• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.118-124
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• 1996

Signal frequency lines generated from the acoustic targets are of particular importance for target detection and classification in passive sonar systems. The underwater noise consists of a mixture of ambient noise and radiated noise of targets. Detction of exact signal frequency lines depends on signal detection threshold and variation of ambient noise. In this paper, a detection method of signal frequency lines for acoustic targets using autoassociative memory (ASM) neural network, which is not sensitive to variation of signal detection threshold and ambient noise, is proposed. It is confirmed by simulation and application of real acoustic targets that the proposed method shows good performance for detection of signal frequency lines.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.15-24
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• 1995

This paper describes a system that can be used to recognize an unknown material regardless of the fuzzy neural network(FNN). There are some problems to realize the recognition system using temperature response. It requires too many memories to store the vast temperature response data and it has to be filtered to remove noise which occurs in experiment. And the temperature response is influenced by the change of ambient temperature. So, this paper proposes a practical method using curve fitting to remove above problems of memories and noise. and FNN is proposed to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient. Temperatures and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be recognized by the thermal conductivity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.611-614
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• 2004

In this study, ambient vibration tests are carried out to replace the current bridge integrity assessment using controlled vehicle test, which requires the traffic control and may induce public complains. Ambient vibration tests and output-only modal identification can be very effective approach to evaluate the bridge integrity because the ambient vibration tests can be performed very easily without trafnc control. The bridges in test road of Jungbu Inland Highway were tested and the results are discussed here.

• Geophysics and Geophysical Exploration
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• v.18 no.2
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• pp.85-90
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• 2015

Orientation corrections for Korean seismic stations were calculated by using ambient noise cross-correlation. This method uses Rayleigh waves extracted from ambient noise cross-correlation instead of teleseismic waveforms from earthquakes, which have been generally used for previous studies. The theoretical background of the method is that the phase of radial-vertical cross-correlation function should be the same as that of $90^{\circ}$ phase-shifted vertical-vertical cross-correlation function. The results calculated from stacked cross-correlograms from Jan. 2007 to Sep. 2008 are comparable to the previous results obtained from teleseismic waveforms. In addition, overall the standard deviations of orientation corrections are less than $5^{\circ}$. The temporal variation in orientation corrections calculated for every 30 days shows no significant change and also standard deviations of them are mostly less than $5^{\circ}$. This means that the orientations of stations used in this study have been kept constant during the period. The sensitivity test for stacking period of the ambient noise cross-correlation method shows that continuous ambient noise record of at least about 30 days is required for estimating reliable orientation corrections.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.209-220
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• 2014

In this paper, we analyze the discriminability of features for the classification of transient signals with an ambient noise in a shallow water. For the classification of the transient signals, robust features for the variance of a noise are required due to a low SNR under a marine environment. In the modelling the ambient noise in shallow water, theoretical noise model, Wenz's observation data from the shallow water, and Yule-walker filter are used. Discrimination of each feature of the transient signals with an additive ambient noise is analyzed by utilizing a Fisher score. As the analysis of a classification accuracy about the transient signals of 24 classes using the selected features with a high discriminability, the features selected in the environment without a noise relatively have a good classification accuracy. From the analyzed results, we finally select a total 16 features out of 28 features. The recognition using the selected features results in the classification accuracy of 92% in SNR 20dB using Multi-class SVM.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.246-250
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• 2014

Large mutual capacitance touch screen panels (TSP) are susceptible to display and ambient noise. This paper presents a multi-touch detection algorithm using an efficient noise compensation technique for large mutual capacitance TSPs. The sources of noise are presented and analyzed. The algorithm includes the steps to overcome each source of noise. The algorithm begins with a calibration technique to overcome the TSP mutual capacitance variation. The algorithm also overcomes the shadow effect of a hand close to TSP and mutual capacitance variation by dynamic threshold calculations. Time and space filters are also used to filter out ambient noise. The experimental results were used to determine the system parameters to achieve the best performance.

• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.47-51
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• 1991

it is well observed in the ocean that the surface disturbances due to rain, wind and breaking waves generate bubble clouds several meters deep from the water surfaces. Thses kinds of bubble clouds can work as a physical mechanism to produce underwater ambient noise. In the laboratory experiment observing the noise generated from a bubble cloud we showed a role of individual bubbles in collective oscillations of a bubble cloud. The experimental data agree very well with the theoretical predictions. These results confirm that the collective oscillations of a bubble cloud is one of the more likely mechanisms for an ocean ambient noise source around several hundred hertz.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.396.1-396
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• 2002

The AVT under traffic-induced vibrations was carried out on Namhae Suspension bridge in Korea. Mode shapes as well as natural frequencies up to the 15th mode were acquired exactly, and the effect of traffic mass and temperature on measured natural frequencies was investigated. The results from the AVT are compared with those from forced vibration test(FVT) and FE analysis. (omitted)

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.385.1-385
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• 2002

Operational Modal Analysis also known as Ambient Modal Analysis has an increasing interest in mechanical cngineering. Especially on big structures where the excitation and not less important the determination of the forces is most often a problem. In a structure like a wind turbine wing where the modes occur both close in frequency and bi-directional the Ambient excitation has big advantages. (omitted)

• 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.