• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.1-27
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• 2014

This paper aims at exploring the multi-scalar processes through which the Gumi Industrial Complex was developed in the late 1960s and the early 1970s. Existing studies, influenced by the "Developmental State Thesis", tend to see the industrialization processes of South Korea either by focusing on the socio-politico-economic processes at the national scale or in terms of the plan rationality of the national bureaucrats. This paper, however, denies this perspective on the basis of the strategic relational approach to the state and the multi-scalar perspective. In particular, it argues that the state actions for national industrialization have been the outcome of complex interactions, conflicts and negotiations among social forces, acting in and through the state, and at diverse geographical scales. This paper attempts to empirically prove this argument on the basis of a case study on the construction processes of Gumi Industrial Complex. The development of Gumi Industrial Complex cannot be solely explained in terms of either the plan rationality of the national bureaucrats or the political motivation related to the fact that Gumi was the hometown of President Park Jung-Hee. This paper argues that the development of Gumi Industrial Complex was heavily influenced by the role of the following actors; place-dependent local actors in Gumi and the multi-scalar agents, such as the Korean-Japanese businessmen and the national parliament members elected in the Gumi electoral district.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.523-533
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• 2022

This paper presents a two-stage spatio-spectral fusion method (2SSFM) based on area-to-point regression kriging (ATPRK) to enhance spatial and spectral resolutions using multi-sensor satellite images with complementary spatial and spectral resolutions. 2SSFM combines ATPRK and random forest regression to predict spectral bands at high spatial resolution from multi-sensor satellite images. In the first stage, ATPRK-based spatial down scaling is performed to reduce the differences in spatial resolution between multi-sensor satellite images. In the second stage, regression modeling using random forest is then applied to quantify the relationship of spectral bands between multi-sensor satellite images. The prediction performance of 2SSFM was evaluated through a case study of the generation of red-edge and short-wave infrared bands. The red-edge and short-wave infrared bands of PlanetScope images were predicted from Sentinel-2 images using 2SSFM. From the case study, 2SSFM could generate red-edge and short-wave infrared bands with improved spatial resolution and similar spectral patterns to the actual spectral bands, which confirms the feasibility of 2SSFM for the generation of spectral bands not provided in high spatial resolution satellite images. Thus, 2SSFM can be applied to generate various spectral indices using the predicted spectral bands that are actually unavailable but effective for environmental monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.107-114
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• 2017

In general, credibility for some analysis results is very important to most decision makers. Especially, it is even more critical for military commander to choose the best course of action by using the simulation results when he want to decide to allocate his available weapon system assets. Therefore, improving the credibility of simulation output is one of the key issues in military research fields. In this paper, we proposed a new simulation framework to improve the credibility of weapon's effectiveness analysis results. Multi-agent based simulation tool is applied and compare current process to the proposed framework. We also showed an example case when a communication repeaters are installed to expand the commanding area scope. The example clearly tells why this new simulation framework is more efficient and improve the credibility of simulation results.

• 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.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1149-1189
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• 2014

Nowadays, many farmers use online management diagnosis tool developed by Rural development agency(RDA) for the purpose of self-diagnosis of their farm management. Database(DB) was created using the diagnosis results and has been used for agri-firm management consulting. However, the amount of diagnosis data in the DB has been decreasing year by year. This means that the diagnosis tool of RDA did not reach farmers' expectation. Therefore it is necessary to develop a practical consulting tool which is applicable for various types of agri-firm management. This study introduces a management diagnosis tool and consulting method based on multi-contingency organization theory and value chain model for the purpose of improving existing tools and methods. The consulting method based on multi-contingency organization theory shows the core strategy of agri-firms by two different ways such as "efficiency-oriented" direction and "effectiveness-orientated" direction. Also, this method emphasizes that the performance of firm can be achieved when subelements of firm activities follow the same direction with the orientation of core strategy. The important thing is the right firm management activity fitted to its strategic direction. Through this action, limited firm resources can be optimized. In order to make itself understand, this study shows a practical example applied by this method from actual agri-firms.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.107-115
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• 2014

