• Journal of the Korean Statistical Society
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• 제17권1호
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• pp.1-8
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• 1988

Kernel estimates of an unknown regression function are studied. Bandwidth selection rule minimizing integrated absolute error loss function is considered. Under some reasonable assumptions, it is shown that the optimal bandwidth is unique and can be computed by using bisection algorithm. Adaptive bandwidth selection rule is proposed.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• KNOM Review
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• 제21권2호
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• pp.35-45
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• 2018

네트워크 슬라이싱 기능이 모바일 네트워킹에 적용되면서 네트워크 슬라이스를 선택할 수 있는 메커니즘이 필수적이다. 5G 아키텍처에 대한 3GPP 표준 기술 사양에 따라 슬라이스 선택 프로세스를 활용하기 위해 Network Slice Selection Function (NSSF)가 포함되어 있다. 이 네트워크 기능의 실제 구현은 네트워크 인스턴스의 동적 변경 사항을 처리해야하므로 가상 네트워크 기능 (VNF)의 오케스트레이션을 지원하는 플랫폼이 필요하다. 제안 된 솔루션은 Central Office Rearchitected as a Data Center (CORD) 플랫폼에서 모바일 네트워크용으로 특화된 M-CORD를 사용하고 있다. 이는 서비스 오케스트레이터인 XoS를 통합하는 플랫폼 및 Software Defined Networking (SDN), Network Function Virtualization (NFV) 및 클라우드를 관리하는 OpenStack에 기반하고 있다. 이 플랫폼을 통해, 본 논문에서 제시된 NSSF 구현은 백엔드 서비스와 네트워크 기능 인스턴스 간의 동기화를 통해서 동적으로 슬라이스 정보를 얻을 수 있는 적절한 생태계를 제공하고 있다.

• Journal of the Korean Data and Information Science Society
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• 제22권3호
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.710-712
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• 2004

Generation companies(Genco) submit the supply functions as a bidding function to a bid market in a competitive electricity market. The profits of Gencos vary in accordance with the bid functions, so the selection of a bidding function plays a key role in increasing their profits. This paper presents an analysis of the selection of the supply function from the viewpoint of Nash equilibrium(NE). Four types of bidding function parameters are used for analizing the electricity market. The competition of selecting bidding parameters is modeled as subgame and overall game in this research. The NEs in both game are computed by using analytic method and payoff matrix method. It is verified in case studies for the NE of overall game to satisfy the equilibrium condition.

• 전자공학회논문지A
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• 제32A권6호
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• pp.104-110
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• 1995

In this paper, an effective method for the minimization of two-level Reed-muller expressions by cube selection whcih considers functional complexity is presented. In contrast to the previous methods which use Xlinking operations to join two cubes for minimizatio, the cube selection method tries to select cubes one at a time until they cover the ON-set of the given function. This method works for most benchmark circuits, but for parity-type functions it shows power performance. To solve this problem, a cost function which computes the functional complexity instead of only the size of ON-set of the function is used. Therefore the optimization is performed considering how the trun minterms are grouped together so that they can be realized by only a small number of cubes. In other words, it considers how the function is changed and how the change affects the next optimization step. Experimental results shows better performance in many cases including parity-type functions compared to pervious results.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.547-548
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• 2021

본 논문에서는 SR DEVS 모델에서 자식 객체에 대한 부모 객체의 내부 상속을 고려한다. 내부 상속 함수는 상속 부모 객체가 지정될 경우 함수의 상속 처리를 수행한다. 내부 상속의 경우 부모 객체의 자산 특성에 따라 순수 상속과 부분 분열 상속으로 구분된다. 또한 상속에서 여러 자산에 대한 상속 선택이 발생할 경우 함수 처리를 요구한다. 내부 상속에 대해 선택 함수의 결정 방식을 통하여 자식 객체는 부모 객체로부터 자산을 넘겨받는다.

• 한국수자원학회논문집
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• 제39권6호
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• pp.495-502
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• 2006

비매개변수적 빈도해석을 위해 제시되는 핵밀도함수 방법에서 내삽법은 외삽법보다 더 신뢰적이기 때문에 내삽법과 관련된 광역폭의 선택이 외삽 문제와 연관되는 핵함수의 선택보다 중요하다. 그러나, 재현기간이 자료구간보다 커지거나 또는 $200{\sim}500$년 빈도 발생과 같은 확률 값에 대한 추정을 하는 경우는 자료의 외삽이 중요한 문제이며 따라서 이에 따른 핵함수의 선택도 중요시된다. 핵함수에 따라서는 외삽에 대해 상대적으로 작거나 큰 값이 제시 될 수 있으므로 극치값 추정에는 어려운 점이 있다. 따라서 본 논문에서는 일반적으로 내삽 및 외삽에도 적합한 핵함수로 Modified Cauchy 핵함수를 제시하였다.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.951-959
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• 2011

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.