• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.826-828
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• 2014

Recently trend of product is miniaturization. As a result, We need products surface as well as products internal defect inspection. Generally, Inspection products in production process uses a lot of optical inspection. However, This is difficult to internal inspection of products. We used optical device instead of X-ray generator. At the same time, We have developed system to determine the product defect. First, obtain X-ray image from Machine vision function. Next, Measured value is recognize suitability within error range. otherwise recognize defect. Results presence of defective products can be stored by user.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.145-146
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• 2006

본 논문은 부분방전(PD: Partial Discharge)의 패턴인식 확률 극대화를 목적으로 신경망(NN: Neural Network) 파라미터 중에서 은닉층 뉴런의 수, 모멘텀(momentum)의 Step size와 Decay rate 를 최적화하기 위하여 유전 알고리즘(GA: Genetic Algonthm)을 적응하였다. 실험적 연구의 대상으로서, GIS(Gas Insulated Switchgear)사고의 주요 원인으로 보고되어있는 결함들을 인위적으로 모의한 16개 Test cell을 이용하여 부분방전을 발생시켰다. 부분방전 신호는 본 연구팀이 개발한 센서를 이용하여 검출되어 데이터베이스가 구축되어 그로부터 추출된 학습 데이터들의 학습에 다음과 같은 5가지 신경망 모델이 적응되었다: Multilayer Perception (MLP), Jordan-Elman Network (JEN), Recurrent Network (RN), Self-Organizing Feature Map (SOFM), Time-Lag Recurrent Network (TLRN). 유전 알고리즘 적용 효율성을 분석하기 위하여 동일한 데이터를 이용하여 다음과 같은 두 가지 방법을 적용한 결과를 상호 비교하였다. 우선 상기 선택된 모델만 적용하였고 다근 하나는 상기 모델과 Genetic Algorithm이 동시에 적용되었다. 모든 모델에 대하여 학습오차와 패턴 분류 확률을 비교한 결과, 유전 알고리즘 적응 시 부분방전 패턴인식 확률이 향상되었음이 확인되어 향후 신뢰성 있는 GIS 부분방전 진단기술에 활용될 수 있을 것으로 사료된다.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.15-20
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• 2005

Defects of dam body which can be induced in seepage or leakage procedure can directly affect dam safety. Therefore, a proper inspection method should be carried out in the first place to find out their positions and sizes, After that, some reinforcement works such as grouting and the corresponding assessment could be taken in a proper way. The dam(center core type earth dam) issued in this study has been in need for intensive diagnosis and reinforcement work, because a lot of slumps similar to cracks, seepage and some boggy area have been observed on the downstream slope. High resolution seismic reflection method was performed on the crest profile twice before and after grouting work(Aug. 2001 and Nov. 2004) aimed at the dam inspection and the assessment of grouting efficiency as well. To enhance the data resolution, P-beam energy radiation technique which can reduce the surface waves and hence to reinforce the reflection events was used. Strong reflection events were recognized in the stack section before grouting work, It seems that the events would be caused by e.g. horizontal cracks with a considerable aperture, Meanwhile such strong reflection events were not observed in the section after grouting. That is, the grouting work was dear able to reinforce the defects of dam body. Hence, the section showed an well arranged picture of dam inner structure. In this sense, seismic reflection method will be a desirable technique for dam inspection and for monitoring dam inner structure as well.

• Journal of the Korean Geophysical Society
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• v.8 no.1
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• pp.1-6
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• 2005

Defects of dam body which can be induced in seepage or leakage procedure can directly affect dam safety. Therefore, a proper inspection method should be carried out in the first place to find out their positions and sizes. After that, some reinforcement works such as grouting and the corresponding assessment could be taken in a proper way. The dam(center core type earth dam) issued in this study has been in need for intensive diagnosis and reinforcement work, because a lot of slumps similar to cracks, seepage and some boggy area have been observed on the downstream slope. High resolution seismic reflection method was performed on the crest profile twice before and after grouting work(Aug. 2001 and Nov. 2004) aimed at the dam inspection and the assessment of grouting efficiency as well. To enhance the data resolution, P-beam energy radiation technique which can reduce the surface waves and hence to reinforce the reflection events was used. Strong reflection events were recognized in the stack section before grouting work, It seems that the events would be caused by e.g. horizontal cracks with a considerable aperture. Meanwhile such strong reflection events were not observed in the section after grouting. That is, the grouting work was dear able to reinforce the defects of dam body. Hence, the section showed an well arranged picture of dam inner structure. In this sense, seismic reflection method will be a desirable technique for dam inspection and for monitoring dam inner structure as well.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.167-174
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• 1998

