• Communications for Statistical Applications and Methods
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• 제21권3호
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• pp.213-224
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• 2014

In this paper, we study the problem of transforming to normality. We propose to estimate the transformation parameter by minimizing a weighted squared distance between the empirical characteristic function of transformed data and the characteristic function of the normal distribution. Our approach also allows for other symmetric target characteristic functions. Asymptotics are established for a random sample selected from an unknown distribution. The proofs show that the weight function $t^{-2}$ needs to be modified to have thinner tails. We also propose the method to compute the influence function for M-equation taking the form of U-statistics. The influence function calculations and a small Monte Carlo simulation show that our estimates are less sensitive to a few outliers than the maximum likelihood estimates.

• Structural Engineering and Mechanics
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• 제57권2호
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• pp.281-294
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• 2016

The paper is devoted to study a mesh-free analysis method of structural elements of engineering structures based on B-spline Wavelet Basis Function. First, by employing the moving-least square method and the weighted residual method to solve the structural displacement field, the control equations and the stiffness equations are obtained. And then constructs the displacement field of the structure by using the m-order B-spline wavelet basis function as a weight function. In the end, the paper selects the plane beam structure and the structure with opening hole to carry out numerical analysis of deformation and stress. The Finite Element Method calculation results are compared with the results of the method proposed, and the calculation results of the relative error norm is compared with Gauss weight function as weight function. Therefore, the clarification verified the validity and accuracy of the proposed method.

• Journal of Electrical Engineering and Technology
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• 제7권4호
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• 인터넷정보학회논문지
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• 제10권4호
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• pp.115-126
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• 2009

소프트웨어 규모 추정은 소프트웨어 Life-Cycle 초기에 분석되어 규모와 비용의 예측에 도움을 주어야 한다. 2004년 소프트웨어 사업대가 기준에 국제표준에 기반한 기능점수 방식이 도입된 후 사용자 입장에서 소프트웨어의 규모를 바라보고 비용을 산정하는 기반이 마련되었다. 그러나 현재의 기능 점수 측정 방식은 익숙하지 않은 일반 사용자가 접근하기 쉽지 않고, 모든 시스템 및 기능의 복잡도 가중치가 획일화되어 있어 내부 계산 로직이 복잡한 공학용 소프트웨어나 과학계산용, 시뮬레이션 소프트웨어에 대한 산정 방식에서 그 규모를 적절히 산정하지 못하는 문제점을 안고 있다. 본 논문에서는 기존의 기능점수 측정 절차를 간략화하고 프로젝트 초기에 규모의 추정을 쉽고 빠르게 수행할 수 있는 모델을 제시한다. 또한 특정 조직의 특성을 반영할 수 있는 수학적 가중치 산출 모형을 제시함으로써 고정된 복잡도 가중치에 대한 논란의 여지를 없애고 조직의 데이터가 쌓일수록 해당 조직의 특성을 반영해 나갈 수 있는 수학적 가중치 산출 모형을 제시한다. 제시한 모델은 평가 결과 기존의 FPA(Function Point Analysis) 방식보다 빠르게 규모를 측정할 수 있고 LOC(Line of Code)와의 상관관계도 더 높은 장점이 있다.

• 농촌계획
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• 제20권4호
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• pp.13-24
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• 2014

The pond wetlands in agricultural landscape are important natural resources that carry out the function of bio-diversity conservation. However, recently, those have been gradually embedded as their utility value were disappeared. And, the assessment methods used for pond wetlands are insufficient. Therefore, this study was conducted to examine the conservation value of pond wetlands by using the modified RAM, and present the improvement of assesment methods. The study sites, a total of 32 pond of 4 types by land use, were selected on the basis of Ramsar Convention. Through the analysis of precedent studies, the weighted 8 functions were adjusted. According to the assessment results, pond wetlands made the largest contribution to Fishery and Herpetile Habitat function. In addition, it also made large contribution to Floral Diversity, Wildlife Habitat, and Water Quality Protection function. On the other hand, it made a small contribution to Aesthetics and Recreation, Runoff Attenuation, Shoreline /Stream Bank Protection, and Flood/Storm Water Storage function due to the characteristics of small-scale pond wetlands. In the assessment of 8 functions, house type showed the worst assessment result, and mountain type showed the best assessment result. It is thought that those are due to land use type in terms of vicinity. 10 items among 52 of the modified RAM showed the same assessment results in all land use types. Accordingly, it is required to be deleted and modified the assessment method. On the other hand, it is required to add age, interference, and water use to the assessment method. It is thought that these results can be utilized for the development and modification of assessment methods focused on pond wetlands in rural area.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.481-490
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• 2013

Classification is an important research area as data can be easily obtained even if the number of predictors becomes huge. The support vector machine(SVM) is widely used to classify a subject into a predetermined group because it gives sound theoretical background and better performance than other methods in many applications. The SVM can be viewed as a penalized method with the hinge loss function and penalty functions. Instead of $L_2$ penalty function Fan and Li (2001) proposed the smoothly clipped absolute deviation(SCAD) satisfying good statistical properties. Despite the ability of SVMs, they have drawbacks of non-robustness when there are outliers in the data. We develop a robust SVM method using a weight function with the SCAD penalty function based on the local quadratic approximation. We compare the performance of the proposed SVM with the SVM using the $L_1$ and $L_2$ penalty functions.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.187-194
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• 1999

Case-Based Reasoning(CBR) systems support ill structured decision-making. The measure of the success of a CBR system depends on its ability to retrieve the most relevant previous cases in support of the solution of a new case. One of the methodologies widely used in existing CBR systems to retrieve previous cases is that of the Nearest Neighbor(NN) matching function. The NN matching function is based on assumptions of the independence of attributes in previous case and the availability of rules and procedures for matching.(omitted)

• Kyungpook Mathematical Journal
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• 제46권3호
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• pp.337-343
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• 2006

In the present paper we shall prove that on a foundation *-semigroup S with an identity and with a locally bounded Borel measurable weight function ${\omega}$, the pointwise convergence and the uniform convergence of a sequence of ${\omega}$-bounded exponentially convex functions on S which are also continuous at the identity are equivalent.

• 전기학회논문지
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• 제56권1호
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• pp.197-205
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• 2007

We have proposed a novel line detection method based on the estimated probability density function of gradient directions of edges. By estimating peaks of the density function, we determine groups of edges that have the same gradient direction. For edges in the same groups, we detect lines that correspond to peaks of the connectivity weighted distribution of the distances from the origin. In the experiments using the Data Matrix barcode images and LCD images, the proposed method showed better performance than conventional Methods in terms of the processing speed and accuracy.

• 기술사
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• 제22권2호
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• pp.19-25
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• 1989

Utilizing a cosine transform in image compression has several recognized performance benefits, resulting in the ability to attain large compression ratio with small quality loss. Also, various models incorporating Human Visual System (HVS) to Discrete Cosine Trans-form (DCT) scheme are considered. Using the exact frequency components of DCT basis function, the optimum modulation transfer function (MTF) is obtained analytically. The errors at a block boundary which is important factor in transform coder are criteria for error measurement. The HVS weight coding results in perceptually higher quality images compared with the unweighted scheme.