• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.504-513
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• 2017

This study investigated one of the controversial issues on debate or even controversial between policy makers at the government and corporate levels: To examine any financial determinants on the cash holdings of the firms in the advanced and emerging capital markets. Futhermore, it focused on the large representative firms headquartered in the U.S. and the Republic of Korea, taking into account scarcity of the previous literature concentrated on the comparative studies on this particular subject. Several legitimate, but robust econometric estimations such as static and dynamic panel data models and Tobit regression, were applied to investigate possible financial factors ono the cash liquidity. Given the continued debates or arguments on the excess cash reserves between interest partied at the government and corporate levels in the advanced and/or emerging capital markets, and more accelerated capital transfers among associated nations by engaging in the arrangements of the FTAs, the results of the study may provide a vision to search for the optimal level of corporate cash holdings for firms in the two nations.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.71-81
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• 2010

The problem of finding clusters is widely used in numerous applications, such as pattern recognition, image analysis, market analysis. The important factors that decide cluster quality are the similarity measure and the number of attributes. Similarity measures should be defined with respect to the data types. Existing similarity measures are well applicable to numerical attribute values. However, those measures do not work well when the data is described by categorical attributes, that is, when no inherent similarity measure between values. In high dimensional spaces, conventional clustering algorithms tend to break down because of sparsity of data points. To overcome this difficulty, a subspace clustering approach has been proposed. It is based on the observation that different clusters may exist in different subspaces. In this paper, we propose a new similarity measure for clustering of high dimensional categorical data. The measure is defined based on the fact that a good clustering is one where each cluster should have certain information that can distinguish it with other clusters. We also try to capture on the attribute dependencies. This study is meaningful because there has been no method to use both of them. Experimental results on real datasets show clusters obtained by our proposed similarity measure are good enough with respect to clustering accuracy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.27-40
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• 2023

This study analyzes the factors affecting the survival of young start-up companies. A youth start-up company was defined as a company with a founder's age under 39. The study was based on evaluation data from 3,540 companies evaluated by the Technology Guarantee Fund to support youth start-up guarantees during the period from 2012 to 2015. In this study, independent variables were defined as founder characteristics, start-up environment, and start-up strategy, and entrepreneurship, knowledge level, and development capabilities were set as variables for start-up characteristics, competition conditions and comparative advantage with alternatives in the start-up environment, and item novelty, commercialization plan and financing plan were set as variables. For variable measurement, the evaluation index of the youth start-up evaluation model of the Technology Guarantee Fund was used. Management performance was defined as the survival of a company, and the survival of 12, 36, 60, and 84 months was measured based on the occurrence of insolvency registered by the Korea Technology Guarantee Fund. The Cox proportional risk model was used for hypothesis testing. As a result of the analysis, knowledge level and development capability were statistically significant in the characteristics of the founder, and the financing plan in the start-up strategy was statistically significant regardless of the survival period. Among the start-up strategies, the novelty of the item had a positive effect on survival after 36 months. Entrepreneurship was significant only in 12-month survival. The most important order for survival was identified in the order of financing plan, knowledge level, item novelty and development capability, of which the founder's knowledge level in the beginning and the funding plan in the second half had the greatest impact.

• Knowledge Management Research
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• v.23 no.3
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• pp.213-229
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• 2022

This study aimed to reveal factors that contribute to the creation of collective intelligence (CI) and to provide a cornerstone for future studies on this subject. We hypothesized that effects of the complexity and meaningfulness of the task, diversity, openness to experience, independence, decentralization, and the use of information and communication technology (ICT) are preconditions for the creation of CI. To investigate these hypotheses, we surveyed 200 individuals in the research and development-based manufacturing industry and collected a total of 185 valid responses. The results of the analysis showed that the meaningfulness of the task, openness to experience, independence, decentralization, and the use of ICT had positive effects on CI. Both perceived dissimilarity and value diversity had negative effects on CI. When all variables were included, their significance for the creation of CI showed the following order: use of ICT, the meaningfulness of the task, openness to experience, perceived dissimilarity, and value difference. The theoretical and empirical implications of these results were discussed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.71-86
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• 2023

