• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.91-91
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• 2003

Sliding wear behaviors of Inconel 600 and 690 were investigated at room temperature in air. In the present study, Archard's equation which has low reliability was modified. In the prediction of wear volume by Archard's equation, the reliabilities of Inconel 600 and 690 were about from 26.3% to 45.7% and from 69. l% to 88.6%, respectively, The sliding wear behaviors of Inconel 600 and 690 turned out to be influenced by their stacking fault energy, and the fact was confirmed by using TEM and micro-hardness test Based on experimental results, the wear coefficient was modified as a function of the sliding distance. The calculation with the modified wear equation showed that the reliability of Inconel 600 tested with 409 ferritic stainless steel increased from 45.7% to 93.4%.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.635-637
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• 1998

시험 단계나 운용 단계에서 발견된 소프트웨어의 오류를 수정하기 위해서는 많은 비용을 투자해야 한다. 시스템 개발 초기 단계인 설계 단계에서 소프트웨어 시스템의 신뢰도에 영향을 많이 미치는 부분을 찾아 오류를 사전에 방지하는 연구가 많이 진행되고 있다. 모듈의 신뢰도를 설계 단계에서 예측할 수 있다면 프로젝트 관리자는 결함 경향이 강한 모듈 개발에 더 많은 자원을 할당함으로써 보다 신뢰성 있는 소프트웨어를 생산 할 수 있다. 본 논문에서는 실시간 소프트웨어의 설계 결과에 대한 복잡도 측정치를 토대로 신뢰도를 예측하는 모델을 제안하다. 유전자 알고리즘으로 찾아낸 이 모델을 사용하여 결함 경향이 강한(fault prone) 모듈과 그렇지 않은 모듈은 96%의 정확도로 선별해 낼 수 있다.

• KIEAE Journal
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• v.10 no.4
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• pp.75-80
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• 2010

Generally, ground-source heat pump (GSHP) systems have a higher performance than conventional air-source systems. However, the major fault of GSHP systems is their expensive boring costs. Therefore, it is important issue that to reduce initial cost and ensure stability of system through accurate prediction of the heat extraction and injection rates of the ground heat exchanger. Conventional analysis methods employed by line source theory are used to predict heat transfer rate between ground heat exchanger and soil. Shape of ground heat exchanger was simplified by equivalent diameter model, but these methods do not accurately reflect the heat transfer characteristics according to the heat exchanger geometry. In this study, a numerical model that combines a user subroutine module that calculates circulation water conditions in the ground heat exchanger and FEFLOW program which can simulate heat/moisture transfer in the soil, is developed. Heat transfer performance was evaluated for 3 different types ground heat exchanger(U-tube, Double U-tube, Coaxial).

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.588-590
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• 2006

Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto-associative kernel regression by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression.

• Journal of Ship and Ocean Technology
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• v.4 no.4
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• pp.19-33
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• 2000

For the prevention of marine accidents based on human factor, the risk assessment analysis procedure is proposed which consists of (1) the structural analysis of marine accident, (2) the estimation of incidence probability based on the Fault Tree analysis, (3) the prediction of ef-fectiveness to reduced the accident risk by suitable countermeasures in the specified functional system, and (4) the risk assessment by means of minimizing of the total cost expectation and the background risk. As a practical example, the risk assessment analysis for preventing is investigated using the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1444-1446
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• 1999

In the calculation of lightning-induced voltage, it is used the various parameters obtained by LPATS being operated in KEPCO from 1995. Based on the lightning-induced voltage and the exact lightning position acquired by the developed program, we can predict the extent of damages in distribution systems. The result in this paper is very useful in finding fault location by lightning and performing rapid outage recovery and maintenance of distribution line equipments.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.244-250
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• 1997

The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent Property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability's or components are combined, which results in the dynamic MTTF or system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.364-366
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• 2018

본 논문에서는 Digital Twin 기반 건설기계 지능화를 위한 실시간 건설기계 데이터 처리 및 이상 유무 예측 시스템을 제안한다. 이 시스템은 빅 데이터 분산처리 기반으로 실시간 스트리밍 처리가 가능하며, CEP(Complex Event Processing)의 Sliding Window Operator를 활용한 Rule 적용을 통해 건설기계 데이터 처리 및 분석한다. 분석된 결과로 건설기계의 실시간 이상 유무를 판단할 수 있으며, 결과를 기반으로 Deep Learning 기술을 적용하고 학습된 모델을 통해 건설기계의 이상 유무를 예측하여 원활한 부품관리를 할 수 있다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.199-207
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• 2010

Recently, large amount of information in IDS(Intrusion Detection System) can be un manageable and also be mixed with false prediction error. In this paper, we propose a data mining methodology for IDS, which contains uncertainty based on training process and post-processing analysis additionally. Our system is trained to classify the existing attack for misuse detection, to detect the new attack pattern for anomaly detection, and to define border patter between attack and normal pattern. In experimental results show that our approach improve the performance against existing attacks and new attacks, from 0.62 to 0.84 about 35%.