• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.414-418
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• 1996

This paper aims at reviewing the possibility application over normal or abnormal, detection used by AE and the characteristics of grinding process. In this study, when diamond bur in dentistry with chosen grinding conditions were tuned at grinding. The variation of grinding resistance and AE signal is detected by the use of AE measuring system. The tests are carried out in accordance with diamond burs and workpiece: arcyl and cowteeth. According to the experiment results, the following can be expected; AE has the possibility to detect the state normality and abnormality. However, the grinding resistance measuring can find it difficult to detect it. It can be accurately excepted from AE occurrence pattern in contact start point of diamond but and cowteeth, grinding condition and derailment point. It is known that AErms is well compatible with grinding resistance.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.11
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• pp.125-129
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• 2004

For the diagnosis of compressor abnormal condition in air-conditioner, AE signal which is derived from wear condition, compressed air and assembly error is analyzed experimentally. The burst and continuous type AE signal occurred by metal contact and compressed air and AE raw signal of compressors were directly acquired in production line. After extracting samples according to waveforms, Early Life Test(ELT) is conducted and classified to normal and abnormal waveform. The efficient parameters of waveform pattern are investigated in time and frequency domain and the diagnosis algorithm of air-conditioner by Neural Network estimation is suggested.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.68-74
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• 1999

Ths paper does acoustic detection of partial discharge using acoustic sensor in polymer. Time sequential rreasurement of acoustic emission characteristic obtained acoustic sensor deal with statistics process. and 5 characteristic quantities were introduced into this paper. Resulting fann analysis of $\psi$-AEA-n pattern (phase-acoustic emission amplitude-pulse number) and AE quantities ,it can know useful statistics quantities that AE average inception amplitude TEX>$(\overline{AEA_{inc}})$ and AE average maximum amplitude TEX>$(\overline{AEA_{max}})$ make diagnosis of the middle stage of deterioration, AE pulse number and AE average maximum phase $(\overline{\theta{max}})$ make diagnosis of the last stage of deterioration. it obtained that these AE quantities are useful for dias,mosis deterioration form experiment results.esults.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.27-33
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• 2009

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's $\lambda$, D&B(Rij) & Tou are discussed.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.509-510
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• 2018

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.480-487
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• 2003

To improve environmental control in various plants, signal characteristics of plants have been studied by a nondestructive technique. In this paper, the acoustic emission (AE) from plants was analyzed for water stress dependency. AE signals were taken from gymnosperm and angiosperm. AE sensor detected AE signals from the plant stem underneath the plant surface below the sensor. AE hit-event counts in daytime were more than those in night time, and it was found that the daily hit counts pattern was strongly affected by the water stress in the plant. frequency bands of AE signals from the angiosperm was different from those from the gymnosperm. Frequency bands of AE in outdoor condition were in accord with those in indoor having similar conditions.

• Speech Sciences
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• v.8 no.2
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• pp.165-180
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• 2001

In this paper prosodic characteristics of French language are analysed with a corpus of radio interview. Intonation patterns are interpreted in terms of raising pattern, focal raising pattern and falling pattern. Accentual prominence is classified in two types, rhythmic accent and focal accent. Focal accent permit to explain the cohesion in a utterance or between two utterances. As a prosodic variable of discourse pauses are described by their form of realization (filled pause, silent pause, hesitation etc), their distribution and their function in utterance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.344-348
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• 1992

This paper presents a new methodology for on-line tool breakage detection by sensor fusion concept of an acoustic-emission (AE) sensor. A built-in piezoelectric force sensor was used to measure cutting force instead of a tool dynamometer to preserve the machine tool dynamics. he sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. When a tool is broken, the explicit changes of signals' pattern take place. A burst-type AE signal increases abruptly. Followingly, a cutting force drops significantly. Therefore a burst of AE signal is used as a triggering signal to inspect the following cutting force. Significant drop of cutting force is utilized to detect tool breakage. The algorithm was implemented in a DSP board for in-process tool breakage detection. The proposed monitoring system was capable of a good applicable tool breakage detection.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1659-1661
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• 2003

Recently, diagnostic techniques have been investigated to defect a partial discharge in high voltage electrical equipment. We have studied the characteristics of the acoustic partial discharge originating from the electrical defects in cubicle GIS(C-GIS). An acoustic emission(AE) sensor is used on the enclosure to detect partial discharge source because the sensor is sensitive to stress waves in its frequency range that may not be from a partial discharge source. AE signal is analyzed with phase-magnitude-frequency number(${\Phi}$-V-n) and pulse per second(PPS). Experience result has shown that the omitted acoustic signal has phase dependency and phase shift characteristic according to increase with applied voltage. These result will be helpful to the pattern recognition of the acoustic partial discharge in a C-GIS.