This paper evaluates and analyzes the performance of TILE-Gx36(Gx36), a many-core processor. The PARSEC parallel benchmark suite was used to measure the performance, and Core i7 (i7) and Atom are used for the performance comparison. When experimented with the maximum number of threads that can be executed concurrently on each machine, Gx36 showed a 2.73${\times}$ inferior performance to Core i7 and a 1.93${\times}$ superior performance to Atom. Gx36 has the largest Last Level Cache(LLC) among the compared processors. Nevertheless, it reported the biggest number of LLC misses, which, we strongly believe, is the major culprit for lower performance than expected. Our study suggests that the DDC employed in Gx36 is not a favorable cache structure for the general-purpose high-performance computing. The actual measurement with off-the-shelf machine provides non-biased data for polishing the future many-core architecture.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.83-97
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• 2019

As society develops, the dissemination of microdata has increased to respond to diverse analytical needs of users. Analysis of microdata for policy making, academic purposes, etc. is highly desirable in terms of value creation. However, the provision of microdata, whose usefulness is guaranteed, has a risk of exposure of personal information. Several methods have been considered to ensure the protection of personal information while ensuring the usefulness of the data. One of these methods has been studied to generate and utilize synthetic data. This paper aims to understand the synthetic data by exploring methodologies and precautions related to synthetic data. To this end, we first explain muptiple imputation, Bayesian predictive model, and Bayesian bootstrap, which are basic foundations for synthetic data. And then, we link these concepts to the construction of fully/partially synthetic data. To understand the creation of synthetic data, we review a real longitudinal synthetic data example which is based on sequential regression multivariate imputation.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.435-442
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• 2009

Multiple genes interacting is a difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction(MDR) statistical method by dummy variables was applied to identify interaction effects of single nucleotide polymorphisms(SNPs) responsible for longissimus mulcle dorsi area(LMA), carcass cold weight(CWT) and average daily gain(ADG) in a Hanwoo beef cattle population.

• Annual Conference of KIPS
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• 2005.05a
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• pp.263-266
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• 2005

다중센서데이터 융합시스템(MSDFS)은 여러 센서로부터 획득된 이질의 데이터를 정규화된 포맷으로 융합하고 단일 센서에서의 획득오차를 최소한으로 줄여 표적의 정확한 식별 및 판단을 지원하는 시스템이다. 이 시스템들은 고유의 기능을 수행하는 모듈들에 대한 고수준의 재사용성을 요구하므로, 현재의 소프트웨어공학 기법을 적용시 공통부분에 대한 효율적 설계가 어렵다. 따라서 본 논문에서는 시스템 개발에 이러한 비효율적인 요소를 제거하는 제품-계열 개발방법론을 MSDFS의 임베디드 소프트웨어 설계에 적용한다. 이를 위해 분석 대상에 대한 영역지정에서부터 재사용가능한 컴포넌트의 식별까지 설계 하며, 마지막으로 설계된 모델에 대한 검증을 위해 GQM 패러다임을 적용한다. 또한 산출물에 대한 성능평가 기준을 제시하여 시스템 개발을 효과적으로 향상시킬 수 있는 방안을 제시한다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.81-109
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• 2017

본 연구는 Kingdon(1984)과 Howlett(2014) 모델을 결합한 수정모델을 적용하여 국제과학비즈니스벨트의 정책결정과정을 분석하였다. 그 과정에서 어떠한 사회 정치적 요인들이 영향을 미쳤는지에 대해 알아보고 향후, 국제과학비즈니스벨트 사업과 거대기초과학 정책 추진방향을 모색하고자 하였다. 구체적인 분석은 정책결정과정을 정책의재, 정책형성, 정책집행의 단계로 구분하고, 문제 과정 정책 정치의 흐름과 창, 정책선도자를 변수로 활용하였다. 분석결과, 정책결정과정에서 정치의 흐름과 정책선도자의 역할이 중요하게 작용하였고, 과학자 집단보다는 정치가들과 정부 관료자들이 주도적으로 참여하였으며 그 과정속에서 정치적 혼란을 겪으며 급진적으로 진행되었다는 점을 알 수 있었다.