• Journal of Information Technology Applications and Management
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• 제14권3호
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• pp.95-113
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• 2007

To minimize IT operational risks and the opportunity cost for lost business hours. it is necessary to have preparedness in advance and mitigation activities for minimization of a loss due to the business discontinuity. There are few cases that banks have a policy on systematic management, system recovery and protection activities against system failure. and most developers and system administrators response based on their experience and the instinct. This article focuses on the mitigation model development for minimizing the incidents of disk unit in IT operational risks. The model will be represented by a network model which is composed of the three items as following: (1) the risk factors(causes, attributes and indicators) of IT operational risk. (2) a periodic time interval through an analysis of historical data. (3) an index or an operational regulations related to the examination of causes of an operational risk. This article will be helpful when enterprise needs to hierarchically analyze risk factors from various fields of IT(information security, information telecommunication, web application servers and so on) and develop a mitigation model. and it will also contribute to the reduction of operational risks on information systems.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1569-1574
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• 2005

In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.

• 한국화재소방학회논문지
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• 제20권4호
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• pp.13-18
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• 2006

원자력발전소의 화재방호규정은 정성적인 화재위험성평가와 정량적인 화재위험도분석에 기반을 두며, 화재위험은 심층화재방어개념인 화재 예방, 화재 진압, 및 피해 최소화의 3가지 요소에 균형을 유지하면서 화재방호계획에 의해 관리되고 있다. 최근 화재위험 상세평가는 일반적으로 존모델 또는 필드모델을 이용하고 있다. 본 논문에서는 이런 추세에 따라 최신 화재모델링 도구인 FDS를 이용하여 원자력 발전소의 방화지역에 대한 정량적 화재위험분석 및 화재영향 평가가 가능한지 그 여부를 확인하였다. 이의 결과 화재모델링을 이용한 정량적 위험분석은 원자력발전소의 방화지역에 대한 정량적 위험도 분석뿐만 아니라 화재로 인한 원자로 노심 손상빈도를 개선할 수 있는 응용 도구로 활용될 수 있을 것으로 기대된다.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2007년도 International Conference
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

• 정보처리학회논문지D
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• 제14D권6호
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• pp.689-700
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• 2007

정보시스템 운영리스크를 최소화하고, 장애시간 동안의 영업기회 손실비용 규모를 줄이기 위해서는 장애의 예방과 사전준비가 필요하다. 그런데 장애가 발생할 경우, 대부분의 기업에서는 장애발생 직후에 대응과 복구 조치를 취하고 있다. 프로그램 개발자나 시스템운영자들은 과거의 경험과 직관에 의존하여 장애를 관리하고 있을 뿐, 장애를 체계적으로 관리하고 사전에 예방하는 사례를 찾아보기가 힘든 실정이다. 본 논문은 정보시스템 운영리스크의 관점에서, 디스크 장애예방을 위한 피해저감모델의 개발에 초점을 맞추었다. 연구모델은 디스크장치에서 정보시스템 운영리스크가 발생하는 위험원인, 그리고 이러한 원인들을 사전에 점검하는 점검주기, 점검에 필요한 운영규정으로 구성된다. 또한 정보시스템 부문의 하드웨어 장애요인 중에서 가장 크게 나타나고 있는 디스크 장애에 대하여 피해저감모델을 적용함으로써 활용 가능성을 보여 준다.

• 산업경영시스템학회지
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• 제34권4호
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• pp.89-97
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• 2011

Traditional quality control for manufacturing or service sector is not suitable for the quality control of a project as the project is one-time task constrained by time, cost, and quality. To meet the internal and external customers' requirements, quality costs approach to the project will be effective. Hence, we propose PONC (price of nonconformance) estimation procedure and a mathematical model, which are focused on activity-based prevention in the execution step and warranty step of EPLC (extended project life cycle). This procedure and model will help project manager develop preventive action plan for project quality costs minimization from nonconformance risk activities and PONC estimates information.

• 한국재난정보학회 논문집
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• 제17권2호
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• pp.245-253
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• 2021

연구목적: 본 연구는 화재발생 건축물 정보, 신고자 취득 정보 등 초기 정보를 활용하여 화재현장의 위험도를 예측하여, 재난 발생 초기에 효과적인 소방자원 동원 및 적절한 대응을 위한 피해최소화 전략 수립을 지원하는 위험도 예측 모델을 개발하고자 한다. 연구방법: 화재 통계 데이터 상에서 화재의 피해규모와 관련된 변수 규명을 위해 머신러닝 알고리즘을 이용한 변수간 상관성 분석을 실시하여 예측 가능성을 검토하고, 데이터 표준화 및 이산화 등의 전처리를 통해 학습 데이터 셋을 구축하였다. 이를 활용하여 예측 정확도가 높은 것으로 평가 받고 있는 복수의 머신러닝 알고리즘을 테스트하여 가장 정확도가 높은 알고리즘을 적용한 위험도 예측 모델을 개발하였다. 연구결과: 머신러닝 알고리즘 성능 테스트 결과 랜덤포레스트 알고리즘의 정확도가 가장 높게 나왔으며, 위험도 등급에 대해서는 중간치에 대한 정확성이 상대적으로 높은 것으로 확인되었다. 결론: 화재 통계 상 피해규모 데이터의 편향성에 의해 예측모델 정확도가 제한적으로 나타났으며, 예측 모델 성능 개선을 위해 데이터 정합성 및 결손치 보완 등을 통한 데이터 정제가 필요하다.

• ETRI Journal
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• 제27권6호
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.1-10
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• 2012

Supplier selection is an essential task within the purchasing function of supply chain management because it provides companies with opportunities to reduce various costs and realize stable and reliable production. However, many companies find it difficult to determine which suppliers should be targeted as each of them has varying strengths and weaknesses in performance which require careful screening by the purchaser. Moreover, information required to assess suppliers is not known precisely and typically fuzzy in nature. In this paper, therefore, fuzzy multi-objective linear programming (fuzzy MOLP) is presented under fuzzy goals: cost minimization, service level maximization and purchasing risk. To solve the problem, we introduce an enhanced two-phase approach of fuzzy linear programming for the supplier selection. In formulated problem, Analytical Hierarchy Process (AHP) is used to determine the weights of criteria, and Taguchi Loss Function is employed to quantify purchasing risk. Finally, we provide a set of alternative solution which enables decision maker (DM) to select the best compromise solution based on his/her preference. Numerical experiment is provided to demonstrate our approach.

• East Asian mathematical journal
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• 제37권3호
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.