• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.981-985
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• 1987

In this paper, we predicted the quantities of ass output messages with the generalized estimation equation based on regression model. And, to know the generalization of equation, we measured the deviation of errors between the observed and the estimated values. As a result, the proposed equation applied to sample data showed linear characteristics in some cases.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.797-799
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• 1999

In this paper, we design the Multi-FNN(Fuzzy-Neural Networks) using HCM Method. The proposed Multi-FNN uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. Also, We use HCM(Hard C-Means) method of clustering technique for improvement of output performance from pre-processing of input data. The parameters such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. We use the training and testing data set to obtain a balance between the approximation and the generalization of our model. Several numerical examples are used to evaluate the performance of the our model. From the results, we can obtain higher accuracy and feasibility than any other works presented previously.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2004년도 춘계학술대회 논문집
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• pp.136-140
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• 2004

A power-plant simulation tool has been developed for training the plant operators and testing a plant control system. The simulation tool is composed of a graphic editor, a component model builder and a system simulation solver. Such new programing techniques as object-oriented modeling and GUI(Graphical User Interface) are employed in developing the simulation tool. The graphic editor is based on the OpenGL library for effective implementation of GUI while the component model builder is based on object-oriented programming for efficient generalization of component models. The developed tool has been verified through the simulation of a real power plant.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2010년도 춘계학술대회 논문집
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• pp.74-75
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• 2010

Inflow broadband noise is generated when turbulence in the rotor wakes impinges on the downstream stator vanes. In this paper a three-dimensional model is developed to investigate the broadband noise due to turbulence-cascade interaction. In the newly-developed model, we consider the effects of incident turbulent gust component in span-wise direction on the inflow broadband noise. The quasi-three-dimensional theory is deduced based on the tonal analytic theory of Smith (1972) and two-dimensional broadband noise generalization by Cheong et al. (2006; 2009). Extending the modified LINSUB code, quasi-three-dimensional computational results are presented. Finally, we compare these computational results with time-domain results to validate the theory.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (A)
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• pp.4-6
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• 2006

Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metaboiic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.455-466
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• 2013

In nested case-control studies, the most common way to make inference under a proportional hazards model is the conditional logistic approach of Thomas (1977). Inclusion probability methods are more efficient than the conditional logistic approach of Thomas; however, the epidemiology research community has not accepted the methods as a replacement of the Thomas' method. This paper promotes the inverse probability weighting method originally proposed by Samuelsen (1997) in combination with an approximate jackknife standard error that can be easily computed using existing software. Simulation studies demonstrate that this approach yields valid type 1 errors and greater powers than the conditional logistic approach in nested case-control designs across various sample sizes and magnitudes of the hazard ratios. A generalization of the method is also made to incorporate additional matching and the stratified Cox model. The proposed method is illustrated with data from a cohort of children with Wilm's tumor to study the association between histological signatures and relapses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• 한국경영과학회지
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• 제36권3호
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• pp.27-43
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• 2011

This paper investigates properties of the newsvendor problem under a schedule involving progressive multiple discounts compared with the standard newsvendor problem under a no-discounts schedule. Unlike most conventional approaches using the criticial fractile to analyze the retailer and/or supplier behavior(s) in the newsvendor problem, our approach uses riskless profit. From the properties revealed through a series of computational experiments, two conjectures regarding the relationship between the expected profits of both newsvendor problems as a generalization over Khouja's argument (1995) are raised. Those conjectures encourage newsvendors who may face budget or warehouse capacity restriction to use the extended model under a multiple-discounts schedule rather than the standard model with no-discounts schedule because they apply for every order quantity as well as the optimal order quantity. In addition to the conjectures, some insightful results are found to justify the implementation of a multiple-discounts schedule from the computational experiments and a new interpretation for implementation of a multiple-discounts schedule that has not been addressed in Khouja is provided.

• Journal of Astronomy and Space Sciences
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• 제9권1호
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• pp.11-29
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• 1992

In 1963, Toomre built up classes of mass models for the highly flattened galaxies which have free parameters n, $a_n$ and $C_n$. In order to keep the universal dimension, we adopt parameters $b_n({C_n}^2={a_n}^{2n}+^2{b_n}^2/(n-1)!)$ insteal of $C_n$. Series of the normalized Toomre's mass models (G = $V_{max}$ =$R_{max}$ = 1, n = 1 to 7) are derived and the normalized parameters $a_n$ and $b_n$ are determined by the iteration method. Replacing parameters $a_n$ and $b_n$ to ${a_n}^l(=a_nr_{max})$ and ${b_n}^l(=b_n\cdotV_{max}/r_{max})$, we can get the generalization of Toomre's mass model.