• The Journal of the Acoustical Society of Korea
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• v.30 no.1
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• pp.9-16
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• 2011

When a hydrofoil ship plies at high speed, there exist possibilities of collision with ocean mammals dwelling near the surface. An active sonar located within the immersed hydrofoil structure that provides the lift for the vessel, can be used for early warning of their presence. The proper functioning of the active sonar system depends on its ability to reject noise and pick up the target signal. In this article, we measured the noise on a hydrofoil of an operating ship with two flush-mounted hydrophones. The measurements were conducted for the purpose of (1) identifying the effect of operating state of machinery likes engine, cooler and generator (2) observing the change of noise depending on the measuring position (3) observing the change of noise with increasing ship speed. To verify our experiment, experiments were performed three times and the measured results are compared with other investigations and they show similarity to each other. The results are analyzed with frequency domain in order to apply to operating active sonar detecting system and focus on high frequency band within sonar's operating frequency region. Through these experiments and analysis, it is expected that we can identify the generated noise around hydrofoil where active sonar is installed and these results lead us to design active sonar that could distinguish target signal from noise more effectively.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.78-83
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• 2000

Passive sonar system is designed to classify the underwater targets by analyzing and comparing the various acoustic characteristics such as signal strength, bandwidth, number of tonals and relationship of tonals from the extracted tonals and frequency lines. First of all the precise detection and extraction of signal frequency lines is of particular importance for enhancing the reliability of target classification. But, the narrowband frequency lines which are the line formed in spectrogram by a tonal of constant frequency in each frame can be detected weakly or discontinuously because of the variation of signal strength and transmission loss in the sea. Also, it is very difficult to detect and extract precisely the signal frequency lines by the complexity of impulsive ambient noise and signal components. In this paper, the automatic detection and extraction method that can detect and extract the signal components of frequency tines precisely are proposed. The proposed method can be applied under the bad conditions with weak signal strength and high ambient noise. It is confirmed by the simulation using real underwater target data.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.764-770
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• 2015

A crack identification method using an equivalent bending stiffness and natural frequency for cracked beam is presented. Modal properties of cantilever beam with a tip mass is identified by applying the boundary conditions to a general solution. An equivalent bending stiffness for cracked beam based on an energy method is used to identify natural frequencies of cantilever thin-walled pipe with a tip mass, which has a through-the-thickness crack, subjected to bending. The identified natural frequencies of the cracked beam are used in constructing training patterns of neural networks. Then crack location and size are identified using a committee of the neural networks. Crack detection was carried out for an example beam using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.307-310
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• 2006

Underwater ambient noise originating from the geophysical, biological, and man-made acoustic sources contains much information on the sources and the ocean environment affecting the performance of the sonar equipments. In this paper, a set of feature vectors of the ambient noises using MFCC is proposed and extracted to form a data base for the purpose of identifying the noise sources. The developed algorithm for the pattern recognition is applied to the observed ocean data, and the initial results are presented and discussed.

• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.30-36
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• 2008

Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.175-178
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• 2004

선박의 수중방사소음은 다양한 기계류나 추진기 혹은 선체와 유체간의 상호 작용으로 인하여 여러 형태의 특성신호로 나타나게 된다. 이는 선박의 운용조건, 장비 회전특성 및 내부구조에 따라 스펙트럼상에 상이한 주파수로 확인됨은 물론, 신호의 출현 형태에도 다양성을 보이고 있다. 일반적으로 선박소음은 속력 종속적인 추진 계통 성분과 비종속적인 보기류 신호로 구분되나 다수의 신호성분이 혼재되어 발생기원을 분류하는 것은 복잡한 과정을 거쳐야 한다. 본 연구에서는 이러한 점을 해결하기 위해 선박의 Tonal성 신호를 자동으로 탐지하고 분류하기 위해 규준화된 스펙트로그램 상에서 연속되는 신호에 가중치를 주어 지속성 신호여부를 판별한 후에 정해진 임계치를 초과하는 성분을 Tonal로 선정하였다. 선정된 Tonal에 대해 주파수선의 대역특성 및 시간 변동성에 대한 패턴인식 방법을 적용하여 Tonal의 발생기원이 속력 종속/비종속적인지를 자동으로 판별하는 알고리즘의 유용성에 대한 결과를 기술하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.130-135
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• 2006

There have been experiments dealing with the possibility of the actualization of the ANC system by means of operating the DSP adaptation filter. This filter is composed of various filters(including X-LMS algorithm, Filter-U algorithm, and Full-Feedback-Filter-U algorithm) that use ventilation fans and loudspeakers as a primary source in a circular duct as an experimental device. In this operation, the ANC system using the X - LMS algorithm was found to be more effective in reducing noise than without such system. When applying the input signal of the DSP board Full Feedback-Filtered-U algorithm system while having in mind that the additionally installed second control signal was gone through feedback and mixed into the detection microphone installed near the ventilation fan when using the first ventilation fan, the system was not emitted, but maintained stable during the operation of the control filter. At this point, noise tended to decrease at a maximum of l0dB compared to other algorithms at the frequency band of 170-250Hz.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.235-241
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• 2022

Several studies using Convolutional Neural Network (CNN) have been conducted to detect and classify the sounds of marine mammals in underwater acoustic data collected through passive acoustic monitoring. In this study, the possibility of automatic classification of bearded seal sounds was confirmed using a CNN model based on the underwater acoustic spectrogram images collected from August 2017 to August 2018 in East Siberian Sea. When only the clear seal sound was used as training dataset, overfitting due to memorization was occurred. By evaluating the entire training data by replacing some training data with data containing noise, it was confirmed that overfitting was prevented as the model was generalized more than before with accuracy (0.9743), precision (0.9783), recall (0.9520). As a result, the performance of the classification model for bearded seals signal has improved when the noise was included in the training data.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.99-104
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• 2015

In this study, it has been initiated to investigate the specific abnormal vibration signal that has been captured in the power equipment. Array Microphone can be used in order to detect the direction and the position of the noise source. It is possible to track the abnormal mechanical noise in the power plant by utilizing the program and the microphone array system developed from this research. Array microphone system can be operated as a constant monitoring system.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.40-46
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• 2008

Weak target detection and localization in the presence of loud surface ship noise is a critical problem for matched field processing (MFP) in shallow water. For stationary sources, each signal component of received signal can be separated and interference can be suppressed using eigen space analysis schemes. However, source motion, in realistic cases, causes spreading of signal energies in their subspace. In this case, eigenvalues of target and interfere signal components are mixed and hard to be separated with usual phone space eigenvector decomposition (EVD) approaches. Our technique is based on mode space and utilizes the difference in their physical characteristics of surface and submerged sources. Performing EVD for modal cross spectral density matrix, interference components in the mode amplitude subspace can be classified and eliminated. This technique is demonstrated with synthetic data, and results are discussed.