• Journal of the Chungcheong Mathematical Society
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• v.27 no.1
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• pp.145-149
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• 2014

I consider the optimal consumption and portfolio selection problem with nonnegative wealth constraints using the dynamic programming approach. I use the constant relative risk aversion (CRRA) utility function and disutility to derive the closed-form solutions.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.15 no.5
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• pp.31-48
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• 2012

The purpose of this study is to select indicators by a methodical approach and to establish a functional assessment system as a basic study for planning and constructing green space of forest. The types of green space were divided into 6 classes based on theoretical reviews of literature and the functions of green space were restricted to 'nature-ecological function', 'environment-control function' and 'usage function'. As a result of the selection of indicators, 35 indicators were initially selected by theoretical review and these indicators were reduced to 29 through brainstorming. Also, these indicators were classified into three functions such as 12 indicators (nature-ecological function), 8 indicators (environment-control function), 6 indicators (usage function) by analysis of suitability. According to the result of selection of the optimum indicators using MCB (Multiple Comparisons with the Best treatment) analysis, the optimum indicators of 7, 5, and 4 respectively by each function were selected for forest green space. The results of AHP (Analytic Hierarchy Process) for the establishment of the assessment system in forest, the weight of nature-ecological function was evaluated highest at 0.558, while the weight of environment-control and usage function were calculated at each 0.277, 0.165. 'Naturality (0.189)', 'Carbon sink (0.235)', and 'Accessibility (0.354)' among indicators showed highest by each function. The weight of indicator and assessment system may be used as a valuable guideline in case of assessing synthetically green space within urban planning.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.247-253
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• 2017

Feature selection or variable selection is a data mining scheme for selecting highly relevant features with target concept from high dimensional data. It decreases dimensionality of data, and makes it easy to analyze clusters or classification. A feature selection scheme requires an evaluation function. Most of current evaluation functions are based on statistics or information theory, and they can evaluate only for single feature (one-dimensional data). However, features have interactions between them, and require evaluation function for multi-dimensional data for efficient feature selection. In this study, we propose modification of FSDD evaluation function for utilizing evaluation of multiple features using extended distance function. Original FSDD is just possible for single feature evaluation. Proposed approach may be expected to be applied on other single feature evaluation method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.122-133
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• 2014

This paper deals with the rapid charger which is the mid-type between the slow and fast chargers in the aspect of charging time. In its functions, it can perform the Active Power Filter(APF) function without changing the topology besides the charging function. In addition, to perform the charging and APF function, this paper proposes the mode selection algorithm. The operation of the charger that has APF function and the mode selection algorithm are verified by the simulation and experiment.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.909-916
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• 2010

A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

• Korean Journal of Childcare and Education
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• v.20 no.2
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• pp.145-167
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• 2024

Objective: This study aimed to investigate the moderated-moderated mediating effects of children's media content selection and parental restrictive media mediation on the relationship between children's screen media time and executive function difficulties. Methods: A total of 693 parents of children aged 5~6 years participated in this study and were asked to answer all survey questions. The data were analyzed by descriptive statistics and correlation analysis using SPSS 27.0. Model 11 of PROCESS macro 4.3 was used to examine the moderated-moderated mediation model. Children's gender, age, childcare enrollment status, and household income were included in the analyses as covariates. Results: The moderated-moderated mediating effects of children's media content selection and parental restrictive media mediation were found to be significant. Specifically, bedtime mediated the relationship between screen media time and executive function difficulties only when parents did not appropriately implement restrictive mediation and children freely selected media content. Conclusion/Implications: It is recommended that parents understand the importance of implementing restrictive media mediation and selecting appropriate media contents for their child to prevent executive function difficulties in early childhood. Also, child education or day-care centers should offer education program about appropriate media use to reach more parents.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1191-1197
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• 2009

Most Bayesian approaches to model selection of wavelet analysis have drawbacks that computational cost is expensive to obtain accuracy for the fitted unknown function. To overcome the drawback, this article introduces loss functions which are criteria for level dependent threshold selection in wavelet based Bayesian methods with arbitrary size and regular design points. We demonstrate the utility of these criteria by four test functions and real data.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.397-405
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• 1998

We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.