• 산업경영시스템학회지
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• 제38권2호
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• pp.82-90
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• 2015

Location selection of distribution centers is a crucial task for logistics operators and key decision makers of an organization. This is a multi-criteria decision making (MCDM) process which includes both quantitative and qualitative criteria. In order to propose an optimized location selection model, this research suggests a hierarchical group of evaluation criteria : 5 major criteria with 15 sub-criteria. The MCDM approach presented in this research, by integrating Grey Relational Analysis (GRA) with Analytic Hierarchy Process (AHP), tends to rectify the overall quality and uncertainty of the values of evaluation criteria. An example of a location selection case in Korea is illustrated in this study to show the effectiveness of this method.

• 한국건설관리학회논문집
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• 제17권5호
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• pp.22-34
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• 2016

현재 국내 공공건설사업의 발주제도는 입 낙찰제도와 뚜렷한 구분이 없이 혼용되고 있으며, 선정할 수 있는 기준 자체도 단순히 사업예산이나 추정금액에 의해 정해지고 있다. 발주방식은 본래 사업의 특성이나 유형, 목적 등 다양한 요인들의 특성을 적절히 반영하여 결정해야 함에도 불구하고, 주어진 예산이나 기간, 획일적인 법 규정으로 인해 탄력적으로 운영되지 못하고 있어, 이에 대한 근본적인 해결방안을 마련해야 할 필요성이 지속적으로 제기되고 있다. 이를 위해, 본 연구는 최적 발주방식을 선정할 수 있는 의사결정지원모델을 제안하였다. 이를 위해 문헌고찰과 설문 및 면담조사, 통계적 분석기법을 활용하여 영향요인을 발굴하고, 최종 발주방식 유형별 의사결정지원모델을 제안하여, 실무 적용타당성을 검증하였다. 그 결과 의사결정지원모델은 향후 발주방식을 선정하는데 기초자료로 유용하게 활용함으로써 기존 업무관행을 개선할 수 있을 것으로 기대한다.

• 대한토목학회논문집
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• 제12권1호
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• pp.131-140
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• 1992

다목적댐의 유입량 예측과 더불어 유출량의 이용율을 극대화 하면서 홍수 피해를 극소화 시킬 수 있는 방류량을 결정할 수 있는 실시간(實時間) 홍수제어(洪水制御) 문제에 있어서는 수문(水文) 및 기상자료 등 많은 정보의 실시간(實時間) 온라인 취득과 컴퓨터를 사용한 분석이 필수적이다. 입수된 자료의 정확한 분석으로부터 내용이 압축된 컬러 그래픽 등 사람과 컴퓨터간의 대화매체를 도입하면 홍수방류를 결정할 수 있는 책임자에게 분석된 정보를 보다 쉽고 신속하게 전달할 수 있다. 개발된 PC-REFCON은 개인용 컴퓨터를 주축으로 한 실시간 홍수예측 및 저수지 운영을 위한 쇄신된 의사결정 지원 시스템으로서, 자료의 실시간 취득과 가공을 위한 데이타 베이스와 유입량 예측과 댐 방류량 결정을 위한 모형을 포함하였을 뿐 아니라 지금까지와는 전혀 새로운 차원으로 모든 정보를 그래픽과 테이블로 제공하여 주는 대화형 시tm템으로 구성되었다. PC-REFCON은 1992년부터 우리나라의 9개 전 다목적댐 저수지를 홍수시에 실시간으로 홍수량 예측과 방류량을 결정할 수 있는 시스템으로 이용될 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.93-111
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• 2020

Purpose The purpose of this paper is to observe the relative priorities of importances among the modified versions of Block chain system, being based on AHP decision support model which should be also proposed in this paper. Design/methodology/approach Four versions modified from the beginning of Block chain were divided into Public& Permissionless, Private&Permissionless, Public&Permissioned and Private&Permissioned types. Five criteria for evaluating the four versions whether the version were suitable for Medical information system were introduced from five factors of Technologies Accept Model, which were Security, Availability, Variety, Reliability and Economical efficiency. We designed Decision support model based on AHP which would select the best alternative version suitable for introducing the Block chain technology into the medical information systems. We established the objective of the AHP model into finding the best choice among the four modified versions. First low layer of the model contains the five factors which consisted of Security, Availability, Variety, Reliability and Economical efficiency. Second low layer of the model contains the four modified versions which consisted Public&Permissionless, Private&Permissionless, Public&Permissioned and Private& Permissioned types. The structural questionnaire based on the AHP decision support model was designed and used to survey experts of medical areas. The collected data by the question investigation was analyzed by AHP analysis technique. Findings The importance priority of Security was highest among five factors of Technologies Accept Mode in the first layer. The importance priority of Private&Permissioned type was highest among four modified versions of Block chain technologies in second low layer. The second importance priority was Private&Permissionless type. The strong point of Private&Permissioned type is to be able to protect personal information and have faster processing speeds. The advantage of Private& Permissionless type is to be also able to protect personal information as well as from forging and altering transaction data. We recognized that it should be necessary to develop new Block chain technologies that would enable to have faster processing speeds as well as from forging and altering transaction data.

