• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.71-82
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• 1991

• The Journal of the Acoustical Society of Korea
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• v.11 no.3
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• pp.74-82
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• 1992

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.572-577
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• 2011

The acoustic emission (AE) technique is a well-known non-destructive test technique, both in research and for industrial applications. It is mainly used to monitor the onset of cracking processes in materials and components. Predicting and preventing the crack phenomenon has attracted the attention of many researchers and has continued to provide a large incentive for the use of condition monitoring techniques to detect the earliest stages of cracks. In this research, goal is in grasping features of AE signal caused by crack growth. The envelope analysis with discrete wavelet transform (DWT) is used to find the characteristic of AE signal. To estimate feature of divided into three by crack length, the time waveform and the power spectrum were generated by the raw signals and the transferred signal processed by envelope analysis with DWT.

• Journal of Power System Engineering
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• v.14 no.6
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• pp.41-46
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• 2010

Acoustic emission (AE) technique is a well-known non-destructive test technique. Fatigue crack growth test was performed using SM53C to check up the AE signal occurred by crack growth, so AE system was used to detect the crack signal. Features calculated by the AE signals were analyzed to evaluate the steps divided the crack growth into three. The steps, initiation, growth and breaking, were separated by velocity of the crack growth. Time waveform and power spectrum were created by the AE signal of each one of the steps and compared. In the feature domains, it was found that AE values changed rapidly as the velocity of the crack increasing.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.555-561
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• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.92-99
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• 2001

To measure temporal variability of long- range transmission in northern part of the East Sea of Korea, low frequency acoustic sources were deployed on the continental shelf 0.4km south of Cape Shultz near the port of Vladivostok during October 1999. The transmissions of the phase modulated signals were recorded by VLA moored on the northern slope of Ulleung-do. The measured signals were processed for the acoustic arrivals and their variability in time. The temporal signal processing involves pulse compression of the phase-encoded signal, time spread and temporal coherence processing. Variability of the ocean sound speed field in time scales of short period seems to be dominated by random fluctuations caused by sound speed perturbation due to the vertical displacements associated with internal waves.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.177-185
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• 2019

In this paper, we propose and evaluate a FDE combined with STBC transmission structure to cancellation of ISI in underwater acoustic communication. To achieve this purpose, underwater acoustic channels are modeled and the simulation results are presented. In case of STBC-FDE, the transmission rate is less about 4% than STBC-OFDM, but the SER performance is better than STBC-OFDM that is larger from 4.4% to 16.8% at the SNR of 15dB than STBC-OFDM.

• Transactions of Materials Processing
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• v.18 no.2
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.191-196
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• 2016

A development on the indoor positioning technologies and services has been proceeded very actively. Among the several positioning technologies, the TDOA(Time Difference of Arrival) technology using acoustic signal has the best positioning performance. Because so many people use their own smartphones, the location of the smartphone is important, and the TDOA technology should be employed to use the acoustic signal for the positioning. For the digital signal processing with the acoustic signal, the signal should be sampled, and as the sampling rate increases, the positioning accuracy could be improved instead of processing time burden. In this paper, the position estimation error according to the sampling rate is analyzed, and the appropriate sampling rate for the positioning system is proposed.