• Journal of the korean Society of Automotive Engineers
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• v.17 no.2
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• pp.35-41
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• 1995

본 연구는 자동차, 항공기 등의 수송체계의 설계에서 재료가 적용되는 과정을 통하여 설계에서의 재료의 기능 및 역할을 살펴보고, 최소의 비용으로 기능요건을 최대로 충족시키는 경제적 설계가 될 수 있도록 의사결정을 지원해주는 몇가지 분석기법을 소개하기로 한다. 1. 구조설계에서의 재료의 기능 및 역할. 2. 재료의 경제성 평가.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.427-449
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.651-652
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• 2009

• Korean Management Science Review
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• v.3
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• pp.3-8
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• 1986

인공지능 기법을 응용하는 한 분야로서 전문가시스템에 대한 일반적인 개념을 소개하였다. 응용분야에서 전문가의 의사결정 수준과 버금가는 능력을 부여할 수 있는 시스템의 특성을 고려하여 전문가들의 지식을 시스템에 묘사하는 방법과 이를 이용하는 방법에 관하여 논하였다. 또한 지식획득 과정의 특수성을 고려하여 전문가시스템의 개발방식에 관하여도 논하였다.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2001.06a
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• pp.79-79
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• 2001

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.71-80
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• 2011

Thresholding is important step for detecting binary change/non-change information in the unsupervised change detection. This study proposes new unsupervised change detection method using Hyperion hyperspectral images, which are expected with data increased demand. A graph is drawn with applying the range average method for the result value through pixel-based similarity measurement, and thresholding value is decided at the maximum distance point from a straight line. The proposed method is assessed in comparison with expectation-maximization algorithm, coner method, Otsu's method using synthetic images and Hyperion hyperspectral images. Throughout the results, we validated that the proposed method can be applied simply and had similar or better performance than the other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.386-390
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields. Classifying a group into subgroups is one of the most important subjects in data mining. Tree-based methods, known as decision trees, provide an efficient way to finding the classification model. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variable should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present some methods for measuring the node impurity, which are applicable to data sets with multivariate target variables. For illustration, a numerical cxample is given with discussion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.728-731
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• 2015

Nowadays, the majority of the manufacturing plant equipment tend to be transformed with automation system than traditional manual equipment. Production manager to make the process decision-making, not accustomed to manage and take advantage of professional decision-making technique. So rather than using the system proposed, it is common to undergo sporadic measures through the external intermediary. In this paper, whether possible delivery to match the delivery through the issue and decision-making system of the data input to be generated in the manufacturing process, review the supply information of the material used in the process, a plurality of input / output device We have proposed an input module that utilizes the Android Application and ATmega2560. As a result, easily cope with the improvement and change of process information of the work efficiency of the production business of the manufacturing process, and reduced plant data missing tasks, and management improvement to the improvement of the delivery compliance and product satisfaction by improving productivity we try to contribute to.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.63-77
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• 1996

This study suggests that experts' group decision support system (Experts'GDSS) should be helpful to solve specific planning/design problems. Dealing with AHP (Analytic Hierarchical Process), as a group decision model, maximal covering model, as a facility allocation model, and GIS (Geographic Information System; ARC/INFO in this study), as a spatial representation model, this study seeks to examine the implication of GDSS (Group Decision Support System) in the housing facility allocation. The results of this study clearly demonstrated that experts' GDSS acted its positive role, systematically providing the relative weights among the planning variables and objectives. Among different expert groups (e.g., planners, scholars, and public administrators), this study showed that there were certain differences in their decision structures, which generated different solutions in facility allocation. The further study, however, remains to investigate the more systematic implementation of GDSS in planning problems; for example, reducing the conflicts between different groups with different purposes.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.161-170
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• 2013

The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.