• Ocean and Polar Research
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• v.29 no.4
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• pp.311-316
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• 2007

Ocean noise may be used for monitoring wind speed and rainfall rate on the sea surface, as well as for tracking whales' migration routes. In particular, low-frequency ocean noise has recently been of concern with relation to the behavior of marine mammals. Low-frequency ocean noise has been increasing over the past few decades due to increase of ship traffic and offshore oil industry activities. Mechanical noise such as flow noise and cable strumming noise may be induced if low-frequency ocean noise is measured by cabled traditional hydrophone in high current areas. To successfully measure low-frequency ocean noise in a shallow water environment with strong current, we developed a self-recording hydrophone. This paper describes the main configurations of the self-recording hydrophone and presents some results on measured data.

• 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.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.4
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• pp.408-416
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• 2007

This paper summarizes an experimental study on the marine propeller BPF noise. The main objective of this study is to show the worthiness of the noise measurement at the MOERI middle size cavitation tunnel and to acquire useful propeller noise data. Background noise of MOERI(Maritime and Ocean Engineering Research Institute) cavitation tunnel is experimentally analyzed. Experiment carried out in the MOERI cavitation tunnel with wake screen or dummy body, which is simulated the wake. Propeller BPF noise is measured under various operating conditions. In order to secure the reliance of measured propeller noise dada, background noise of each operating conditions are measured and analyzed. The noise characteristics are analyzed according to the operating condition.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.235-242
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• 2016

Among underwater noise sources around submerged bodies, turbulence-induced noise has not been well investigated because of the difficulty of predicting it. In computational aeroacoustics, a number of studies has been conducted using the Ffowcs Williamse-Hawkings (FW-H) acoustic analogy without consideration of quadrupole source term due to the unacceptable calculation cost. In this paper, turbulence-induced noise is predicted, including that due to quadrupole sources, using a large eddy simulation (LES) turbulence model and a developed formulation of permeable FW-H method with an open source computational fluid dynamics (CFD) tool-kit. Noise around a circular cylinder is examined and the results of using the acoustic analogy method with and without quadrupole noise are compared, i.e. the FW-H method without quadrupole noise versus the permeable FW-H method that includes quadrupole sources. The usability of the permeable FW-H method for the prediction of turbulence-noise around submerged bodies is shown.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• Ocean Science Journal
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• v.41 no.1
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• pp.1-10
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• 2006

Total sea surface temperature (SST) in a coupled GCM is diagnosed by separating the variability into signal variance and noise variance. The signal and the noise is calculated from multi-decadal simulations from the COLA anomaly coupled GCM and the interactive ensemble model by assuming both simulations have a similar signal variance. The interactive ensemble model is a new coupling strategy that is designed to increase signal to noise ratio by using an ensemble of atmospheric realizations coupled to a single ocean model. The procedure for separating the signal and the noise variability presented here does not rely on any ad hoc temporal or spatial filter. Based on these simulations, we find that the signal versus the noise of SST variability in the North Pacific is significantly different from that in the equatorial Pacific. The noise SST variability explains the majority of the total variability in the North Pacific, whereas the signal dominates in the deep tropics. It is also found that the spatial characteristics of the signal and the noise are also distinct in the North Pacific and equatorial Pacific.

• Ocean Science Journal
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• v.41 no.3
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• pp.175-179
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• 2006

The Ieodo Ocean Research Station(IORS) is an integrated meteorological and oceanographic observation base which was constructed on the Ieodo underwater rock located at a distance of about 150 km to the south-west of the Mara-do, the southernmost island in Korea. The underwater ambient noise level observed at the IORS was similar to the results of the shallow water surrounding the Korean Peninsula (Choi et al. 2003) and was higher than that of deep ocean (Wenz 1962). The wind dependence of ambient noise was dominant at frequencies of a few kHz. The surface current dependence of ambient noise showed good correlation with the ambient noise in the frequency of 10 kHz. Especially, the shrimp sound was estimated through investigations of waveform and spectrum and its main acoustic energy was about 40 dB larger than ambient noise level at 5 kHz.

• Ocean and Polar Research
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• v.28 no.4
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• pp.459-465
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• 2006

Ambient noise as a background noise in the ocean has been well known for its the various and irregular signal characteristics. Generally, these signals we treated as noise and they are analyzed through stochastical level if they don't include definite sinusoidal signals. This study is to see how ocean ambient noise can be analyzed by the chaotic analysis technique. The chaotic analysis is carried out with underwater ambient noise obtained in areas near the Korean Peninsula. The calculated physical parameters of time series signal are as follows: histogram, self-correlation coefficient, delay time, frequency spectrum, sonogram, return map, embedding dimension, correlation dimension, Lyapunov exponent, etc. We investigate the chaotic pattern of noises from these parameters. From the embedding dimensions of underwater noises, the assesment of underwater noise by chaotic analysis shows similar results if they don't include a definite sinusoidal signal. However, the values of Lyapunov exponent (divergence exponent) are smaller than that of random noise signal. As a result we confirm the possibility of classification of underwater noise using Lyapunov analysis.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.97-103
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• 2016

The sensitivity of interior noise that the passengers perceive is comparatively high in the train, and structure-borne and air-borne types of noises come into the train. In this paper, to analyze contributions of these noise sources operational transfer path analysis(OTPA) is used. OTPA has some advantages of executing the contribution rates of several sources simultaneously, and in this work, 29 points are measured while running. Transfer functions between reference measurement points and response measurement points are calculated by the singular value decomposition(SVD) and Principal component analysis(PCA) method, and the frequency characteristics of the noise source are successfully derived. Also the interior noise is predicted and compared with measurement data to show the reliability.