• 한국진공학회:학술대회논문집
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• 한국진공학회 2016년도 제50회 동계 정기학술대회 초록집
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• pp.332.1-332.1
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• 2016

Application of magnetic fields is important to characterize the carrier dynamics in semiconductor quantum structures. We performed photoluminescence (PL) measurements from an InGaP-AlInGaP single quantum well under pulsed magnetic fields to 50 T. The zero field interband PL transition energy matches well with the self-consistent Poisson-Schr?dinger equation. We attempted to analyze the dimensionality of the quantum well by using the diamagnetic shift of the magnetoexciton. The real quantum well has finite thickness that causes the quasi-two-dimensional behavior of the exciton diamagnetic shift. The PL intensity diminishes with increasing magnetic field because of the exciton motion in the presence of magnetic field.

• 디지털콘텐츠학회 논문지
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• 제2권2호
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• pp.157-166
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• 2001

본 논문은 내용 기반 이미지 검색 시스템에서 이미지의 위치 및 모양 정보에 의한 회귀선을 추정하여 효율적으로 특징 벡터 추출함과 동시에 같은 도메인상의 특징 벡터가 일정 수준보다 많아질 경우 효율적으로 특징 벡터의 차원을 줄이는 기법을 제안한다. 특히, 특징 벡터의 차원을 줄이는 제안된 기법은 특징 벡터의 수에 관계없이 특정한 n개의 특징 벡터로의 변환이 가능하다. 본 논문에서 제안된 기법들은 실제 내용 기반 이미지 검색 시스템의 구현을 통해 기존의 방법보다 효율적인 검색은 물론 다차원 특징 벡터를 특정 n차원의 특징 벡터로 변환함으로써 다차원 색인 기법이 가지고 있는 가장 큰 단점인 '차원의 저주' 문제를 근본적으로 해결할 수 있는 방법임을 보인다.

• International Journal of Contents
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• 제10권1호
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• pp.29-35
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• 2014

This paper proposes an approach for visualizing research trends using theme maps and extra information. The proposed algorithm includes the following steps. First, text mining is used to construct a vector space of keywords. Second, correspondence analysis is employed to reduce high-dimensionality and to express relationships between documents and keywords. Third, kernel density estimation is applied in order to generate three-dimensional data that can show the concentration of the set of documents. Fourth, a cartographical concept is adapted for visualizing research trends. Finally, relative vitalization information is provided for more accurate research trend analysis. The algorithm of the proposed approach is tested using papers about Traditional Korean Medicine.

• 한국전산유체공학회지
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• 제10권1호
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• pp.45-50
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• 2005

A fundamental understanding of the flow around the wind turbine is important to investigate the performance of new type of wind turbine. This study presents the simulation of three dimensional flow fields around the Darius wind turbine as an example. Incompressible Navier-Stokes equations are used for this simulation. The rotating coordinate system that rotates in the same speed of the turbine is used in order to simplify the boundary condition on the blades. Additionally, the boundary fitted coordinate system is employed in order to express the shape of the blades precisely. Fractional step method is used to solve the basic equations. Third order upwind scheme is chosen for the approximation of the non-linear terms since it can compute the flow field stably even at high Reynolds number without any turbulence models. The flow fields obtained in this study are highly complex due to the three dimensionality and are visualized effectively by using the technique of the computer graphics.

• 한국IT서비스학회지
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• 제10권2호
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.109.3-109
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• 2001

The so-called curse of dimensionality arises when Gaussian mixture is used on high-dimensional small-sample-size data, since the number of free elements that needs to be specied in each covariance matrix of Gaussian mixture increases exponentially with the number of dimension d. In this paper, by constraining the covariance matrix in its decomposed orthonormal form we get a local PCA model so as to reduce the number of free elements needed to be specified. Moreover, to cope with the small sample size problem, we adopt BYY data smoothing learning which is a regularization over maximum likelihood learning obtained from BYY harmony learning to implement this local PCA model.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.79-84
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• 2003

Likelihood estimation in random-effect models is often complicated because the marginal likelihood involves an analytically intractable integral. Numerical integration such as Gauss-Hermite quadrature is an option, but is generally not recommended when the dimensionality of the integral is high. An alternative is the use of hierarchical likelihood, which avoids such burdensome numerical integration. These two approaches for fitting binary data are compared and the advantages of using the hierarchical likelihood are discussed. Random-effect models for binary outcomes and for bivariate binary-continuous outcomes are considered.

• 대한산업공학회지
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• 제40권3호
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• pp.275-282
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• 2014

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.689-690
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• 2006

Relational discriminant analysis is a way of representing an object based on the dissimilarity measures among the prototypes extracted from feature vectors instead of the vectors themselves. Thus, by appropriately selecting a few number of representatives and by defining the dissimilarity measure, in this paper we propose a method of reducing the dimensionality and getting to achieve a better classification performance in both speed and accuracy. Our experimental results demonstrate that the proposed mechanism increases the performance as compared with the conventional approaches for samples involving artificial data sets.