• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• Proceedings of the KIEE Conference
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• 2002.07c
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• pp.1691-1693
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• 2002

Acoustic partial discharge detection has been used on GIS testing for more than 10 years. Sensitivities show that most of critical defect types can be detected with a reasonable signal-to-noise ratio. The paper reports on acoustic partial discharge detection comparable to sensitive electrical PD(partial discharge) measuring method.

• Journal of Biosystems Engineering
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• v.22 no.1
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• pp.41-48
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• 1997

A nondestructive quality inspection technique using acoustic impulse response method was studied to investigate the feasibility of egg shell inspection. An experimental system was built to generate impact force, to measure the response signal and to analyze the frequency spectrum. This system includes an impulse generating unit, an egg holding seat, a microphone with preamplifier, and a digital oscilloscope connected to Personal Computer by RS-232C interface. The factors such as impulse generating method, egg holding method, and sensor location were evaluated by analyzing the power spectrum density of the measured signal. The results obtained are summarized as follows : 1. From the sampled eggs, the proper conditions for detecting damaged eggs were found as followings; ceramic for the impact ball material, rubber for egg seat material, 20 degrees for an impact angle of pendulum, 10mm for the distance between egg and sensor, the sharp side for impacting part, and 180 degrees for the location of sensor. 2. Examination of the Fourier transformed analysis in beth normal and damaged eggs revealed that those factors such as the resonant frequency, a number of peak frequencies and the magnitude of power spectrum were important to detect damaged eggs.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.485-492
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• 2009

The thermal fatigue crack(TFC) is one of the life-limiting mechanisms at the nuclear power plant operating conditions. In order to evaluate the structural integrity, various non-destructive test methods such as radiographic test, ultrasonic test and eddy current are used in the industrial field. However, these methods have restrictions that defect detection is possible after the crack growth. For this reason, acoustic emission testing(AET) is becoming one of powerful inspection methods, because AET has an advantage that possible to monitor the structure continuously. Generally, every mechanism that affects the integrity of the structure or equipment is a source of acoustic emission signal. Therefore the noise filtering is one of the major works to the almost AET researchers. In this study, acoustic emission signal was collected from the pipes which were in the successive thermal fatigue cycles. The data were filtered based on the results from previous experiments. Through the data analysis, the signal characteristics to distinguish the effective signal from the noises for the TFC were proven as the waveform difference. The experiment results provide preliminary information for the acoustic emission technique to the continuous monitoring of the structure failure detection.

• Proceedings of the KSLP Conference
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• 2003.11a
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• pp.208-211
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• 2003

This paper introduces a history and achievements of the research activities on screening of laryngeal diseases using acoustic analysis. First domestic and international research trends are introduced. Next brief introduction of the research results by the authors are mentioned. First, classification method of the laryngeal diseases using neural network is summarized. Then similar research using ARS (Automatic Response System) is mentioned. Finally, current research activities on screening of laryngeal diseases on internet is introduced.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1918-1920
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• 2000

In this paper, acoustic signals in GIS were analyzed by using wavelet transform and FFT to distinguish sound source caused by collision of particles and partial discharges. As a result, the analysis using wavelet transform was more accurate than that using FFT. Therefore, wavelet transform was useful technique to analyze the acoustic signals in GIS.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.7-14
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• 2010

AE(acoustic emission) sensor has been used for a state monitoring and observation during a ultra-precision machining because AE signal, which has high frequency range, is sensitive enough. In case of ceramic fabrication, a monitoring of machining state is important because of its hard and brittle nature. A machining characteristic of ceramic is susceptibly different in accordance with variable machining conditions. In this study, Yttria($Y_2O_3$) ceramic was fabricated using the ultra-precision lapping process with in-process electrolytic dressing(IED) method. And the surface machining characteristic and AE sensor signal were compared and analyzed.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.262-267
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• 2007

Detection of gas leak from a tube is a very important issue in the quality control of machines such as the heat exchanger of an air-conditioner, because leakage of operating gas directly reduces the performance of machines. The acoustic emission (AE) method is a common way to detect leak of gas, however its application under the environment of mechanical vibration is restricted since most AE detectors are very sensitive to external vibration noise. In order to overcome this problem, we propose a method based on the mode analysis of the Lamb wave. In this method, the dominant Lamb mode and its frequency are found first, and then a proper band-pass filter is used to retain only this frequency component. In this way, we could improve the SNR (signal-to-noise ratio) of AE signal generated by gas leak from the tube even under vibratory environment.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.1910-1920
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• 1993

An experimental study to investigate the unconventional chip formation called triangulation of chip in cutting with a SPRT (self-propelled rotary tool) is performed using acoustic emission (AE) signal analysis. In doing that, a quantitative model of the AE RMS signal in triangulation with a SPRT is first developed. The predicted results from this model show good correlation between the AE RMS signal and the general characteristics of triangular chip formation. Then, effects of various process parameters such as cutting conditions (cutting speed, depth of cut, oblique angle and normal rake angle) and the work material properties on the chip formation in cutting with a SPRT are explored. Special attention is paid to the work material properties which are found to have significant effects on triangulation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.