• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.716-730
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• 2008

In Service-Oriented Architecture (SOA), service providers develop and deploy reusable services on the repositories, and service consumers utilize blackbox form of services through their interfaces. Services are also highly evolvable and often heterogeneous. Due to these characteristics of the service, it is hard to manage the faults if faults occur on the services. Autonomic Computing (AC) is a way of designing systems which can manage themselves without direct human intervention. Applying the key disciplines of AC to service management is appealing since key technical issues for service management can be effectively resolved by AC. In this paper, we present a theoretical model, Symptom-Cause-Actuator (SCA), to enable autonomous service fault management in SOA. We derive SCA model from our rigorous observation on how physicians treat patients. In this paper, we first define a five-phase computing model and meta-model of SCA. And, we define a schema of SCA profile, which contains instances of symptoms, causes, actuators and their dependency values in a machine readable form. Then, we present detailed algorithms for the five phases that are used to manage faults the services. To show the applicability of our approach, we demonstrate the result of our case study for the domain of 'Flight Ticket Management Services'.

• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.21-26
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• 2013

Currently almost people use a personal computer for various purpose. But some people are not familiar to computer system. If they see only black screen on the monitor when they turn on the computer power, they can not recognize whether it is hardware or software faults. So, in this paper is aimed to develop the module of computer hardware fault detecter using ROM BIOS before OS booting. This module use PCI interface with mother board of computer. Before os booting, it can get the ROM BIOS memory by interrupt and show what hardware is fault according to the predefined memory content of BIOS.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.2
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• pp.65-70
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• 2003

In this study, researchers developed the estimative algorithm for artificial defect in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages : Crack, Delamination and Normal. According to the results, we were confirmed that estimative algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.27-33
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• 1997

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.

• Proceedings of the KIEE Conference
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• 2008.04b
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• pp.128-130
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• 2008

제조물책임(Product Liability, 이하 PL)법은 2002년 7월 1일부터 국내에서 시행되고 있으며, 대기업뿐만 아니라 중소기업에서도 자사제품에 대한 신뢰성과 제품향상을 위해 많은 관심을 갖고 있다. PL법 환경에서 전력기기에 대한 제조결함과 사고의 연관성은 PL법의 적용대상이므로 국제화시대에 제품결함에 의한 전력설비 사고발생시 이의 정확한 원인규명 등을 통하여 기업에 있어서는 동일사고 예방 및 제품의 신뢰성 향상과 소비자에 있어서는 피해구제 마련 등 PL분쟁시 정확한 사고원인규명과 처리기준에 대한 자료구축이 필요한 실정이다.

• Proceedings of the KIEE Conference
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• 2008.05a
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• pp.145-146
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• 2008

초고압 케이블에서 발생하는 부분방전을 측정하기 위해 다양한 방법들이 연구 개발되어왔다. 최근에는 초고압 케이블의 설치 후 시행하는 준공시험에 있어 부분방전 측정을 필수적으로 할 만큼 부분방전 진단기술의 중요성이 부각되고 있는 실정이며, 디지털 측정기술을 통한 부분방전자동측정 기술이 많이 제안되고 있다. 특히, 비전문가들만으로도 진단 및 감시가 가능하도록 하는 자동 패턴 분류에 대한 다양한 연구에 활발히 보고되고 있다. 본 논문에서는 초고압 케이블에서 발생되는 결함을 내부, 외부, 노이즈의 세 가지로 분류하고 PRPD(Phase Resolved Partial Discharge) 형태로 모의된 실험데이터와 현장에서 축적된 데이터를 선별하여 다양한 통계치를 추출하였고, 결함별 구분이 용이하지 않은 통계치를 제외한 값들을 Neural Network 방법으로 학습시켰다. 학습된 가중치 값을 LabView로 작성된 프로그램에 사용하여 변전소 내 EBG에서 검출한 부분방전 측정 결과에 적용하였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.146-153
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• 2003

Ultrasonic inspection of defects has been focused on the existence of defect in structural material and need has much time and expenses in inspecting all the coordinates (x, y) on material surface. Neural networks can have an application to coordinates (x, y) of defects by multi-point inspection method. Ultrasonic inspection modeling is optimized by neural networks Neural networks has trained training example of absolute and relative coordinate of defects, and defect pattern. This method can predict coordinates (x, y) of defects within engineering estimated mean error $\psi$.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.68-68
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• 2004

기계기술의 지속적인 발달과 신기술의 개발로 인해 산업전반의 기반 기술인 기계 장치산업은 점점 복잡해지고 또한 다양화되면서 장치시설을 건전하고 신뢰성 있게 유지하고 관리하는 문제가 중요하게 대두되고 있다. 이중 가스 및 오일을 운송하는 배관은 대부분 지하에 매설되어 있고, 다양한 환경에 위치하여 있는데, 이러한 배관은 설치한지 오래되면 여러 가지 환경적 영향에 의해 부식과 같은 결함이 발생되고(Fig. 1과 Fig 2 참조) 이러한 결함이 성장하여 임계크기에 도달하여 대형 재난으로 발전하는 사고가 종종 보고 되고 있으며 이로 인한 경제적, 사회적 손실이 지대하기 때문에 매우 중요하게 인식되고 있다.(중략)

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.