• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.571-573
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• 1995

This paper presents a method for optimal dispatch which minimizes transmission losses and improves voltage profile of power systems using genetic algorithm based on the mechanism of natural genetics and natural selection. The constraints are VAR sources(transformer tap, generator voltage magnitude and shunt capacitor/reactor), load bus voltages and generator reactive power. Real variable-based genetic algorithms which can save coding times and maintain the accuracy are applied for optimal dispatch of reactive power. The genes of genetic algorithm consisted of integers for considering discrete VAR sources. A efficient operator for crossover is proposed to consider the effect of close genes. The algorithm proposed can apply to problems for large scale power systems with multi-variables and complex nonlinear functions efficiently. The proposed method is applied to IEEE 30 buses model system to show its effectiveness.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1025-1036
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• 2003

Variable selection algorithm for principal component analysis using penalty function is proposed. We use the fact that usual principal component problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Simulated annealing algorithm is used in searching for optimal solutions with penalty functions. Comparisons between several well-known penalty functions through simulation reveals that the HARD penalty function should be suggested as the best one in several aspects. Illustrations with real and simulated examples are provided.

• ETRI Journal
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• 제16권3호
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• pp.27-41
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• 1994

This paper provides a performance study of multistage interconnection networks in packet switching environment. In comparison to earlier work, the model is more extensive - it includes several parameters such as multiple-packet messages, variable buffer size, and wait delay at a source. The model is also uniformly applied to several representative networks and thus provides a basis for fair comparison as well as selection of optimal values for parameters. The complexity of the model required use of simulation. However, a partial analytical model is provided to measure the congestion in a network.

• 한국멀티미디어학회논문지
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• 제16권4호
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• pp.502-509
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• 2013

정보화시대에 대학에서의 교양 컴퓨터교육과정은 컴퓨터에 대한 소양을 쌓고 정보화 사회에 능동적으로 대처할 수 있는 능력을 배양하여 생산성 향상은 물론 국가 간의 경쟁력에서 뒤지지 않게 하는데 목표를 두고 있다. 본 논문에서는 대학생을 대상으로 컴퓨터교육 만족도에 영향을 미치는 결정적인 변인의 발견 및 만족도를 분석한다. 전처리과정으로 자바 기반의 기계 학습 도구인 상관에의한 특성선택을 사용하여 최적의 변인을 선택한다. 그리고 퍼지의사결정법에 기반하여 각 변인의 가중치를 사용하여 최적의 변인을 생성하였다. 본 논문의 연구결과는 컴퓨터교육 만족도 자료의 분석에서 퍼지의사결정법을 제안하고, 재현율과 정밀도 분석에 의해 만족도 평가에 대한 정확성을 확인하였다.

• 한국정보처리학회논문지
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• 제6권3호
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• pp.781-791
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• 1999

DD(Decision Diagrams) is an efficient operational data structure for an optimal expression of boolean functions. In a graph-based synthesis using DD, the goal of optimization decreases representation space for boolean functions. This paper represents boolean functions using OPKFDD(Ordered Pseudo-Kronecker Functional Decision Diagrams) for a graph-based synthesis and is based on the number of nodes as the criterion of DD size. For a property of OPKFDD that is able to select one of different decomposition types for each node, OPKFDD is variable in its size by the decomposition types selection of each node and input variable order. This paper proposes a method for generating OPKFDD efficiently from the current BDD(Binary Decision Diagram) Data structure and an algorithm for minimizing one. In the multiple output functions, the relations of each function affect the number of nodes of OPKFDD. Therefore this paper proposes a method to decide the input variable order considering the above cases. Experimental results of comparing with the current representation methods and the reordering methods for deciding input variable order are shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권3호
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• pp.309-330
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• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• 대한전기학회논문지:전력기술부문A
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• 제52권8호
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• pp.450-456
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• 2003

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).

• Steel and Composite Structures
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• 제45권1호
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• pp.39-49
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• 2022

This research investigates a rotary disk with variable cross-section and incompressible hyperelastic material with functionally graded properties in large hyperelastic deformations. For this purpose, a power relation has been used to express the changes in cross-section and properties of hyperelastic material. So that (m) represents the changes in cross-section and (n) represents the manner of changes in material properties. The constants used for hyperelastic material have been obtained from experimental data. The obtained equations have been solved for different m, n, and (angular velocity) values, and the values of radial stresses, tangential stresses, and elongation have been compared. The results show that m and n have a significant impact on disk behavior, so the expected behavior of the disk can be obtained by an optimal selection of these two parameters.

• 기술경영경제학회:학술대회논문집
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• 기술경영경제학회 2003년도 제22회 동계학술발표회 논문집
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• pp.191-199
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• 2003

It is critical to select an appropriate technology valuation method when the characteristics of a technology and valuation environment are variable. To ensure high quality decision making when selecting a technology valuation method, it is necessary to understand the principles of a good technology valuation method, and define and apply a decision making theory for selecting an optimal method. The authors propose that Axiomatic Design Principles can be applied as a decision making theory. In order to apply Axiomatic Design for this problem, this paper describes four domains(customer, functional, physical, and process domain) and four axioms(independence, information, cost, time axiom) for the decision making process for the optimal technology valuation method. The result of this study will contribute flexibility to the dynamic technology valuation process.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.29-36
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• 2009

We present two local-search based metaheuristics for the multi-source capacitated facility location problem. In such a problem, each customer's demand can be supplied by one or more facilities. The problem is NP-hard and the number of locations in the optimal solution is unknown. To keep the search process effective, the proposed methods adopt the following features: (1) a multi-exchange neighborhood structure, (2) a tabu list that keeps track of recently visited solutions, and (3) a multi-start to enhance the diversified search paths. The transportation simplex method is applied in an efficient manner to obtain the optimal solutions to neighbors of the current solution under the algorithm framework. Two in-and-out selection rules are also proposed in the algorithms with the purpose of finding promising solutions in a short computational time. Our computational results for some of the benchmark instances, as well as some instances generated using a method in the literature, have demonstrated the effectiveness of this approach.