• 한국콘텐츠학회논문지
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• 제21권8호
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• pp.163-176
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• 2021

본 연구는 급변하는 시장환경에서 기업의 의사결정에 영향을 미치는 요인을 찾기 위해 직원들이 지각한 조직공정성과 정보시스템 품질이 흡수역량을 통하여 의사결정의 질에 미치는 영향 관계를 살펴보고자 하였다. 이를 위하여 중소기업 직원들을 대상으로 239부의 설문데이터를 수집하였고, SPSS 22.0과 PLS 3.0을 사용하여 연구가설을 검증하였다. 연구결과 조직공정성과 정보시스템 품질이 흡수역량에 개별적으로 정(+)의 영향을 나타내었고, 흡수역량은 의사결정의 질에 정(+)의 영향을 나타내었다. 본 연구를 통하여 조직공정성과 정보시스템 품질이 흡수역량의 선행요인이 되는 이론적 토대를 마련하였고, 인적자원에 동기부여가 되는 조직공정성과 정보시스템 품질에 대한 종합적인 분석을 통하여 중소기업의 의사결정의 질을 높여 경쟁력을 확보하는 이론적, 실무적 시사점을 제시하고자 하였다. 향후 연구에서는 정보시스템 품질에 대한 추가적인 연구과 의사결정의 질로 인한 성과부분에 다양한 연구가 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.217-224
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• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

• 디지털융복합연구
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• 제14권3호
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• pp.209-217
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• 2016

최근 경제성장과 더불어 삶의 질이 향상됨에 따라 야외활동이 증가되었으며, 야외활동의 진행여부 의사결정은 기상여건과 밀접한 관계를 갖고 있다. 현재 이러한 야외활동 의사결정은 기상청의 일기예보와 주관적인 경험에 의해 결정되어지고 있다. 따라서, 야외활동 의사결정을 위해 기상정보를 기반으로 객관적 근거를 제시할 수 있는 분석 방법이 필요하다. 논문에서는 데이터마이닝을 기반으로 기상정보를 분석하여 야외활동 의사결정을 지원할 수 있는 기상정보 분석 알고리즘을 제안한다. 또한, 프로야구 일정 히스토리와 자동기상관측장비의 관측 자료를 데이터마이닝의 분류 알고리즘을 적용하여 실험을 수행하고, 제안한 알고리즘의 향상된 성능을 검증하였다.

• Journal of Information Technology Applications and Management
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• 제17권3호
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• pp.163-189
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• 2010

The purpose of this research is to develop a decision-making model for the calculation of the National Disaster Management System's standard prevention workforce quota. The final purpose of such model is to support in arranging a rationally sized prevention workforce for local authorities by providing information about its calculation in order to support an effective and efficient disaster management administration. In other words, it is to establish and develop a model that calculates the standard disaster prevention workforce quota for basic local governments in order to arrange realistically required prevention workforce. In calculating Korea's prevention workforce, it was found that the prevention investment expenses, number of prevention facilities, frequency of flood damage, number of disaster victims, prevention density, and national disaster recovery costs have positive influence on the dependent variable when the standard prevention workforce was set as the dependent variable. The model based on the regression analysis-which consists of dependent and independent variables-was classified into inland mountainous region, East coast region, Southwest coastal plain region to reflect regional characteristics for the calculation of the prevention workforce. We anticipate that the decision-making model for the standard prevention workforce quota will aid in arranging an objective and essential prevention workforce for Korea's basic local authorities.

• 디지털산업정보학회논문지
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• 제11권4호
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• pp.33-45
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• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.