• Journal of Digital Convergence
• /
• v.16 no.10
• /
• pp.11-26
• /
• 2018

A strategic technology selection for GHG reduction is crucial to secure mitigation means. Especially, a technology selection for a public sector is encouraged to consider integrated perspectives due to various stakeholders under public goals. However, previous studies have mainly focused on technological and economic factors, moreover, consistent criteria have not been applied. This study develops indicators for the GHG reduction technology selection from the public perspective based on delphi method with 22 experts. The result provides valid indicators of technology selection for GHG reduction considering an aspect of technology, economics, environment, policy, society. Specifically, 16 indicators from 5 categories on commercialized technology, and 18 indicators from 5 categories on new technology. We expect that those indicators are useful for a decision-making tool of technology selection. Moreover, provide the basis for the study of judgement criteria to evaluate GHG reduction technology.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.6
• /
• pp.646-650
• /
• 2014

In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• The Korean Journal of Applied Statistics
• /
• v.31 no.6
• /
• pp.733-749
• /
• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• 한국사회정책
• /
• v.20 no.3
• /
• pp.9-46
• /
• 2013

The degree of delegation related to the social security acts is involved in the candidate selection methods and the standing committee system. The social security acts with a small amount of delegation have an affinity with the bottom-up selection methods and the standing committee to guarantee long term in office. In Germany, the bottom-up selection method which guarantees the right of party members to nominate candidates and the standing committee to guarantee long term in office have an affinity with the Social Acts with less delegation. But the social security acts with a large number of delegation have an affinity with the top-down selection methods and the standing committee not to guarantee long term in office. In Korea, the top-down selection method in which the central headquarter of the party dominates the selection process, and the standing committee whose members are to be selected every two years have an affinity with the Social Security Acts with the excessive delegation.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.20 no.5
• /
• pp.331-340
• /
• 2016

It is important to select proper ground motions for obtaining accurate results from response history analyses. The purpose of this study is to propose an accurate and efficient method that does not require excessive computation for selecting and scaling ground motions to match target response spectrum mean and variance. The proposed method is conceptually simple and straightforward, and it does not use a simulation algorithm that requires a sophisticated subroutine program. In this method, the desired number of ground motions are sequentially scaled and selected from a ground motion library. The proposed method gives the best selection results using Sum of Square Error and has the smallest value(=0.14). Also, The accuracy and consistency of the proposed method are verified by comparing the selection results of the proposed method with those of existing methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.4
• /
• pp.317-322
• /
• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• International Journal of Internet, Broadcasting and Communication
• /
• v.9 no.1
• /
• pp.72-80
• /
• 2017

In this work, an efficient transmit antenna selection approach for the quadrature spatial modulation (QSM) systems is proposed. The conventional Euclidean distance antenna selection (EDAS)-based schemes in QSM have too high computational complexity for practical use. The proposed antenna selection algorithm is based on approximation of the EDAS decision metric employed for QSM. The elimination of imaginary parts in the decision metric enables decoupling of the approximated decision metric, which enormously reduces the complexity. The proposed method is also evaluated via simulations in terms of symbol error rate (SER) performance and compared with the conventional EDAS methods in QSM systems.

• Communications for Statistical Applications and Methods
• /
• v.22 no.2
• /
• pp.147-157
• /
• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• The KIPS Transactions:PartD
• /
• v.9D no.5
• /
• pp.887-896
• /
• 2002

Based on SPICE (Software Process Improvement and Capability dEtermination) evaluation model, SPIRE (Software Process Improvement in Regions of Europe) is developed and published as a process improvement model for small and medium size organizations. However, practical selection guidelines or mapping rules between business goals and software processes do not exist within SPIRE. This research aims to construct an objective reference mapping table between business goals and software processes, and to propose a process selection method using the mapping table. The mapping table is constructed by the convergence of domestic software process experts' opinions using Delphi techniques. In the suggested process selection method, target processes are selected using the intuition of project participants or project managers as well as the reference mapping table. The feasibility of the proposed selection method has been reviewed by applying to two small software companies. Using the reference mapping table, we could select key processes which were passed over by project managers.

• The Korean Journal of Applied Statistics
• /
• v.19 no.1
• /
• pp.149-161
• /
• 2006

The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.