Although starting a business plays a key role in strengthening national competitiveness and creating jobs, it is recognized as a risky choice. Failure to start a business can result in a wide range of negative effects, such as loss of personal wealth as well as deterioration of national competitiveness. This study considers startups that have reached a level of sustainable growth by achieving performance above the minimum profitability and sales standards for KOSDAQ listing, or achieved EXIT through sale or listing, as successful startups. based on the practical experiences of 23 successful entrepreneurs and Based on perception, the importance and priorities of startup success factors were derived through stratification analysis (Analytic Hierarchy Process, AHP), and interviews were conducted. In particular, using the ERIS model, we comprehensively analyze various variables of a start-up by considering the four elements of the entrepreneur, resources, industry, and strategy, and examine the changes and importance of success factors according to the characteristics of each growth stage of the start-up. As a goal, we specifically identified the challenges and opportunities faced by entrepreneurs at each stage. As a result of the study, the order of importance of the top factors of success factors in the start-up period was found to be the entrepreneur, resources, industry, and strategy. In particular, the importance of the entrepreneur's entrepreneurship spirit, special capabilities, general capabilities, and human resources was emphasized. The order of importance of the top factors of success factors during the growth period was found in the following order: entrepreneur, resources, industry, and strategy. In particular, the importance of general capabilities, entrepreneurship, and human and organizational resources was emphasized. This study is significant in that it analyzes startup success factors from the perspective of successful entrepreneurs and provides useful insights and directions to entrepreneurs and policy makers.

• Convergence Security Journal
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• v.6 no.3
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• pp.117-126
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• 2006

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.509-516
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• 2004

This study is to investigate the amount of biased estimates for heritability and genetic correlation according to data structure on marbling scores in Korean cattle. Breeding population with 5 generations were simulated by way of selection for carcass weight, Longissimus muscle area and latent values of marbling scores and random mating. Latent variables of marbling scores were categorized into five by the thresholds of 0, I, 2, and 3 SD(DSI) or seven by the thresholds of -2, -1, 0,1I, 2, and 3 SD(DS2). Variance components and genetic pararneters(Heritabilities and Genetic correlations) were estimated by restricted maximum likelihood on multivariate linear mixed animal models and by Gibbs sampling algorithms on multivariate threshold mixed animal models in DS1 and DS2. Simulation was performed for 10 replicates and averages and empirical standard deviation were calculated. Using REML, heritabilitis of marbling score were under-estimated as 0.315 and 0.462 on DS1 and DS2, respectively, with comparison of the pararneter(0.500). Otherwise, using Gibbs sampling in the multivariate threshold animal models, these estimates did not significantly differ to the parameter. Residual correlations of marbling score to other traits were reduced with comparing the parameters when using REML algorithm with assuming linear and normal distribution. This would be due to loss of information and therefore, reduced variation on marbling score. As concluding, genetic variation of marbling would be well defined if liability concepts were adopted on marbling score and implemented threshold mixed model on genetic parameter estimation in Korean cattle.

• Applied Chemistry for Engineering
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• v.9 no.7
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• pp.990-997
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• 1998

In this work, the $SiO_2$ films on the silicon substrate with different orientations were first prepared by the low temperature process using the ECR plasma diffusion as a function of microwave power and oxidation time. Before and after thermal treatment, the surface morphology, Si/O ratio from physicochemical properties, and the electrical properties of the oxide films were also investigated. The oxidation rate increased with microwave power, while surface morphology showed the nonuniform due to etching. The film quality, therefore, was lowered with increasing the defect by etching and the content of positive oxide ions in the oxide films from bulk by higher self-DC bias. The content of positive oxide ions in the oxide films with different Si orientations showed Si(100) < Si(111) < poly Si. The defects in $Si/SiO_2$ interface of $SiO_2$ film could be decreased by annealing, while $Q_{it}$ and $Q_f$ were independent of thermal treatment and the dependent on concentration of reactive oxide ions and self-DC bias of substrate. At microwave power of 300, and 400 W, the high quality $SiO_2$ film that had lower surface roughness and defect in $Si/SiO_2$ interface was obtained. The value of interface trap density, then, was ${\sim}9{\times}10^{10}cm^{-2}eV^{-1}$.

• Applied Biological Chemistry
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• v.39 no.4
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• pp.287-292
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• 1996

The present study was carried out to investigate the difference of absorption and translocation of Ni by radish grown for 45 days under factors such as the levels of Ni in soil, soil pH, competitive metal interactions, covering with soil, organic matter and lime. Germination rate of radish in soil treated by Ni 150 mg/kg was 40%. As soil pH was increased, the contents of Ni translocated to radish showed the negative correlation$(shoot=-0.965^{**},\;root= -0.837^{*})$. The Ni content in shoot and root of radish grown under 50 mg/kg Ni treated soil were slowly decreased with iron levels increased. However Ni content in shoot and root of radish grown under 50 mg/kg Ni treated soil showed increasing trend with the increasing the zinc levels. As covering with soil, organic matter and lime were treated, Ni contents of shoot and root in radishes were in the decreasing order of organic matter>covering with soil>lime.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.