• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.388-392
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• 2009

Operation of LNG (Liquefied Natural Gas) plants requires an effective maintenance strategy. To this end, the long-term and short-term trend of faults, such as mechanical and electrical troubles, should be identified so as to take proactive approach for ensuring the smooth and productive operation. However, it is not an easy task to predict the fault trend in LNG plants. Many variables and unexpected conditions make it quite difficult for the facility manager to be well prepared for future faulty conditions. This paper presents a model to predict the fault trend in a LNG plant. ARIMA (Auto-Regressive Integrated Moving Average) model is combined with Wavelet Transform to enhance the prediction capability of the proposed model. Test results show the potential of the proposed model for the preventive maintenance strategy.

• 한국인터넷방송통신학회논문지
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• 제18권3호
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• pp.151-157
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• 2018

소프트웨어 결함 예측에 관한 기존의 연구들은 대부분 모델의 입력 모듈이 결함을 가지고 있는지 여부를 판단하는 이진 감독형 분류 모델들에 관한 것들이었다. 하지만 이진 분류 모델은 결함의 복잡한 특성들을 고려하지 않고 단순히 입력 모듈의 결함 유무만을 판단한다는 문제점이 있고, 감독형 모델은 대부분의 개발 집단이 보유하고 있지 않은 훈련 데이터 집합을 필요로 한다는 한계점이 있다. 본 논문은 이러한 두 가지 문제점을 해결하기 위해 비감독형 알고리즘을 사용한 심각도 기반 삼진 분류 모델을 제안하였으며, 평가 실험 결과 제안 모델이 감독형 모델들에 필적하는 예측 성능을 보였다.

• 한국지진공학회논문집
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• 제20권1호
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• pp.45-54
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• 2016

This study suggests a prediction model of ground motion spectral shape considering characteristics of earthquake records in Korea. Based on the Graizer and Kalkan's prediction procedure, a spectral shape model is defined as a continuous function of period in order to improve the complex problems of the conventional models. The approximate spectral shape function is then developed with parameters such as moment magnitude, fault distance, and average shear velocity of independent variables. This paper finally determines estimator coefficients of subfunctions which explain the corelation among the independent variables using the nonlinear optimization. As a result of generating the prediction model of ground motion spectral shape, the ground motion spectral shape well estimates the response spectrum of earthquake recordings in Korea.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 1998년도 가을 학술발표회 논문집
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• pp.241-248
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• 1998

• Journal of Advanced Marine Engineering and Technology
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• 제30권2호
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• pp.253-259
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• 2006

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.251-256
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• 2005

There is few study which automatically diagnose the fault from ship's monitored signal. The bigger control and monitoring system is, the more important fault diagnosis and maintenance is to reduce damage brought forth by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault and is composed to fault detection knowledge base and fault diagnosis knowledge base. For this all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem by analyzing ship's operation data. To verifying capability of fault detection, diagnosis and prediction, Fault Management System(FMS) is developed by C++. Simulation experiment by FMS is carried out with population data set made by log book data of 2 months duration from a large full container ship of H shipping company.

• 한국인터넷방송통신학회논문지
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• 제19권3호
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• pp.127-133
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• 2019

소프트웨어 결함 예측 연구들의 대부분은 라벨 데이터를 훈련 데이터로 사용하는 감독형 모델에 관한 연구들이다. 감독형 모델은 높은 예측 성능을 지니지만 대부분 개발 집단들은 충분한 라벨 데이터를 보유하고 있지 않다. 언라벨 데이터만 훈련에 사용하는 비감독형 모델은 모델 구축이 어렵고 성능이 떨어진다. 훈련 데이터로 라벨 데이터와 언라벨 데이터를 모두 사용하는 세미 감독형 모델은 이들의 문제점을 해결한다. Self-training은 세미 감독형 기법들 중 여러 가정과 제약조건들이 가장 적은 기법이다. 본 논문은 Self-training 알고리즘들을 이용해 여러 모델들을 구현하였으며, Accuracy와 AUC를 이용하여 그들을 평가한 결과 YATSI 모델이 가장 좋은 성능을 보였다.

• 한국터널지하공간학회 논문집
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• 제16권4호
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• pp.361-372
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• 2014

터널 변위 계측은 전방의 단층대의 예측과 안정성 해석에서 매우 중요하다. 본 연구에서는 3차원 유한요소해석을 통해 터널 단층대의 방향과 존재에 따른 터널의 거동 특성을 평가하였다. 터널의 거동을 대표하는 계측치인 천단변위를 막장면에서 5 m 떨어진 지점에서 평가하였으며, 경향선과 L/C비 (종방향변위/천단변위)를 계산하여 통계적 관리기법인 x-Rs를 적용하여 단층대의 예측방안을 제시하였다. 결과적으로, x-Rs 관리도 기법이 전방 단층대의 존재와 방향을 예측하는데 활용될 수 있음을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1353-1355
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• 1996

This paper presents classification of high impedance fault pattern using linear prediction coefficients. A feature of neutral phase current is extracted by the linear predictive coding. This feature is classified into faults by a multilayer perceptron neural network. Neural network successfully classifies test data into three faults and one normal state.

• Structural Engineering and Mechanics
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• 제9권6호
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• pp.557-568
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• 2000

As time-variant reliability approaches become increasingly used for service life prediction of the aging infrastructure, the demand for computer solution methods continues to increase. Effcient computer techniques have become well established for the reliability analysis of structural systems. Thus far, however, this is largely limited to time-invariant reliability problems. Therefore, the requirements for time-variant reliability prediction of deteriorating structural systems under time-variant loads have remained incomplete. This study presents a computer program for $\underline{REL}$iability of $\underline{T}$ime-Variant $\underline{SYS}$tems, RELTSYS. This program uses a combined technique of adaptive importance sampling, numerical integration, and fault tree analysis to compute time-variant reliabilities of individual components and systems. Time-invariant quantities are generated using Monte Carlo simulation, whereas time-variant quantities are evaluated using numerical integration. Load distribution and post-failure redistribution are considered using fault tree analysis. The strengths and limitations of RELTSYS are presented via a numerical example.