The ultrasonic test is recognized for its significance as a non-destructive testing method to detect volume defects such as porosity and incomplete penetration which reduce strength in the weld zone. This paper illustrates the defect detection in the weld zone of ferritic carbon steel using ultrasonic wave and the evaluation of pattern recognition by chaotic feature extraction using time series signal of detected defects as data. Shown in the time series data were that the time delay was 4 and the embedding dimension was 6 which indicate the geometric dimension of the subject system and the extent of information correlation. Based on fractal dimension and lyapunov exponent in quantitative chaotic feature extraction, feature value of 2.15, 0.47 is presented for porosity and 2.24, 0.51 for incomplete penetration The precision rate of the pattern recognition is enhanced with these values on the total waveform of defect signal in the weld zone. Therefore, we think that the ultrasonic pattern recognition method of weld zone defects of ferritic carbon steel by ultrasonic-chaotic feature extraction proposed in this paper can boost precision rate further than the existing method applying only partial waveform.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.17-22
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• 2011

This study proposes feature extraction method of pattern recognition by evaluation of weld defects in weld zone of titanium. For this purpose, analysis objectives in this study are features of attractor quadrant and fractal dimension. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as porosity of weld zone. These differences in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 0.87 and 1.00 in the case of part of 0.5 skip distance and 0.72 and 0.93 in the case of part of 1.0 skip distance were proposed on the basis of fractal dimensions. Attractor quadrant point, feature values of 1.322 and 1.172 in the case of ${\phi}1{\times}3mm$ porosity and 2.264 and 307 in the case of ${\phi}3{\times}3mm$ porosity were proposed on the basis of distribution value. The Proposed feature extraction of pattern recognition in this study can be used for safety evaluation of weld zone in titanium.

• Journal of the Korean Society of Safety
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• v.23 no.4
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• pp.7-12
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• 2008

This study proposes integrity evaluation method of weld zone in titanium using titanium distance amplitude characteristics curve(TDACC) and ultrasonic signals. For these purposes, the ultrasonic signals for porosity defects of weld zone in titanium are acquired in the type of time series data and echo strength. 4 lines in the DACC indicated damage evaluation standard of weld zone in titanium. The acquired ultrasonic signals agree fairly well with the measured results of reference block and sensitivity block(defect location, bean1 propagation distance, echo strength, etc). The proposed TDACC in this study agree fairy well with the measured results of weld zone in titanium(weld defects as porosity). The proposed TDACC in this study can be used for integrity evaluation of weld zone in titanium.

• Journal of the Korean Society for Railway
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• v.2 no.1
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• pp.38-46
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• 1999

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objective in this study is fractal dimension and attractor quadrant feature. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These differences in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor feature extraction in this study can enhance the precision rate of ultrasonic evaluation for defect signals of rail weld zone such as side hole and crack.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.10a
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• pp.280-283
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• 1995

계층형 신경회로망은 학습능력이나 비선형사상능력을 가지고 있고, 그 특징을 이용하여 패턴인식이나 동정 및 제어 등에의 적용이 시도되어 성과를 올리고 있다. 현재, 그 학습법으로 널리 이용되고 있는 것이 역전파학습법으로 최급 강하법이나 공액경사법 등의 최적화 방법이 적용되고 있지만, 학습에 많은 시간이 걸리는 점, 국소적 최적해(local minima)에 해의 수렴이 이루어져 오차가 충분히 작게 되지 않는 점 등이 문제점으로 지적되고 있다. 본 논문에서는 Hu에 의해 고안된 random 탐색법과 조합된 random tabu 탐색법으로 최적결합계수를 구하는 학습알고리즘으로, 국소적 최적해에 수렴하는 것을 방지하고, 수렴정도를 개선하는 새로운 방법을 이용하여 회전기계의 이상진동진단에 적용가능성을 검토하고 오차역전파법에 의한 진단결과와 비교검토한다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.