• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.858-887
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• 2017

This study deals with the cases of policy changes of the research council system which exists since 1999 as a policy tool to achieve the goals for autonomy and independence of Government-funded Research Institutes (GRIs) in the field of science and technology and their cooperation and organic connection. The purpose of the study was to analyze through which process policies about the research council system have changed and whether they resulted in the successful achievements of the aimed policy goals, and to contribute to the effective policy implementation in the future. The Research Council System of Science and Technology has been undergoing a change for four government replacements (from Kim Dae-jung's Government to Park Geun-hye's Government). In this study, the major policy making factors and the policy change process of each government case are analyzed in comparison using Kingdon's multiple streams framework, as a result, their policy change type by the Hogwood and Peters's theory and the achievement of their policy goals of autonomy and responsibility were examined.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.231-251
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• 2010

The numbers of SCI paper or patent in science and technology are expected to be related with the number of researcher and knowledge stock (R&D stock, paper stock, patent stock). The results of the regression model showed that severe multicollinearity existed and errors were made in the estimation and testing of regression coefficients. To solve the problem of multicollinearity and estimate the effect of the independent variable properly, principal component regression model were applied for three cases with S&T knowledge production. The estimated principal component regression function was transformed into original independent variables to interpret properly its effect. The analysis indicated that the principal component regression model was useful to estimate the effect of the highly correlate production factors and showed that the number of researcher, R&D stock, paper or patent stock had all positive effect on the production of paper or patent.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.79-88
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• 2008

On the internet, the NIDS(Network Intrusion Detection System) has been widely deployed to protect the internal network. The NIDS builds a set of rules with analysis results on illegal packets and filters them using the rules, thus protecting the internal system. The number of rules is ever increasing as the attacks are becoming more widespread and well organized these days. As a result, the performance degradation has been found severe in the rule application fer the NIDS. In this paper, we propose a multiple pattern matching scheme to improve rule application performance. Then we compare our algorithm with Wu-Mantel algorithm which is known to do high performance multi-pattern matching.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2813-2827
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• 2018

Progressive censoring schemes have become quite popular in reliability study. Under progressive censored data, however, some units can be failed between two points of observation with exact times of failure of these units unobserved. For example, loss may arise in life-testing experiments when the failure times of some units were not observed due to mechanical or experimental difficulties. Therefore, multiply progressive censoring scheme was introduced. So, we derives a maximum likelihood estimator of the parameter of exponential distribution. And we introduced the goodness-of-fit test statistics using order statistic and Lorenz curve. We carried out Monte Carlo simulation to compare the proposed test statistics. In addition, real data set have been analysed. In Weibull and chi-squared distributions, the test statistics using Lorenz curve are more powerful than test statistics using order statistics.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.768-783
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• 2020

Purpose: In response to the critical risk of fire due to the characteristic of Publicly used establishments(hereinafter referred to as PUE), 'Special Act on the Safety Control of PUE' was enacted in 2006 and is still in operation. However, in spite of numerous revisions so far, still there are problems to be resolved. This study analyses the regulatory regime of fire safety in UK to find measures which could fundamentally improve the safety management of PUE. Method: This study compares and analyses the safety management system of PUE in Korea and the case of the UK by using the comparative research method. Result: As a result of the qualitative analysis, some noticeable systems and concepts of the UK regulatory regime have been discovered(e.g. 'Responsible person', 'Fire risk assessment', 'Fire safety audit', etc.) and consequently, 'Proposal for the Fire Safety Management System for PUE' is designed based on the findings from examples in the UK and drawbacks of safety management of PUE. Conclusion: This study proposes the way to improve the safety management of PUE into a more rational and effective system by analysing the case of the UK, which reorganised the fire safety management to the private sector centered, in which the fire authority minimizes intervention.

• Korean Chemical Engineering Research
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• v.46 no.6
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• pp.1087-1094
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• 2008

This article presents a multi-scale simulation approach starting from the molecular level for the adsorption process development, specifically, in n-hexane adsorption on activated carbon. A grand canonical Monte-Carlo(GCMC) method is used for the prediction of adsorption isotherms of n-hexane on activated carbon at the molecular level. Geometric effects and hydrodynamic properties of the adsorption column are examined by means of the two dimensional CFD(computational fluid dynamics) simulation. The adsorption isotherms from the molecular simulation and the axial diffusivity from the CFD simulation are exploited for the process simulation where the elution curve of n-hexane is obtained. For the first moment(mean residence time) of the pulse-response with respect to temperature and flowrate, the process simulation results obtained from this three-steps multiscale simulation approach show a good agreement with experimental data within 20% of maximum difference. The multi-scale simulation approach addressed in this study will be useful to accelerate the adsorption process development, while reducing the number of experiments required.

• Journal of the Korean GEO-environmental Society
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• v.9 no.7
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• pp.53-60
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• 2008

The purpose of this study is to make case studies on estimation of shear wave velocity in core zone of some rockfill dams by MASW (Multi-channel Analysis of Surface Waves) and to compare the results of case studies with those of the empirical method. Furthermore, the purpose is to recommend the range of shear wave velocity in core zone by MASW and to supply the preliminary data for estimation of shear wave velocity in core zone which is needed for dynamic analysis. From the results of case studies and the comparison between the results of case studies and those of empirical equation, it was found that the shear wave velocities obtained by MASW were smaller than those by the empirical recommendation (Sawada & Takahashi) in the depth of more than 10 m. Also, it is recommended that using the lower bound of empirical formulation by Sawada and Takahashi be available and resonable in case that MASW is not available due to the field condition and the investigation is preliminary.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.147-150
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• 2004

Firstly, the precipitation data have to be interpolated for the estimation of water resources. For this purpose, the correlative analysis is made between the topographic variables, which, influence the precipitation phenomena, are classified by elevation(ELEV), slope(SLOPE), distance to the sea(SEA), obstacle (OBST), barrier(BAR), and roughness index(SHIELD), using TOVA(Topographic Variables Extraction Program) and events precipitation during the periods from january the 1st 2000 to December 31 2002. The coefficients of determination show that each event has different topographic influence and ELEV, SLOPE and OBST to the South-West, and SHIELD of every direction have close relationship with the precipitation. The multiple regression model explains 96% of the spatial variation of precipitation.