• The Journal of Bigdata
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• v.8 no.1
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• pp.61-71
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• 2023

With the development of data-driven decision-making and sophisticated big data processing technique, there is a growing demand for information on how to process data. However, recent studies with data preprocessing mentioned only as a means to achieve a result. Therefore, in this study, we aimed to write in detail about the data processing pipeline, include preprocessing data. In particular, we shares the context and domain knowledge to aid fluent understand of the research.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.121-127
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• 2013

As the worldwide trade of agricultural products became liberalized with the establishment of WTO and FTA, relatively cheaper agricultural products have influenced the Korean domestic market substantially. Fortunately, foreign countries have soften their restrictions on Korean agricultural exports, providing Korean farmers with more opportunities to advance into the world market. This study aims to propose a multiple-criteria decision-making model for selecting an optimal tomato export farm, as the part of an effort to vitalize exports of domestic agricultural products amid the competitive agricultural market worldwide. For this purpose, we are suggesting a 2-step decision-making model which consists of a simple hierarchy decision model that preliminary selects tomato export farms and a detailed hierarchy decision model that chooses the final and optimal tomato export farm. We applied the analytic hierarchy process (AHP) to determine the relative importance of the used evaluation factors and to choose the tomato export farms with most potential. Eventually, the systematic and efficient decision-making model proposed in this paper can be applied to determine the optimal export farms for crops other than tomato, and thus it can encourage the competitiveness of Korean agricultural exports.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.212-216
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• 2011

Fault detection technique for photovoltaic power systems is significant to dramatically reduce economic damage in industrial fields. This paper presents a novel fault detection approach using Fourier neural networks and stochastic decision making strategy for photovoltaic systems. We achieve neural modeling to represent its nonlinear dynamic behaviors through a gradient descent based learning algorithm. Next, a general likelihood ratio test (GLRT) is derived for constructing a decision malling mechanism in stochastic fault detection. A testbed of photovoltaic power systems is established to conduct real-time experiments in which the DC power line communication (DPLC) technique is employed to transfer data sets measured from the photovoltaic panels to PC systems. We demonstrate our proposed fault detection methodology is reliable and practicable over this real-time experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.165-170
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• 2004

공간 데이터 웨어하우스에서 구축기는 의사절정을 위한 기반 데이터의 구축을 담당한다. 일반적으로 공간 데이터 웨어하우스의 데이터 적재는 잦은 갱신으로 인한 서버의 부하를 줄이기 위하여 구축기에 적재할 데이터를 임시 저장하고 일정주기마다 적재하는 방법을 이용한다. 이때 구축기의 정보는 차원테이블에 대한 갱신정보와 사실 테이블의 일부 갱신정보만을 유지하므로 여러 차원 테이블로 구성된 사실 테이블의 갱신은 공간 데이터 웨어하우스 서버에서 수행해야 한다. 사실 테이블의 갱신연산은 연관된 차원 테이블들에 의해 처리되므로 높은 처리 비용이 필요하다. 따라서 사실테이블의 처리로 인해 적재시간이 증가하며, 이는 사용자의 의사결정 응답시간을 증가시킨다. 본 논문에서는 공간 데이터 웨어하우스의 구축기에서 사실테이블의 사전 계산 기법을 제안한다. 이 기법은 차원 테이블 및 사실 테이블에 대한 메타정보와 추가적으로 기록되어야할 데이터 정보를 구축기에 유지한다. 구축기는 이 정보를 이용하여 삽입 연산시 사실 테이블에 적재할 갱신 정보를 사전에 계산하고, 이를 적재주기에 함께 적재한다. 따라서 사실 테이블의 신을 데이터 적재 이전에 구축기에서 계산하므로 공간 데이터 웨어하우스 서버에서 발생하는 높은 처리 비용을 감소시킬 수 있다. 공간 데이터 웨어하우스 사용자의 의사결정 응답시간을 감소시킨다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.254-260
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• 2017

