http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
대구시 기반시설양호 주택지의 주민의식과 환경 개선 방향
조득환,Cho, Deuk-Hwan 한국주거학회 2010 한국주거학회 논문집 Vol.21 No.2
A regional planning is central to addressing various urban problems surrounding the detached housing areas of a metropolitan city of Daegu. The problems are related with decrease in population, socio-economic issues of redevelopment and a sustainable society, the various housing market and housing rights, and a need to explore a desirable alternative to enhance the civil needs when renovating General Residential Zone. The aim of this paper is to look into a possible method of residents led environmental improvement by surveying the residents' consciousness in low-density residential areas. The residents wish to have a residential parking permit program to be set up in place and financial support for old independent houses and finally for lampposts and CCTV to be installed by the administrative assistants. The 75.7 per cent residents who inhabit in the low-density residential areas wish that a residential environmental improvement plan is required and the population at 65.9 per cent could consider a need for the citizens to participate in the residential improvement. 70.3 per cent answered that a consultative group of inhabitants is needed to be built, while 40.5 per cent expressed that they would like to participate in that group in case the group is set up. The possibility of productive residential improvement via resident participation in the low-density residential areas is high, as long as small various environmental improvement projects are created, a resident-consultative group formed, and finally a residential improvement plan is addressed in a comprehensive way.
조득환,Cho, Deuk-Hwan 한국주거학회 2010 한국주거학회 논문집 Vol.21 No.3
The purpose of this study is to estimate the housing demands in order to address a suitable housing policy for a metropolitan city of Daegu in South Korea. Although the population of Daegu declines, a number of households increase since a number of people per households decrease. Currently a household with four people is a main housing type, however it is expected that a household with one or two increase. In 2017, a household with one will be dominant. Estimating housing sizes and their demand, the households below $60\;m^2$ gradually decline while those over $85\;m^2$ is expected to rise. Nevertheless, the demands for the house below $60\;m^2$ in its size increase at 39.2 per cent. Currently a house with $60\;m^2$ is being constructed. In particular, that of $85\;m^2$ gradually increases. The current trends may result in the widening gaps between the household demand and supply of Daegu. Therefore, it is recommended that relevant local authorities and developers should consider providing various house sizes by taking the current housing demand of Daegu into account.
인공지능(AI)을 활용한 공모주 투자여부 및기준 수익률 달성 여부 예측 모델
조득환(Deuk Hwan Cho),류호선(Ho Sun Ryou),정승환(Seung Hwan Jung),오경주(Kyong Joo Oh) 한국데이터정보과학회 2020 한국데이터정보과학회지 Vol.31 No.3
우리나라의 금융시장이 발전함에 따라, 개인의 투자에 관한 관심이 높아지면서 공모주 시장의 참여자도 증가하고 있다. 반면 학계에서는 다른 금융 분야와 비교하면 공모주에 관한 연구는 부족하고, 상장 이후 공모주식의 가격 예측에 관한 연구는 전무한 상황이다. 개인투자자들이 공모주 시장에서 얻을 수 있는 정보는 매우 한정된 상황에서 본 논문은 공개된 데이터로부터 인공지능과 통계 방법론을 통하여 공모주 투자 시의 수익률을 예측하는 데에 유의미한 변수를 발견하고 이를 예측하여 투자자의 의사결정에 도움을 줄 수 있는 결과를 얻었다. 본 논문의 모델에 판별분석, 의사결정나무, 로지스틱 회귀분석, 인공신경망, 유전자 알고리즘의 방법론을 사용하였으며, 변수선정과정, 예측모델에서 각각 최적의 방법론을 사용하여 모델을 구성하였다. As Korea’s financial market develops, the interest in private investment increases and the participants in the IPO (initial public offering) market increase. On the other hand, there is a lack of research on public offerings compared to other financial sectors, and there is no study on the price prediction of public offerings after public listing. Given the limited information available to individual investors in the competition market, this paper found a significant variable in predicting the return on investment in the competition through artificial intelligence and statistical methodology. By predicting the rate of return, we have obtained results that can help investors make decisions. We used the methodology of discriminant analysis, decision tree, logistic regression, artificial neural network, and genetic algorithm in our model. The model was constructed using the optimal methodology in the variable selection process and the predictive model.