• Journal of Energy Engineering
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• v.24 no.1
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• pp.69-77
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• 2015

The PMV regression analysis was conducted for this model based on a database of the PMV variables. PMV regression model simplification was completed through sensitivity and data analysis. The simplified PMV regression model's and Fanger PMV model was confirmed through MAE and RMSE. And the EMS in EnergyPlus was used to establish a simplified PMV regression analysis-based thermal comfort control. Also, the thermal comfort controls based on simplified PMV model and the Fanger PMV model were applied to the building model, it was confirmed that both controls met the thermal comfort range in more than 90% of cases during the air conditioning period.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.292-292
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• 2011

전라북도 장수군 계북면 양악리 토옥동계곡의 초입에 위치한 양악호는 농업용 저수지로 관개수로를 통하여 약 $3.94\;km^2$의 몽리면적에 용수를 공급한다. 양악호는 1985년 건설공사를 시작하여 1991년에 준공되었다. 관개수로를 통해 공급된물은 다양한 경로를 통해 양악천으로 회귀되며, 회귀수량은 양악호로부터 약 8 km 하류에 위치한 양악1교에서 관측된다. 연구 대상지역은 행정구역상 전라북도 장수군, 무주군과 진안군이 경계를 이루며, 유역의 대표적인 거주지로는 당저, 파곡, 외림 그리고 주고마을이 있다. 본 논문에서는 용담댐 상류에 위치한 양악천 유역을 대상으로 관개수로를 통한 유입량, 하류하천에서의 배수량, 양악천 상류 양악호에서의 강수량, 농지에서의 경작현황 및 몽리면적 등 실측자료를 활용하여 농업용수 회귀율을 분석하였다. 농지의 면적은 현장에서의 직접조사 및 위성영상을 활용한 간접조사를 통하여 수혜지역으로부터 '인삼 재배 면적'을 제외하는 방법으로 산출하였다. 관개수로를 통한 용수공급이 없는 시기의 하천유지유량은 용수의 유입이 없는 1월부터 4월초까지의 자료를 분석하여 산정하였다. 하류하천에서의 배수량은 홍수기 전 후로 구분하여 조사하였다. 양악천 유역내 강우자료는 양악호 취수탑 입구에 설치된 우량계를 이용하여 측정하였으며, 원촌교 인근의 계북 우량관측소 자료를 검증용으로 활용하였다. 양악천 유역은 유역외 용수공급을 위하여 양수장을 운영하고 있으며, 생활용수 공급 시스템은 장수군, 무주군과 진안군 등 행정구역별로 각각 다르다. 그러므로 회귀율 산정시 양수장 운영일지 및 마을 일부의 정화조 사용량을 참고하였다. 양악천유역에서는 4월 말부터 9월 말까지 약 5개월간 용수공급이 이루어졌다. 초 여름인 6월 28일 부터 7월 03일까지의 회귀율이 43.66 %로 최저로 나타났으며, 4월 30일부터 5월 16일까지는 67.77 %로 제일 높게 분석되었다. 분석 결과 이 지역은 6월 말에서 7월 초순을 제외한 대부분의 기간에는 약 55 ~ 60 % 사이의 비교적 높은 회귀율을 보였다. 2010년 양악천 유역 전구간의 평균 회귀율은 56 %이며, 무강우시 회귀율 분석결과는 다음과 같다.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.81-89
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• 1995

In this paper, we report the continuous speech recognition system using the continuous HMM with discrete duration control and the regression coefficients. Also, we do recognition experiment using One Pass DP method(for 25 sentences of robot control commands) with finite state automata context control. In the experiment for 4 connected spoken digits, the recognition rates are $93.8\%$ when the discrete duration control and the regression coefficients are included, and $80.7\%$ when they are not included. In the experiment for 25 sentences of the robot control commands, the recognition rate are $90.9\%$ when FSN is not included and $98.4\%$ when FSN is included.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.351-354
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• 2018

