• 한국통신학회논문지
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• 제27권4C호
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• pp.316-325
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• 2002

This paper investigates the restoration schemes which are applied to the MPLS domain upon a network failure. We define the following three restoration service models by combining the various restoration schemes: "FIS-based protection service" (FIS: failure indication signal), "inversion traffic protection service" and "1+1 protection service". After a qualitative analysis of the performance in them, we have analyzed it on quantitative basis by the simulation. According to the simulation results, "1+1 protection service" guarantees the fastest and most lossless restoration service among them; however, it results in consuming considerable bandwidth and producing an amount of control traffic, which means poor network utilization. On the other hand, "FIS-based protection service" spends less bandwidth and generates less control traffic, which means better network utilization, but produces poor restoration service. "Inversion traffic protection service" provides the medium restoration service and utilization between "1+1 protection service" and "FIS-based protection service."

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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• pp.66-73
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• 2003

The technique for the seismic reliability evaluation of the electric power network is presented. In the previous study, the state of the substations was represented by the bi-state which is classified as failure or survival. However, the hi-state model can result in oversimplified analysis, because substations are worked by the parallel operating system. In this paper, Considering the characteristics of the parallel operating system, the damage of the substation is expressed by the multi-state for the more realistic seismic reliability evaluation. Using Monte-Carlo simulation method, the seismic reliability for Korean 345㎸ electric power network is evaluated. Analysis results show that reliability levels of the network by the multi-state analysis is higher than that by the hi-state analysis and the electric power network in southeastern area of the Korean Peninsular may be vulnerable to earthquakes.

• 융합정보논문지
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• 제10권7호
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• pp.41-48
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• 2020

최근 전국 각 지역 AMI(Advanced Metering Infrastructure) 원격검침 시스템의 보급사업이 활성화되고 있으며, 전력수요 관리를 위한 양방향 통신 및 보안 요금제 기능 등 다양한 계량 기능을 제공하고 있다. 현재 AMI 시스템은 새로운 내부 IoT 장비 및 네트워크 규모의 증가로 인해 기존 RDB(Relational Database) 기반 장애 분석이 어렵다. 본 연구는 기존 RDB 데이터를 활용하는 새로운 GDB(Graph Database)기반 장애 분석 방법을 제안한다. 내부 임계치와 상태 값 등 누적된 데이터를 통해 새로운 장애 패턴의 상관관계를 분석한다. GDB 기반 시뮬레이션 결과 RDB에서 분석이 어려웠던 새로운 장애 패턴을 예측할 수 있음을 확인하였다.

• International Journal of Korean Welding Society
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• 제1권2호
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• pp.51-60
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• 2001

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.762-766
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• 1998

Mechanical valve is one of the most widely used implantable artificial organs, Since its failure (mechanical failures and thrombosis to name two representative example) means the death of patient, its reliability is very important and early noninvasive detection is essential requirement . This paper will explain the method to detect the thrombosis formation by spectral analysis and neural network. In order quantitatively to distinguish peak of a normal valve from that of a thrombotic valve, a 3 layer backpropagation neural network, which contains 7,000 input nodes, 20 hidden layer and 1output , was employed. The trained neural network can distinguish normal and thrombotic valve with a probability that is higher than 90% . In conclusion, the acoustical spectrum analysis coupled with a neural network algorithm lent itself to the noninvasive monitoring of implanted mechanical valves. This method will be applied to be applied to the performance evaluation of other implantable rtificial organs.

• Geomechanics and Engineering
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• 제23권1호
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• pp.51-59
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• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.

• 한국컴퓨터정보학회논문지
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• 제1권1호
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• pp.25-36
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• 1996

본 논문은 격자 구조 회선 교환 망에서 발생할 수 있는 호 차단 확률 및 failure model을 설정하여 신뢰도를 분석하였다 특히 failure model에서는 link failure 모델을 고려하였다. 대상 모델은.flooding search routing 방식을 사용하여 패킷을 통화 대상자 노드에 전송하였다. 이때. 각 링크failure는 독립적이라고 가정하였다. 이와 같은 failure모델의 성능을 평가하기 위한 방법으로서 joint probability를 이용하여 소규모 격자 구조 회선 교환망의 신뢰도를 분석해 보았으며. 이를 시뮬레이션 한 값과 비교해 보았다 또한. 통신망에서 주요한 성능 지표중 하나8! 호 차단 확률을 구하여 회선망의 신뢰도를 평가하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.482-487
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• 1993

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO $_{2}$ Arc Welding using the finite element method. The validity of the above results is demonstrated by experimental elastic stress relief method which is called Holl Drilling Method. The first part of numarical analysis performs a three-dimensional transient heat transfer anslysis, and the second part then uses results of the first part and performs a three-dimensional transient thermo-clasto-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method were used to train a backpropagation neural network to predict residual stress. Architecturally, the finite element method were used to train a backpropagation voltage and the current, a hidden layer to accommodate failure mechanism mapping, and an output layer for residual stress. The trained network was then applied to the prediction of residual stress in the four specimens. The results of predicted residual stress have been very encouraging.

• 한국경영과학회지
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• 제21권2호
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• pp.141-170
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• 1996

The purpose of this paper is to critically review and compare the evaluation methods of fault tolerant MIN system and to suggest future study direction. The reliability analysis of MIN system is discussed. The system performance modeling under the faulty components is another main focus of this study.

• 한국컴퓨터정보학회논문지
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• 제24권7호
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• pp.125-133
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• 2019

Because military weapons systems for special purposes are small and complex, they are not easy to maintain. Therefore, it is very important to maintain combat strength through quick maintenance in the event of a breakdown. In particular, naval ships are complex weapon systems equipped with various equipment, so other equipment must be considered for maintenance in the event of equipment failure, so that skilled maintenance personnel have a great influence on rapid maintenance. Therefore, in this paper, we analyzed maintenance data of defense equipment maintenance information system through text mining and social network analysis(SNA), and tried to identify the naval ship maintenance expertise. The defense equipment maintenance information system is a system that manages military equipment efficiently. In this study, the data(2,538cases) of some naval ship maintenance teams were analyzed. In detail, we examined the contents of main maintenance and maintenance personnel through text mining(word cloud, word network). Next, social network analysis(collaboration analysis, centrality analysis) was used to confirm the collaboration relationship between maintenance personnel and maintenance expertise. Finally, we compare the results of text mining and social network analysis(SNA) to find out appropriate methods for finding and finding naval ship maintenance expertise.