• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1439-1447
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• 2014

Most of literatures on Korean child aggression are based on using the cross-sectional data sets. Although there is a related study with a longitudinal data set, it is assumed that the data sets measured repeatedly in the longitudinal data are mutually independent. A longitudinal data analysis for Korean child aggression is then necessary. This study is to analyze the effect of child development outcomes including academic achievement, self-esteem, depression anxiety, delinquency, victimization by peers, abuse by parents and internet using time on child aggression with Korea Welfare Panel Study data observed three times between 2006 and 2012. Since Korea Welfare Panel Study data have missing values, the missing at random is assumed. The linear mixed effect model and the restricted maximum likelihood estimation are considered.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.445-466
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• 2016

Nonparametric Bayesian methods have seen rapid and sustained growth over the past 25 years. We present a gentle introduction to the methods, motivating the methods through the twin perspectives of consistency and false consistency. We then step through the various constructions of the Dirichlet process, outline a number of the basic properties of this process and move on to the mixture of Dirichlet processes model, including a quick discussion of the computational methods used to fit the model. We touch on the main philosophies for nonparametric Bayesian data analysis and then reanalyze a famous data set. The reanalysis illustrates the concept of admissibility through a novel perturbation of the problem and data, showing the benefit of shrinkage estimation and the much greater benefit of nonparametric Bayesian modelling. We conclude with a too-brief survey of fancier nonparametric Bayesian methods.

• Convergence Security Journal
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• v.21 no.3
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• pp.57-66
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• 2021

As the detection performance using deep learning and machine learning of the intrusion detection field has been verified, the cases of using it are increasing day by day. However, it is difficult to collect the data required for learning, and it is difficult to apply the machine learning performance to reality due to the imbalance of the collected data. Therefore, in this paper, A mixed sampling technique using t-SNE visualization for imbalanced data processing is proposed as a solution to this problem. To do this, separate fields according to characteristics for intrusion detection events, including payload. Extracts TF-IDF-based features for separated fields. After applying the mixed sampling technique based on the extracted features, a data set optimized for intrusion detection with imbalanced data is obtained through data visualization using t-SNE. Nine sampling techniques were applied through the open intrusion detection dataset CSIC2012, and it was verified that the proposed sampling technique improves detection performance through F-score and G-mean evaluation indicators.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.109-109
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• 2022

The global population is increasing every year, and the amount of food waste is also increasing. Direct landfilling of food waste has been prohibited since 2005, and in accordance with the London Convention in 2013, the discharge of livestock manure, sewage sludge, and food waste into the sea is prohibited. In the case of incineration to treat the discharged food waste, the heat point is lowered due to the moisture in the food waste itself, so fuel must be added. Therefore, this study was conducted to get basic data for setting the limit of application by investigating the growth and yield of crops after treating food waste dry powder mixed fertilizer (MF) on red pepper. In the experiment, continuous cultivation was carried out for two years in 2021 (1st year) and 2022 (2nd year). The treatment groups were set as Not Treatment (NT), Chemical Fertilizer (CF), Mixed Fertilizer (MF), Mixed Fertilizer×2 (MF×2). After harvest, crop growth and yield were investigated. As a result of the 1st years of growth survey, CF, MF, MF×2 show significant difference in shoot length compared to NT. About fresh weight and dry weight, CF show significant difference compared to NT. The 2nd years of growth survey, the shoot and root length, fresh weight did not show significant difference with NT. In case of dry weight, MF is significant increased compared to NT. As a result of the yield survey of the 1st year, all treatment groups did not show a significance in yield compared to the NT. In case of 2nd year, all treatment groups show significantly increased value compared to NT. The yield of MF was highest among the treatment groups. In the future, it is thought that it is necessary to quantitatively evaluate the effect of food waste dry powder mixed fertilizer through additional experiments and continuous cultivation, and to establish an appropriate amount of use and establishment of a manual based on this.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.355-361
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• 2010

This paper addresses a mathematical approach to decision making in a real-world material distribution situation. The problem is characterized by a low-volume and highly-varied mix of products, therefore there is a lot of material movement between the facilities. This study focuses especially on the transportation scheduler with a tool that can be used to quantitatively analyze the volume of material moved, the type of truck to be used, production schedules, and due dates. In this research, we have developed a mixed integer programming problem using the minimum cost, multiperiod, multi-commodity network flow approach that minimizes the overall material movement costs. The results suggest that the optimization approach provides a set of feasible solution routes with the objective of reducing the overall fleet cost.

• Journal of Distribution Science
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• v.18 no.7
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• pp.73-81
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• 2020

Purpose: This research examines the factors that influence organic food purchasing decisions of kindergartens in Ho Chi Minh City, Vietnam. Research Design, Data, and Methodology: A mixed-method research was utilized in this study. It included a focus group of 10 participants and a survey of 304 respondents, (quantitative research) who are employed in the selected kindergartens, using both online and paper surveys based on nonprobability and convenient sampling. The SPSS and SmartPLS 3 software were used to analyze data. Results: a) Eight factors affect the purchase decision of kindergartens; b) Environment Attention, Normative Beliefs, Trust belief on brand, Cost of meal set, and Reference group positively affect Intention behavior; c) Feeling safe positively affect Perceived Quality Product. Perceived quality of product and Intention behavior positively affect organic food Purchase Decision of kindergartens. Conclusion: Eight factors affect organic food purchasing decisions of kindergartens in Ho Chi Minh City. This study offers recommendation and solutions for a stable output of organic products in Vietnam, and ways to popularize them within the community.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.445-453
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• 2011

Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.2-96
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• 2002

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme that represents the information from observations as a set of random variables in the form of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other, which can be utilized in linear p...

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1158-1165
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• 2021

This paper proposes a CNN accelerator which is optimized Pooling layer operation incorporated in Multiplication And Accumulation(MAC) to reduce the memory size. For optimizing memory and data path circuit, the quantized 8bit integer weights are used instead of 32bit floating-point weights for pre-training of MNIST data set. To reduce chip area, the proposed CNN model is reduced by a convolutional layer, a 4*4 Max Pooling, and two fully connected layers. And all the operations use specific MAC with approximation adders and multipliers. 94% of internal memory size reduction is achieved by simultaneously performing the convolution and the pooling operation in the proposed architecture. The proposed accelerator chip is designed by using TSMC65nmGP CMOS process. That has about half size of our previous paper, 0.8*0.9 = 0.72mm². The presented CNN accelerator chip achieves 94% accuracy and 77us inference time per an MNIST image.