• IE interfaces
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• v.2 no.1
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• pp.47-58
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• 1989

Many decision problems in the real world have uncertainty and complexity. In many cases, decision makers do not have decision-analytic knowledge enough to solve a given decision problem. This paper developes a Decision Support System(DSS) that can be used for structuring decision problem into decision tree based on the concept of influence diagram and analyzing the decision problem by following Decision Analysis Cycle. This study suggests a DSS system(DACUL) in order to implement Decision Analysis Cycle using Lotus1-2-3. DACUL system has been developed in IBM XT/AT compatible PC.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.80-93
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• 2024

This study attempted to analyze the factors that influence the participation of beneficiary companies in the government's defense industry promotion support project. To this end, experimental data were analyzed by constructing a prediction model consisting of highly important variables in beneficiary company decisions among various company information using the decision tree model, one of the data mining techniques. In addition, various rules were derived to determine the beneficiary companies of the government's support project using the analysis results expressed as decision trees. Three policy measures were presented based on the important rules that repeatedly appear in different predictive models to increase the effect of the government's industrial development. Using the analysis methods presented in this study and the determinants of the beneficiary companies of the government support project will help create a sustainable future defense industry growth environment.

• Smart Structures and Systems
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• v.21 no.2
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• pp.249-255
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• 2018

Many studies in risk management have been focused on management process, contract relation, and risk analysis in the past decade, but very few studies have addressed project risks from the perspective of risk efficiency. This study started with using Fault Tree Analysis to develop a framework for the decision-making support system of risk management from the perspective of risk efficiency, in order for the support system to find risk strategies of optimal combination for the project manager by the trade-off between project risk and cost of project strategies. Comprehensive and realistic risk strategies must strive for optimal decisions that minimize project risks and risk strategies cost while addressing important data such as risk causes, risk probability, risk impact and risk strategies cost. The risk management in the construction phase of building projects in Taiwan upon important data has been analyzed, that provided the data for support system to include 247 risk causes. Then, 17 risk causes were extracted to demonstrates the decision-making support system of risk management from the perspective of risk efficiency in building project of Taiwan which could reach better combination type of risk strategies for the project manager by the trade-off between risk cost and project risk.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.91-102
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• 2000

Nowadays, every kind of system is changed so complex and enormous, it is necessary to assure system reliability, product liability and safety. Fault tree analysis(FTA) is a reliability/safety design analysis technique which starts from consideration of system failure effect, referred to as “top event”, and proceeds by determining how these can be caused by single or combined lower level failures or events. So in fault tree analysis, it is important to find the combination of events which affect system failure. Minimal cut sets(MCS) and minimal path sets(MPS) are used in this process. FTA-I computer program is developed which calculates MCS and MPS in terms of Gw-Basic computer language considering Fussell's algorithm. FTA-II computer program which analyzes importance and function cost of VE consists. of five programs as follows : (l) Structural importance of basic event, (2) Structural probability importance of basic event, (3) Structural criticality importance of basic event, (4) Cost-Failure importance of basic event, (5) VE function cost analysis for importance of basic event. In this study, a method of initiation such as failure, function and cost in FTA is suggested, and especially the priority rank which is calculated by computer-aided decision analysis program developed in this study can be used in decision making determining the most important basic event under various conditions. Also the priority rank can be available for the case which selects system component in FMEA analysis.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.463-475
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• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.662-673
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• 2012

This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.120-126
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• 2014

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.73-82
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• 2012

Currently, R&D investment of government is increased dramatically. However, the budget of the government is different depending on the size of ministry and priorities, and then it is difficult to obtain consensus on the budget. They did not establish decision support systems to evaluate and execute R&D budget. In this paper, we analyze factors affecting research funds by linear regression and decision tree analysis in order to increase investment efficiency in national research project. Moreover, we suggested strategies that budget is estimated reasonably.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.275-284
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• 2014

Decision Tree is one of analysis techniques which conducts grouping and prediction into several sub-groups from interested groups. Researcher can easily understand this progress and explain than other techniques. Because Decision Tree is easy technique to see results. This paper uses CART algorithm which is one of data mining technique. It used 273 variables and 70094 data(2010-2011) of working environment survey conducted by Korea Occupational Safety and Health Agency(KOSHA). And then refines this data, uses final 12 variables and 35447 data. To find satisfaction factor in working environment, this page has grouped employee to 3 types (under 30 age, 30 ~ 49age, over 50 age) and analyzed factor. Using CART algorithm, finds the best grouping variables in 155 data. It appeared that 'comfortable in organization' and 'proper reward' is the best grouping factor.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.23-30
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• 2015

As BigData is issued, many applications that operate based on the results of data analysis have been developed, typically applications are products recommend service of e-commerce application service system, search service on the search engine service and friend list recommend system of social network service. In this paper, we suggests a decision frequent pattern tree that is combined the origin frequent pattern tree that is mining similar pattern to appear in the data set of the existing data mining techniques and decision tree based on the theory of computer science. The decision frequent pattern tree algorithm improves about problem of frequent pattern tree that have to make some a lot's pattern so it is to hard to analyze about data. We also proposes to model for a Mapredue framework that is a programming model to help to operate in distributed environment.