The service life of civil infrastructure needs to be maintained or extended through appropriate inspections and maintenance planning, which results from the optimization process. A multi-objective optimization process can lead to more rational and flexible trade-off solutions rather than a single-objective optimization for the service life management of civil infrastructure. Recent investigations on the service life management of civil infrastructure were generally based on minimizing the life-cycle cost analysis and maximizing the structural performance. Various objectives for service life management have been developed using novel probabilistic concepts and methods over the last few decades. On the other hand, an increase in the number of objectives in a multi-objective optimization problem can lead to difficulties in computational efficiency, visualization, and decision making. These difficulties can be overcome using the objective reduction approach to identify the redundant and essential objectives. As a result, the efficiency in computational efforts, visualization, and decision making can be improved. In this paper, the multi-objective optimization using the objective reduction approach was applied to the service life management of concrete bridges. The results showed that four initial objectives can be reduced by two objectives for the optimal service life management.

• Korean Journal of Construction Engineering and Management
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• v.8 no.3
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• pp.76-86
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• 2007

Construction development work invents profit as those finalize object, and a make or break success of project depends on correct analysis and forecast business feasibility at project early. Business feasibility study would be decision-making under precarious situation because is connoting uncertainty that is future. estimate at present visual point essentially. Under uncertainty, a decision-making method is based on probability theory of statistics, but business feasibility study had applied with not feasibility study by probabilistic decision method but it by determinism derision method so far. Therefore in this study doing decision-making by a probability theory method for successful project at early business feasibility study, it present a probabilistic study method that use simulation that can supply a little more correct and reliable data to decision-maker As result, a probabilistic study method is more suitable than deterministic study method as technique for a financial feasibility study of construction development work. Making good use of this probabilistic study method at important business or careful decision-making, because efficient Judgment that is based accuracy and authoritativeness may become available.

• Spatial Information Research
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• v.15 no.4
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• pp.371-383
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• 2007

The Korea Planning Support Systems (KOPSS) represent Korean style national policy and planning support systems using the GIS-based spatial analysis methods. The KOPSS refers to a kind of the spatial decision support systems (SDSS) or the planning support systems (PSS) for stakeholders' spatial decision-making. The KOPSS uses the existing individual databases obtained from the Architecture Information Systems (AIS) and Land Management Information Systems (LMIS) that have been constructed by information projects since the mid 1990s. The purpose of this paper is to suggest the development strategies and establish the theoretical frameworks of the KOPSS by considering the comprehensive basic composites of the GIS-based SDSS or PSS. For this, it deals with the basic concepts, the development strategies, the base environment strategies, and the promotion strategies of the KOPSS.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.429-436
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• 2013

Context aware environment u-health service is to provide health service with recognition of a computer. The computer recognizes that a patient can contact real life in many context. Context aware environment service for recommend have to definition of context data and service recommendations related to factors shall be identified. In this paper, Context aware environment of u-health service will be provide context data related to identifies recommendations factors using multivariate analysis method and recommendations factors creation to decision tree, association rule based decision model. health service recommend for significantly context data can be distinguish through recommendation factors of identify. Also, context data of patient can know preference factors through preference decision model.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.13-20
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• 2007

Decision tree is one of the most useful analysis methods for various data mining functions, including prediction, classification, etc, from massive data. Decision tree grows by splitting nodes, during which the purity increases. It is needed to stop splitting nodes when the purity does not increase effectively or new leaves does not contain meaningful number of records. Pruning is done if a branch does not show certain level of performance. By pruning, the structure of decision tree is changed and it is implied that the previous splitting of the parent node was not effective. It is also implied that the splitting of the ancestor nodes were not effective and the choices of attributes and criteria in splitting them were not successful. It should be noticed that new attributes or criteria might be selected to split such nodes for better tries. In this paper, we suggest a procedure to modify decision tree by Fuzzy theory and splitting as an integrated approach.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.179-190
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• 2009

In the financial industry, the decision tree algorithm has been widely used for classification analysis. In this case one of the major difficulties is that there are so many explanatory variables to be considered for modeling. So we do need to find effective method for reducing the number of explanatory variables under condition that the modeling results are not affected seriously. In this research, we try to compare the various variable reducing methods and to find the best method based on the modeling accuracy for the tree algorithm. We applied the methods on the pension insurance of a insurance company for getting empirical results. As a result, we found that selecting variables by using the sensitivity analysis of neural network method is the most effective method for reducing the number of variables while keeping the accuracy.