• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• Land and Housing Review
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• v.14 no.1
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• pp.67-82
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• 2023

Using the Artificial Neural Network model and Binary Logistic Regression model, this study investigates influence factors on the quality of life in terms of housing environment during the COVID-19 in Seoul. The results show that the lower the satisfaction level of housing policy, the lower the quality of life in the employment field and the lower the quality of residential field. On the other hand, permanent workers and self-employed respondents have experienced improvement in residential quality during the pandemic. A limitation of this study is associated with disentangling the causal relationship using the 'black box' characteristics of ANN method.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• The KIPS Transactions:PartD
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• v.12D no.7 s.103
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• pp.1017-1022
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• 2005

There are many changes in the software requirements during the whole software life cycle. These changes require modification of the software, and it is important to keep software quality and stability while we are modifying the software. Refactoring is one of the technology to keep software quality and stability during the software modification; there are many researches related to automatic refactoring. In this paper, we propose three factors for Move Method which is one of the refactoring technique. We applied binomial logistic analysis to data which were extracted from sample program by each factor. The result of this process was very close to the result of manual analysis by program experts. Furthermore, we found that these factors have major roll to determine Position of a method, and these factors can be used as a basis of finding optimal position of a method.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.291-300
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• 1999

인터넷을 기반으로 한 정보통신의 급속한 발전이라는 기업환경의 변화에 적응하기 위해서 기업은 점차 모든 경영시스템을 인터넷을 기반으로 하도록 변화시키고 있을 뿐만 아니라, 기업 조직 또한 전세계를 기반으로한 글로벌 기업 형태로 변화하고 있다. 이러한 급속한 경영환경의 변화로 인해서 기업 내에서는 종전과는 다른 형태의 부서간 상호의사결정조정 과정이 필요하게 되었다. 일반 기업들을 대상으로 한 상호의사결정의 지원과정에 대해서는 기존에 많은 연구들이 있었으나 글로벌기업과 같은 네트워크 형태의 새로운 형태의 기업에 있어서의 상호의사결정과정을 지원할 수 있는 의사결정지원시스템에 대해서는 단순한 그룹의사결정지원시스템 또는 분산의사결정지원시스템과 같은 연구들이 주를 이루고 있다. 따라서 본 연구에서는 인터넷 특히, 웹을 기반으로 한 기업의 글로벌경영 및 분산 경영에서 비롯되는 부서간 상호의사결정이라는 문제를 효율적으로 지원할 수 있는 기업의 글로벌경영 및 분산 경영에서 비롯되는 부서간 상호의사결정이라는 문제를 효율적으로 지원할 수 있는 메커니즘을 제시하고 이에 기반한 프로토타입 형태의 시스템을 구현하여 성능을 검증하고자 한다. 특히, 기업 내에서 가장 대표적으로 상호의사결정지원이 필요한 생산과 마케팅 부서를 대상으로 상호의사결정지원 메커니즘을 개발하고 실험을 진행하였다. 그 결과 글로벌 기업내의 생산과 마케팅 부서간 상호의사결정을 효율적으로 지원 할 수 있는 상호조정 메카니즘인 개선된 PROMISE(PROduction and Marketing Interface Support Environment)를 기반으로 한 웹 분산의사결정지원시스템 (Web-DSS : Web-Decision Support Systems)을 제안하는 바이다.자대상 벤처기업의 선정을 위한 전문가시스템을 구축중이다.의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.139-148
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• 1999

오늘날 기술집약적인 벤처기업들에 대한 관심이 집중되고 있다. 소수의 진취적인 벤처기업들이 기술개발 및 신상품 개발 등 두드러진 활약을 보이고 있기 때문이다. 그러나 실제 이 벤처기업의 성공 가능성은 그렇게 높지 않다. 특히 벤처기업 환경이 아직 미약한 국내의 경우 위험부담이 훨씬 더 크다. 이러한 벤처기업 환경에서 투자대상 벤처기업을 선정하는 것은 매우 전략적인 의사결정이다. 일반적으로 일반 벤처투자가들은 관심이 있는 산업에 해당하는 기업의 사업계획서와 기초적인 관련 정보를 토대로 투자여부를 결정한다. 그렇지만 실제로는 이와 같은 분석에 필수적으로 요구되는 정보가 불확실할 뿐만 아니라 기술분야에 대한 전문적 지식도 부족하기 때문에 투자 여부를 결정하는 것은 매우 복잡하고 어려운 문제이다. 그러므로 투자대상 벤처기업의 선정을 효과적으로 지원해주는 체계적인 접근이 필요하다. 특히 벤처 사업과 관련된 기술 동향 및 수준 등에 관련된 전문 지식과 경험이 체계적으로 제공되어야 하고 또한 벤처 투자가의 개인적 경험과 판단이 평가 프로세스에 직접적으로 반영될 수 있어야 한다. 이에 본 연구에서는 전문가의 지식과 경험을 체계화하고 투자가의 개인적 판단을 효과적으로 수용할 수 있는 전문가시스템의 접근방법을 제시하고자 한다. 투자대상 벤처기업의 선정을 위한 전문가시스템을 구축하기 위해 본 연구에서는 다양한 정보수집 과정을 거쳤다. 우선 벤처 투자와 관련된 기존 문헌을 심층 분석하였으며 아울러 벤처 투자 업계에서 활약중인 전문 벤처캐피탈리스트들과의 수차례 인터뷰를 통해 벤처기업 평가의 주요 요인과 의사결정 과정을 파악할 수 있었다. 이러한 과정을 통하여 본 연구에서는 벤처 투자의 90%를 차지하는 정보통신분야에 속한 기법 중에서 투자대상 벤처기업의 선정을 위한 전문가시스템을 구축중이다.의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and in

