• 한국수자원학회논문집
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• 제52권2호
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• pp.125-132
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• 2019

본 연구에서는 기존의 정량적인 강수량 정보를 제공하는 방식에서 벗어나 호우발생에 따른 생활환경의 변화에 끼치는 영향을 고려한 호우영향예보서비스의 필요성을 기반으로 호우위험영향도 평가가 가능한 호우재해 위험영향 매트릭스를 개발하고, 이를 통해 호우위험영향을 평가하는 방법을 제시하였다. 사당동 일대를 대상으로 실제 발생 호우사상(2011년 7월 27일)을 적용하였으며, 호우에 의한 침수로 영향을 받는 대상별(사람, 교통, 시설) 호우위험영향평가를 수행하였다. 이를 위해 1 km 격자기반으로 호우위험정도(Impact Level)를 산정하고, 침수심 결과를 조합하여 격자기반의 잠재호우위험영향(Potential Risk Impact)을 산정하였다. 여기에 강우발생가능성 Likelihood와의 조합을 통해 호우영향예보가 가능한 호우위험영향(Heavy Rainfall Risk Impact) 값을 산정하여 사당동 지역의 호우영향정도를 격자기반으로 4개의 등급으로 분석, 제시하였다.

• 대한전기학회논문지:전력기술부문A
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• 제54권3호
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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• pp.2356-2363
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• 2015

Localization of lightning strike point (LSP) in the forest is modeled to mitigate the forest fire damage. Though forest fire ignited by lightning rarely happens, its damage on the forest is grievousness. Therefore, predicting accurate location of LSP becomes crucial in order to control the forest fire. In this paper, we proposed a new hybrid localization algorithm by combining the received signal strength (RSS) and the received signal strength ratio (RSSR) to improve the accuracy by mitigating the environmental effect of lightning strike location in the forest. The proposed hybrid algorithm employs antenna theory (AT) model of cloud-to-ground (CG) return stroke lightning channel to forecast the location of the lightning strike. The obtained results show that the proposed hybrid algorithm achieves better location accuracy compared to the existing RSS method for predicting the lightning strike location considering additive white Gaussian noise (AWGN) environment.

• 자원ㆍ환경경제연구
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• 제15권5호
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• pp.799-826
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• 2006

본 연구에서는 시계열분석기법과 그래프 이론을 활용하여 8개의 북미천연가스 현물시장간의 관계를 분석하였다. 벡터오차수정모형과 탐욕동급검색 알고리즘(Greedy Equivalence Search Algorithm)을 활용한 그래프 이론을 통해 시장간의 관계를 분석한 결과, 가격발견과정은 초과수요지역에서 발생하여 초과공급지역으로 진행되는 것으로 나타났다. 북미 천연가스 현물시장 중에서 시카고로 대표되는 미국의 중서부지역이 가격발견과정에 있어 가장 중요한 시장인 것으로 나타났으며 미국 동부지역에 있어 펜실바니아의 Ellisburg-Leidy Hub이, 그리고 미국서부지역에 있어서는 Malin Hub이 가격발견과정에 있어 중요한 시장인 것으로 나타났다.

• 한국정보통신학회논문지
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• 제14권1호
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• pp.1-8
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• 2010

u-Gov가 추구하는 진정한 대국민 행정서비스를 위해 현재 정부기관의 다양한 이해관계로 각기 다르게 제공하는 구조적, 기술적 요소 정비가 필요하다. 그러기 위해서는 정부 주도로 통합된 정보를 관리 제공하는 협업적 조직구조와 컨버전스 기술의 구도로 구성되어야 한다. 이것은 UT 기반의 위치 확인 서비스로 상호연결성과 상호작용을 증진할 수 있는 서비스를 확산하여 복지사회가 추구하는 역할을 추진해야 한다. 따라서 본 논문에서는 새로운 UT 기반의 응용서비스 예측모델 설계를 위한 UT기반의 요구사항을 도출하여 응용서비스 기본구도를 구현 하였다.

• Smart Structures and Systems
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• 제26권1호
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Asian Journal of Innovation and Policy
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• 제9권2호
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• pp.133-144
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• 2020

Korean economy has experienced a very rapid growth largely due to the change of the innovation system since the last half century. The recent outbreak of COVID-19 impacts the global economy as well as Korea's innovation system. In order to understand the influence of the shock to the Korean technological system, we have forecast the future of the system combining qualitative and quantitative techniques such as expert panel, cross impact analysis, and scenario planning. According to the results, we have identified 39 driving forces influencing the change of Korea's technological innovation system. Four scenarios have been suggested based on the predetermined factors and core uncertainties. In other words, uncertainties of emergence of the regions and global value chains generate four scenarios: regional growth, unstable hope, returning to the past, and regional conflicts. The 'regional growth' scenario is regarded as the most preferable, whereas the 'regional conflicts' scenario is unavoidable. In conclusion, we put forward some policy implications to boost the regional innovation system by exploiting the weakened global value chains in order to move on to the most preferable scenario away from the return to the past regime.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.552-555
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• 2016

기존의 거울이라고 하면 단순히 자신의 모습을 보는 용도로 쓰였다. 하지만 현재 상용화 되고 있는 "Mirror Display"는 미용실의 전면 거울, 대형 백화점의 쇼핑 코너와 화장품 코너 등에 설치 되어 있다. 추가로 거울에 광고와 특정 서비스 등으로 부가적인 수익을 창출해 나가고 있다. 본 논문에서는 "Mirror Display"를 소형화하여 가정집에서 사용자의 설정에 따라 일기 예보, 캘린더, 시간, 교통정보, 중요 뉴스 등을 제공한다. 또한 홀로그램 기술을 접목하여 3D 출력방식으로 필요한 정보를 보여준다는 것은 거울의 본질을 극대화 할 수 있을 것이다. 이런 사용자 만족도를 상승 시킬 "Hologram Mirror Display"를 구현한다.

• The Journal of Asian Finance, Economics and Business
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• 제9권7호
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.