• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.171-174
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• 2009

비선형 동력학 시스템으로 구성된 전력 수요의 시계열 데이터를 예측하기 위해 적용된 신경망 및 퍼지 적응 알고리즘 등은 예측오차가 상대적으로 크게 나타났다. 이는 전력수요 시계열 데이터가 가지고 있는 카오스적인 성질에 기인하며 이중 초기값에 민감한 의존성은 장기적인 예측을 더욱더 어렵게 하는 요인으로 작용한다. 전력수요 시계열 데이터가 가지고 있는 카오스적인 성질을 정량 및 정성적인 방식으로 분석을 수행하고, 시스템 동력학적 특성의 정량분석에 이용되는 Lyapunov 지수를 이용하여 어트랙터 재구성, 다차원 카오스 시계열 데이터를 예측하는 방식으로 수요예측 시뮬레이션을 수행하고 결과를 비교 평가하여 기존 제안방식보다 실용적이며 효과적임을 확인한다.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2006년도 하계학술발표대회 논문집
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• pp.789-794
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• 2006

The potential impacts of climate change on heating and cooling energy demand were investigated by means of transient building energy simulations and hourly weather data scenarios for Inchon. Future trends for the 21 st century was assessed based oil climate change scenarios with 7 global climate models(GCMs), We constructed hourly weather data from monthly temperatures and total incident solar radiation ($W/m^2$) and then simulated heating and cooling load by Trnsys 16 for Inchon. For 2004-2080, the selected scenarios made by IPCC foresaw a $3.7-5.8^{\circ}C$rise in mean annual air temperature. In 2004-2080, the annual cooling load for a apartment with internal heat gains increased by 75-165% while the heating load fell by 52-71%. Our analysis showed widely varying shifts in future energy demand depending on the season. Heating costs will significantly decrease whereas more expensive electrical energy will be needed of air conditioning during the summer.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.627-640
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• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.

• 한국인공지능학회지
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• 제10권1호
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.

• Structural Engineering and Mechanics
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• 제60권6호
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• pp.1021-1038
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• 2016

Considerable part of reinforced concrete building has suffered from destructive earthquakes in Turkey. This situation makes necessary to determine nonlinear behavior and seismic performance of existing RC buildings. Inelastic response of buildings to static and dynamic actions should be determined by considering both flexural plastic hinges and brittle shear hinges. However, shear capacities of members are generally neglected due to time saving issues and convergence problems and only flexural response of buildings are considered in performance assessment studies. On the other hand, recent earthquakes showed that the performance of older buildings is mostly controlled by shear capacities of members rather than flexure. Demand estimation is as important as capacity estimation for the reliable performance prediction in existing RC buildings. Demand estimation methods based on strength reduction factor (R), ductility (${\mu}$), and period (T) parameters ($R-{\mu}-T$) and damping dependent demand formulations are widely discussed and studied by various researchers. Adopted form of $R-{\mu}-T$ based demand estimation method presented in Eurocode 8 and Turkish Earthquake Code-2007 and damping based Capacity Spectrum Method presented in ATC-40 document are the typical examples of these two different approaches. In this study, eight different existing RC buildings, constructed before and after Turkish Earthquake Code-1998, are selected. Capacity curves of selected buildings are obtained with and without considering the brittle shear capacities of members. Seismic drift demands occurred in buildings are determined by using both $R-{\mu}-T$ and damping based estimation methods. Results have shown that not only capacity estimation methods but also demand estimation approaches affect the performance of buildings notably. It is concluded that including or excluding the shear capacity of members in nonlinear modeling of existing buildings significantly affects the strength and deformation capacities and hence the performance of buildings.

• 대한토목학회논문집
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• 제39권2호
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• pp.351-359
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• 2019

날로 악화되고 있는 주차문제의 개선을 위하여 지자체에서는 관련법에 의거하여 주차장 수급실태조사를 주기적으로 실시하고 있다. 하지만 기존 평가 방법에서는 조사방법의 한계로 인해 주차공급 및 규제에 의해 수요가 억제된 상태 하에서 발생하는 주차수요를 조사하고 있어, 주차 수급문제 진단의 핵심이 될 수 있는 대상지역 내 주차 이용수요의 발생 총량을 정확하게 파악할 수 없는 실정이다. 또한 현황연도만을 대상으로 분석을 실시하고 있어, 장래 주차 문제에 대한 예상과 분석에는 한계를 보이고 있다. 본 연구에서는 이러한 문제를 개선하기 위하여 기존 평가 방법과 차별성을 갖는 개선된 주차장 수급실태 평가 방법을 제시하고, 기존 평가 방법을 적용한 분석 결과와의 상호 비교를 통하여 개선된 평가 방법이 주차문제 개선을 위한 장기적 관점의 주차정책 수립에 유용할 수 있음을 보여준다.