• Korean Journal of Social Welfare Studies
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• v.49 no.4
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• pp.65-100
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• 2018

Korean Youth are facing diverse problems. For-instance Korean youth are even called '7 given-up generation' which indicates that they gave up marriage, giving birth, social relationship, housing, dream and the hope. From this point, the study concludes that the influential factors of the alcohol problems of youth should be studied based on the eco social perspectives. And it adopted data-mining methods, using SAS-Enterprise Miner for the analysis, targeting 2538 youths. Specifically, the study analyzed and chose the most predictable model using decision tree analysis, artificial neural network and logistic analysis. As the result, the study found that gender, age, smoking, spouse, family-number, jobsearching and economic participation are statistically significant determinants of alcohol problems of youth. Precisely, those who are male, younger, have the spouse, have less family number, searching jobs, have more income and have the job were more prone to have the alcohol problems. Based on the result, this study proposed the addiction problems targeting youth and etc. and expect to have the contribution on implementing procedures for the alcohol problems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.535-543
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• 2019

Recently, growing social interest in alley markets, which have shown rapid growth like Gyeonglidan-gil street in Seoul, has led to the need for an analysis of growth factors. This paper aims at exploring growing alley markets through time-series clustering using DTW (Dynamic Time Warping) and examining the growth factors through logistic regression. According to cluster analysis, the number of growing markets of the Northeast, the Southwest, and the Southeast were much more than the Northwest but the proportion in region of the Northwest, the Northeast, and the Southwest were much more than the Southeast. Logistic regression results show that people in 20s and 30s have a lower impact on sales than those in 50s, but have a greater impact on growth of alley market. Alley markets located in high-income areas often reached their growth limits, indicating a tendency to stagnate or decline. The proximity of a subway station effected positive on sales but negative on growth. This research is an advanced study in that the causes of sales growth of alley markets is examined, which has not been examined in the preceding study.

• Spatial Information Research
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• v.8 no.1
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• pp.85-99
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• 2000

Accurate information on habitat distribution of protected fauna is essential for the habitat management of Korea, a country with very high development pressure. The objectives of this study were to develop a habitat suitability model of wild boar based on GIS and logistic regression, and to create habitat distribution map, and to prepare the basis for habitat management of our country s endangered and protected species. The modeling process of this restudyarch had following three steps. First, GIS database of environmental factors related to use and availability of wild boar habitat were built. Wild boar locations were collected by Radio-Telemetry and GPS. Second, environmental factors affecting the habitat use and availability of wild boars were identified through chi-square test. Third, habitat suitability model based on logistic regression were developed, and the validity of the model was tested. Finally , habitat assessment map was created by utilizing a rule-based approach. The results of the study were as folos. First , distinct difference in wild boar habitat use by season and habitat types were found, however, no difference in wild boar habiat use by season and habitat types were found , however, ho difference by sex and activity types were found. Second, it was found, through habitat availability analysis, that elevation , aspect , forest type, and forest age were significant natural environmental factors affecting wild boar hatibate selection, but the effects of slope, ridge/valley, water, and solar radiation could not be identified, Finally, the habitat at cutoff value of 0.5. The model validation showed that inside validation site had the classification accuracy of 73.07% for total habitat and 80.00% for cover habitat , and outside validation site had the classification accuracy of 75.00% for total habitat.

• The Journal of Engineering Geology
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• v.14 no.4 s.41
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• pp.443-450
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• 2004

This study developed a prediction model of debris flow to predict a landslide probability on natural terrain composed of the Tertiary sedimentary and volcanic rocks using a logistic regression analysis. The landslides data were collected around Pohang, Gyeongbuk province where more than 100 landslides were occurred in 1998. Considered with basic characteristics of the logistic regression analysis, field survey and laboratory soil tests were performed for both slided points and not-slided points. The final iufluential factors on landslides were selected as six factors by the logistic regression analysis. The six factors are composed of two topographic factors and four geologic factors. The developed landslide prediction model has more than $90\%$ of prediction accuracy. Therefore, it is possible to make probabilistic and quantitative prediction of landslide occurrence using the developed model in this study area as well as the previously developed model for metamorphic and granitic rocks.