• Journal of Elementary Mathematics Education in Korea
• /
• v.23 no.1
• /
• pp.75-92
• /
• 2019

The 2015 revised mathematics curriculum encourages students to use graphs in newspapers and the Internet as materials when teaching graphs, and to experience a series of statistical problem-solving processes that collect, classify, organize, graph and interpret data. The graphs that the students learn through traditional textbooks were a single type of graphs. In particular, the graphs of the 5th and 6th grade groups were only increased in numbers, but the basic concepts were repeated in the 3rd and 4th grades. Fortunately, from the 2009 revision curriculum, it is possible to select the graph suitable for the situation while comparing the characteristics of some graphs. In most cases, the graphs used in the real world are presented in the form of a compounded infographics. The purpose of this study is to analyze and analyze the manifestations of information processing competence elements emphasized in the 2015 revised curriculum through the statistical project class using the informal graphic in the fifth grade of elementary school. And we suggested a concrete class plan.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.191-207
• /
• 2024

Many studies have been conducted to quickly detect out-of-control situations in autocorrelated processes. The most traditionally used method is a residual control chart, which uses residuals calculated from a fitted time series model. However, many procedures for monitoring autocorrelated processes using statistical learning methods have recently been proposed. In this paper, we propose a monitoring procedure using the latent vector of LSTM Autoencoder, a deep learning-based unsupervised learning method. We compare the performance of this procedure with the LSTM Autoencoder procedure based on the reconstruction error, the RNN classification procedure, and the residual charting procedure through simulation studies. Simulation results show that the performance of the proposed procedure and the RNN classification procedure are similar, but the proposed procedure has the advantage of being useful in processes where sufficient out-of-control data cannot be obtained, because it does not require out-of-control data for training.

• The Korean Journal of Applied Statistics
• /
• v.36 no.2
• /
• pp.141-166
• /
• 2023

This paper investigates synthetic data generation methods and their evaluation measures. There have been increasing demands for releasing various types of data to the public for different purposes. At the same time, there are also unavoidable concerns about leaking critical or sensitive information. Many synthetic data generation methods have been proposed over the years in order to address these concerns and implemented in some countries, including Korea. The current study aims to introduce and compare three representative synthetic data generation approaches: Sequential regression, nonparametric Bayesian multiple imputations, and deep generative models. Several evaluation metrics that measure the utility and disclosure risk of synthetic data are also reviewed. We provide empirical comparisons of the three synthetic data generation approaches with respect to various evaluation measures. The findings of this work will help practitioners to have a better understanding of the advantages and disadvantages of those synthetic data methods.

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1227-1234
• /
• 2015

Traditional criteria for optimum experimental designs depend on the specifications of the model; however, there will be a dilemma when we do not have perfect knowledge about the model. Box and Draper (1959) suggested one direction to minimize bias that may occur in this situation. We will demonstrate some examples with exact solutions that provide a no-bias design for polynomial regression. The most interesting finding is that a design that requires less bias should allocate design points away from the border of the design space.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2001.06a
• /
• pp.412-415
• /
• 2001

본 연구는 초등 음악과 창작 학습을 위한 멀티미디어 활용과 그 효과를 알아보기 위하여 멀티미디어 활용 집단과 전통적인 수업 집단으로 구분하여 극용하고 학업성취도를 비교 분석해 보는 것을 목적으로 하였다. 멀티미디어 활용은 Noteworthy 1.70b를 이용하였다. 연구 대상은 부산광역시 장전초등학교 6학년 학생이며, 두 집단으로 구분하여 실시하였다. 연구의 가설로는 멀티미디어 활용 집단의 학업 성취도가 전통 수업 집단의 성취도보다 높을 것이라고 설정하였고, 결과 분석은 SAS(통계분석프로그램)를 사용하였다. 연구 결과 멀티미디어 활용 학습은 학습 집단 전체에 대한 학업 성취도를 균등하게 향상시킬 수 있다는 결론을 내릴 수 있었다.

• Korean Journal of Food Science and Technology
• /
• v.51 no.4
• /
• pp.348-355
• /
• 2019

This study investigated the impact of cellulase treatment on the extraction yield of potato starch (PS), and compared the physicochemical properties of PS by conventional (CSE) and enzymatic (ESE) starch extraction. In ESE, the PS extraction yield was predominantly influenced by reaction temperature, time and their interaction, compared to the cellulase concentration. When potatoes were treated for 8 h at $40^{\circ}C$ with 1.5% cellulase, the PS extraction yield was about 3.4-fold higher than that by CSE. Compared to CSE-PS, ESE-PS showed lower total starch contents and higher amylose contents, resulting in lower swelling factors and distorted pasting viscosity profiles accompanied by absence of peak and breakdown viscosities. However, ESE did not affect the gelatinization characteristics of PS. Overall results suggested that ESE can provide the highest yield of PS, and ESE-PS can be a potential starch source for extending the utilization of PS in food industries.

• The Korean Journal of Applied Statistics
• /
• v.7 no.2
• /
• pp.9-19
• /
• 1994

Beta-binomial model, which is reparametrized in terms of the mean probability $\mu$ of a positive deagnosis and the $\kappa$ of agreement, is widely used in psychology. When $\mu$ is close to 0, inference about $\kappa$ become difficult because likelihood function becomes constant. We consider Bayesian approach in this case. To apply Bayesian analysis, Gibbs sampler is used to overcome difficulties in integration. Marginal posterior density functions are estimated and Bayesian estimates are derived by using Gibbs sampler and compare the results with the one obtained by using numerical integration.

• Journal of the Korean Society for Library and Information Science
• /
• v.44 no.2
• /
• pp.333-356
• /
• 2010

Small libraries, with their insufficient quantities of materials and lack of diversity within the collection compared to larger general public libraries, may need to be assessed and develop collections based on empirical analysis. This study suggests a method for collection evaluation with other cases analyzing ILL (interlibrary loan) data, which is especially heavy in Small libraries in addition to the holdings and circulation data that are traditionally used in collection development. Collecting and analyzing materials proceeded from 14 Small libraries which operate ILL in a city and tried to figure out features of each library comparing collection statistics with usage statistics including circulation and interlibrary loans. It also identified subject areas heavily used in a Small library, based on the analysis of collection and usage statistics, for the purpose of formulating future policy.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.2
• /
• pp.93-101
• /
• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Journal of Science Education
• /
• v.35 no.2
• /
• pp.283-294
• /
• 2011

The purpose of this study were to develop a tutorial for secondary students to understand of basic concepts of special relativity, which is appropriate for the cognition level of secondary student. We developed the concept evaluation tool and the tutorial material. Result from pretest and post-test are presented to verify the effect of the tutorial for helping student understanding of the concept such as time, event, reference frame, relativity of simultaneity. Secondary student had intense cognitive conflict about the complex concepts such as simultaneity, length contraction and time expansion. This tutorial could be proposed methodology to overcome cognitive difficulty for understanding these concepts.