본 연구에서는 일 증발접시 증발량 모델링을 위한 다변량 적응 회귀 스플라인 (multivariate adaptive regression splines, MARS) 모델의 성능을 평가하였다. 모델 입력변수 집합은 부산 관측소 (기상청)로부터 수집된 기상자료를 활용하여 증발접시 증발량과의 상관성이 높은 변수들의 조합으로 구성되었으며, 일사량, 일조시간, 평균지상온도, 최대기온의 조합으로 구성된 세 가지 입력집합이 결정되었다. MARS 모델의 성능은 네 가지의 모델성능평가지표를 활용하여 정량적으로 산출되었으며, 그 결과를 인공신경망 (artificial neural network, ANN) 모델과 비교하였다. 입력변수로서 일사량 및 일조시간을 가지는 Set 1의 경우 MARS1 모델이 ANN1 모델보다 우수한 성능을 나타내었으며, Set 2 (일사량, 일조시간, 평균지상온도)의 경우 ANN2 모델, Set 3 (일사량, 일조시간, 평균지상온도, 최대기온)의 경우 MARS3 모델이 상대적으로 우수한 모델 성능을 나타내었다. 모든 분석 모델들을 비교하였을 때, MARS3, ANN2, ANN3, MARS2, MARS1, ANN1 모델의 순서로 우수한 모델 성능을 나타내었으며, 특히 MARS3 모델은 CE = 0.790, $r^2=0.800$, RMSE = 0.762, MAE = 0.587로서 가장 우수한 일 증발접시 증발량 모델링 성능을 나타내었다. 따라서 본 연구에서 적용한 MARS 모델은 지상관측 기상자료를 활용한 일 증발접시 증발량 모델링에서 효과적인 대안이 될 수 있을 것으로 판단된다.

• Economic and Environmental Geology
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• v.39 no.3 s.178
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• pp.255-268
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• 2006

Recent 22-year (1981-2002) meteorological data of 58 Korea Meteorological Adminstration (KMA) station were analyzed to investigate spatial and temporal variation of surface air temperature (SAT) and ground surface temperature (GST) in Korea. Based on the KMA data, multiple linear regression (MLR) models, having two regression variables of latitude and altitude, were presented to predict mean surface air temperature (MSAT) and mean ground surface temperature (MGST). Both models showed a high accuracy of prediction with $R^2$ values of 0.92 and 0.94, respectively. The prediction of MGST is particularly important in the areas of geothermal energy utilization, since it is a critical parameter of input for designing the ground source heat pump system. Thus, due to a good performance of the MGST regression model, it is expected that the model can be a useful tool for preliminary evaluation of MGST in the area of interest with no reliable data. By a simple linear regression, temporal variation of SAT was analyzed to examine long-term increase of SAT due to the global warming and the urbanization effect. All of the KMA stations except one showed an increasing trend of SAT with a range between 0.005 and $0.088^{\circ}C/yr$ and a mean of $0.043^{\circ}C/yr$. In terms of meteorological factors controlling variation of GST, the effects of solar radiation, terrestrial radiation, precipitation, and snow cover were also discussed based on quantitative and qualitative analysis of the meteorological data.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.475-487
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• 2015

This study measures the managerial efficiency of Korea's 14 public enterprises using bootstrap DEA in 2013. In addition, it examines the factors that affect on the bootstrap bias-corrected efficiency using truncated regression analysis. The results and implications of this study are as follows. First, using bootstrap DEA model analysis, the results showed that the mean technical efficiency was 0.3182, the mean pure technical efficiency was 0.4994 and the mean scale efficiency was 0.6585. The main cause of technical inefficiency was due to pure technical inefficiency. Second, rank test between technical efficiency of general DEA model and bootstrap DEA model was no significant difference under CRS and VRS assumption. Third, the main cause of the inefficiency in 11 DMUs among 14 DMUs were mainly due to the pure technology and three DMUs were because of the scale efficiency. Finally, in the truncated regression analysis, cost of labor, profit, sales, return of equity, and the number of employees appeared as factors affecting the scale efficiency at the 10% significance level.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.541-550
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• 2007

One of main aspects of time series analysis is to forecast future values of series based on values up to a given time. The prediction interval for future values is usually obtained under the normality assumption. When the assumption is seriously violated, a transformation of data may permit the valid use of the normal theory. We investigate the prediction problem for future values in the original scale when transformations are applied in ARMA models. In this paper, we introduce the methodology based on Yeo-Johnson transformation to solve the problem of skewed data whose modelling is relatively difficult in the analysis of time series. Simulation studies show that the coverage probabilities of proposed intervals are closer to the nominal level than those of usual intervals.

• Environmental and Resource Economics Review
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• v.27 no.4
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• pp.613-637
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• 2018

Fuel cell power plant which has advantages as a distributed generation is influenced by high cost of investment and uncertainty of electricity price. This study suggests the model of real options which considers the irreversibility of investment in the fuel cell plant and the uncertainty of electricity price. Most models of real options assume the geometric Brownian motion for convenience, but this study develops the model for the feasibility analysis considering the mean reverting process of electricity price, with the closed form solution on the value of investment option. The result of the empirical analysis considering the data related to the fuel cell generation with the scale of 20MW and the domestic RPS circumstance represents that the investment is feasible without the uncertainty, and is not feasible with the uncertainty. This result implies that the political support as well as the improvement of profit system including revenue and cost are necessary for the activation of the fuel cell power plant.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.