• Proceedings of the IEEK Conference
• /
• 2004.06b
• /
• pp.459-462
• /
• 2004

In this paper, we present a reducing power consumption of a scheduling for module selection under the time constraint. A a reducing power consumption of a scheduling for module selection under the time constraint execute scheduling and allocation for considering the switching activity. The focus scheduling of this phase adopt Force-Directed Scheduling for low power to existed Force-Directed Scheduling. and it constructs the module selection RT library by in account consideration the mutual correlation of parameters in which the power and the area and delay. when it is, in this paper we formulate the module selection method as a multi-objective optimization and propose a branch and bound approach to explore the large design space of module selection. Therefore, the optimal module selection method proposed to consider power, area, delay parameter at the same time. The comparison experiment analyzed a point of difference between the existed FDS algorithm and a new FDS_RPC algorithm.

• The Korean Journal of Applied Statistics
• /
• v.29 no.4
• /
• pp.643-656
• /
• 2016

High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.103-108
• /
• 2023

We study the problem of selecting a subset of sensor nodes in sensor networks in order to maximize the performance of parameter estimation. To achieve a low-complexity sensor selection algorithm, we propose a greedy iterative algorithm that allows us to select one sensor node at a time so as to maximize the log-determinant of the inverse of the estimation error covariance matrix without resort to direct minimization of the estimation error. We apply QR factorization to the observation matrix in the log-determinant to derive an analytic selection rule which enables a fast selection of the next node at each iteration. We conduct the extensive experiments to show that the proposed algorithm offers a competitive performance in terms of estimation performance and complexity as compared with previous sensor selection techniques and provides a practical solution to the selection problem for various network applications.

• Journal of Korean Society for Quality Management
• /
• v.19 no.1
• /
• pp.51-64
• /
• 1991

This paper deals with a robust subset selection procedure based on Hodges-Lehmann estimators of location parameters. An improved formula for the estimated standard error of Hodges-Lehmann estimators is considered. Also, the degrees of freedom of the studentized Hodges-Lehmann estimators are investigated and it is suggested to use 0.8n instead of n-1. The proposed procedure is compared with the other subset selection procedures and it is shown to have good effciency for heavy-tailed distributions.

• Journal of Electrical Engineering and Technology
• /
• v.4 no.3
• /
• pp.360-364
• /
• 2009

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase induction machine using genetic algorithm. The parameter estimation procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The propose estimation algorithm is of non-linear kind based on selection in genetic algorithm. The machine parameters are obtained as the solution of a minimization of objective function by genetic algorithm. Simulation shows good performance of the propose procedures.

• Communications for Statistical Applications and Methods
• /
• v.5 no.2
• /
• pp.277-285
• /
• 1998

The smoothing parameter $\lambda$ in smoothing spline regression is usually selected by minimizing cross-validation (CV) or generalized cross-validation (GCV). But, simple CV or GCV is poor candidate for estimating prediction error. We defined MGCV (Multifold Generalized Cross-validation) as a criterion for selecting smoothing parameter in smoothing spline regression. This is a version of cross-validation using $leave-\kappa-out$ method. Some numerical results comparing MGCV and GCV are done.

• Proceedings of the KIPE Conference
• /
• 1999.07a
• /
• pp.58-61
• /
• 1999

This paper describes the program of optimally choosing parameter in designing inductor, which applied by fuzzy theory, and verifies the reliability of program to use in design of power supply of electronic machine and information communication. It is available to find optimal value of complex and various parameter, such as core, winding, winding number, and air-gap, etc., needed on designing inductor. We expects to minimize time and cost of inductor design.

• Communications for Statistical Applications and Methods
• /
• v.12 no.2
• /
• pp.413-423
• /
• 2005

Consider the problem of estimating the underlying regression function from a set of noisy data which is contaminated by a long tailed error distribution. There exist several robust smoothing techniques and these are turned out to be very useful to reduce the influence of outlying observations. However, no matter what kind of robust smoother we use, we should choose the smoothing parameter and relatively less attention has been made for the robust bandwidth selection method. In this paper, we adopt the idea of robust location parameter estimation technique and propose the robust cross validation score functions.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.672_673
• /
• 2009

In this paper seeks the parameter which relates with the efficiency from premium efficiency level single-phase induction motor. Also it compares with the parameters and it analyzes and an optimum parameter it seeks by FEM. Consquently, a optimal design is accomplished from the this paper. Also parameters compare efficiency. And it analyzes and studies about optimum parameter by FEM. The sample single-phase induction motor selection selected existing premium level motor. We analyze each parameter using 2-D finite element analysis (FEM). According to Study of losses and Design flow, losses and efficiency can be explain by many parameter. So this paper present optimal parameters. Finally, this paper presents the method which raises the efficiency of premium efficiency level single-phase induction motor.

• Journal of Animal Science and Technology
• /
• v.44 no.6
• /
• pp.669-676
• /
• 2002

The efficiency of marker assisted selection(MAS) over conventional selection index based sorely on phenotypic records was studied by deterministic simulation model. Parameter combination of heritability and amount of genetic variation due to the markers included in the index was employed. For the index with own phenotypic information vs. the index with own phenotypic plus marker information, the relative efficiency of MAS over the selection with phenotypic records was about 38% high when heritability was low(0.05). However, when heritability was high(50%), the relative efficiency of MAS was vary low and almost negligible. For more practical situation of selection index which included information on own, sire and dam, MAS was less effective than when selection criteria was only on own performance.