoom module is also carried out in order to identify the most contributing components. One of possible countermeasures of noise in zoom operating is investigated by an experimental approach.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.238-243
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• 2008

Several experimental techniques for identifying the noise sources distributed over a moving vehicle have been developed recently and are used to design a low noise vehicle. The beamforming method, which uses phase information between several microphones to localize the source position, is proved to be one of the promising techniques applicable even under complicated test environments. In this study a beamforming algorithm is developed and applied to measure the dominant noise sources on a passenger car passing by. Unlike the acoustic signals from a stationary noise source, the sound generated from a moving source is distorted due to the Doppler effects. The information about the speed and relative position of the vehicle are used to eliminate the Doppler effects from the measured acoustic signal by using a de-Dopplerization algorithm. The noise generated from a moving vehicle can be grouped in many ways, however, tire noise and the noise generated from the engine are distinguishable at the speeds being tested.

• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.87-93
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• 2014

If one identifies the detailed distribution of pressure and axial velocity at a source plane, the position and strength of major noise sources can be known, and the propagation characteristics in axial direction can be well understood to be used for the low noise design. Conventional techniques are usually limited in considering the constant source characteristics specified on the whole source surface; then, the source activity cannot be known in detail. In this work, a method to estimate the pressure and velocity field distribution on the source surface with high spatial resolution is studied. The matrix formulation including the evanescent modes is given, and the nearfield measurement method is proposed. Validation experiment is conducted on a wide duct system, at which a part of the source plane is excited by an acoustic driver in the absence of airflow. Increasing the number of evanescent modes, the prediction of pressure spectrum becomes further precise, and it has less than -25 dB error with 26 converged evanescent modes within the Helmholtz number range of interest. By using the converged modal amplitudes, the source parameter distribution is restored, and the position of the driver is clearly identified at kR = 1. By applying the regularization technique to the restored result, the unphysical minor peaks at the source plane can be effectively suppressed with the filtering of the over-estimated pure radial modes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.930-938
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• 2018

Underwater noise radiated from submarines is directly related to the probability of being detected by the sonar of an enemy vessel. Therefore, minimizing the noise of a submarine is essential for improving survival outcomes. For modern submarines, as the speed and size of a submarine increase and noise reduction technology is developed, interest in flow noise around the hull has been increasing. In this study, a noise analysis technique was developed to predict flow noise generated around a submarine shape considering the free surface effect. When a submarine is operated near a free surface, turbulence-induced noise due to the turbulence of the flow and bubble noise from breaking waves arise. First, to analyze the flow around a submarine, VOF-based incompressible two-phase flow analysis was performed to derive flow field data and the shape of the free surface around the submarine. Turbulence-induced noise was analyzed by applying permeable FW-H, which is an acoustic analogy technique. Bubble noise was derived through a noise model for breaking waves based on the turbulent kinetic energy distribution results obtained from the CFD results. The analysis method developed was verified by comparison with experimental results for a submarine model measured in a Large Cavitation Tunnel (LCT).

• 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.