• 품질경영학회지
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• 제50권3호
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• 미래기술융합논문지
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• 제2권3호
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• pp.59-66
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• 2023

본 디지털 전환에서 가장 중요한 것은 SW 기술이다. 그러나 많은 기업이 SW 기술 및 인력 확보에 어려움을 겪고 있다. 특히 SW 인력 부족은 더욱 증가할 것이라 보고되고 있다. 정부는 SW 인력 수급정책을 해소하기 위해 인력양성 정책과 많은 지원사업을 수행하고 있지만, 이러한 정책이 효과적으로 수립되기 위해서는 소프트웨어 산업의 수요와 공급에 대한 정확한 예측이 필수적이다. 따라서 본 연구는 소프트웨어 산업의 수급 불균형을 해소하기 위해 동태적 구조 분석을 수행할 수 있는 시스템 다이내믹스 방법론을 활용하여 시뮬레이션을 개발하였다. 시스템 다이내믹스는 소프트웨어 산업의 인력 수급 불균형 현상에 대해 동태적인 시각에서 그 원인과 정책대안을 찾기에 적절하다. 세부적으로 미국의 노동통계국의(U.S. Bureau of Labor Statistics, BLS) 방법론을 사용하여 적용하여 소프트웨어 산업의 인력 수요 및 공급 예측 모델을 개발하였고, 시나리오 분석을 수행하여 정책적 시사점을 도출하였다.

• Ocean and Polar Research
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• 제43권2호
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• pp.53-63
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• 2021

Changes in a marine environment have a broad socioeconomic implication on fisheries and their relevant industries so that there has been a growing demand for the medium-range (months to years) prediction of the marine environment Using a medium-range ocean prediction model (Ocean Mid-range prediction System, OMIDAS) for the northwest Pacific, this study attempted to assess seasonal difference in the mid-range predictability of the sea surface temperature (SST), focusing on the Korea seas characterized as a complex marine system. A three-month re-forecast experiment was conducted for each of the four seasons in 2016 starting from January, forced with Climate Forecast System version 2 (CFSv2) forecast data. The assessment using relative root-mean-square-error was taken for the last month SST of each experiment. Compared to the CFSv2, the OMIDAS revealed a better prediction skill for the Korea seas SST, particularly in the Yellow sea mainly due to a more realistic representation of the topography and current systems. Seasonally, the OMIDAS showed better predictability in the warm seasons (spring and summer) than in the cold seasons (fall and winter), suggesting seasonal dependency in predictability of the Korea seas. In addition, the mid-range predictability for the Korea seas significantly varies depending on regions: the predictability was higher in the East Sea than in the Yellow Sea. The improvement in the seasonal predictability for the Korea seas by OMIDAS highlights the importance of a regional ocean modeling system for a medium-range marine prediction.

• 한국콘텐츠학회논문지
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• 제20권3호
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• pp.209-222
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• 2020

본 논문은 주거의 미래변화를 전망하고 대응방안을 연구하기 위한 목적으로, 델파이 기법을 통해 주거의 미래를 예측하였다. 먼저, 미래주거 예측 시기를 구분하고, 대상을 주거형태, 주거공간, 주거수요, 건축기술변화로 설정하였으며, 대상에 미치는 Impact Factor를 조사, 분석 하였다. 결과는 ① 사회적, 가치적 관점이 주거형태, 공간, 수요변화에 미치는 영향이 클 것이며, 정치적 관점의 영향은 적을 것으로 예측하였다. ② 형태적 측면에서 고층빌딩에 다운사이징 주택 수요 증가, 기술적 측면에서 빅데이터를 활용한 원격의료지원 서비스와 홈케어 실현 가능성이 높게 예측하였다. 그에 따라 ③ IoT가 미래주거변화에 미치는 영향이 클 것으로 예측하였으며, ④ 공유경제에 의한 코하우징, 그와 관련된 법 제정, 고층, 고밀 주택 보급으로 유지관리를 위한 서비스, 거주자 맞춤형 주거지원 혹은 임대차 시장 선진화, 건축기술 발전으로 미래형 주거확산 등이 전망된다.