• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.71-78
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• 2016

This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.

• Journal of Environmental Science International
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• v.26 no.10
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• pp.1135-1146
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• 2017

At riverbank filtration sites, groundwater levels of alluvial aquifers near rivers are sensitive to variation in river discharge and pumping quantities. In this study, the groundwater level fluctuation, pumping quantity, and streamflow rate at the site of a riverbank filtration plant, which produces drinking water, in the lower Nakdong River basin, South Korea were interrelated. The relationship between drawdown ratio and river discharge was very strong with a correlation coefficient of 0.96, showing a greater drawdown ratio in the wet season than in the dry season. Autocorrelation and cross-correlation were carried out to characterize groundwater level fluctuation. Autoregressive model analysis of groundwater water level fluctuation led to efficient estimation and prediction of pumping for riverbank filtration in relation to river discharge rates, using simple inputs of river discharge and pumping data, without the need for numerical models that require data regarding several aquifer properties and hydrologic parameters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1047-1056
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• 2007

Basically, an ultrasonic imaging system has two fundamental imaging modes available. One is the B-mode imaging modality which provides an image of reflection coefficient, and the other is the Doppler color flow mode that maps blood flow inside the human heart and blood vessels. This paper presents a new method of detecting and compensating for aliasing that occurs when the Doppler frequency exceeds one-half of the pulse-repetition frequency (PRF). Its validity is shown by computer simulation. The new method not only extends the measurable Doppler frequency, but also helps to reduce the effect of noise. The results show that the aliasing can be compensated for correctly fur signal-to-noise ratios down to 20 dB.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.5
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• pp.1023-1031
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• 1989

An experimental study is conducted to investigate the turbulent wake flow around tubes in vertical single row tube banks. All measurements are performed at Reynolds number(Re$_{max}$) 4.2*10$_{3}$ - 2.5*10$_{4}$ with varying tube spacings from the wide pitch ratio(H/D=3.07) to the very narrow one(H/D=1.23). Flow patterns are visualized using the smoke-wire method. Mean static pressures, velocity components, and various statistical quantities of turbulence are obtained by the computer on-line technique. In the case of wide tube spacings, the near wakes of tube show similar trends to those of a single tube, and their flow indicats an anisotropic turbulence. However, as the pitch ratio decreases, wide and narrow wakes appear alternately behind adjacent tubes due to the deflected flow. Also, in the case of H/D .leq. 1.54, Karman vortex is not formed at the side of relatively wide wake.e.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.767-780
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• 2021

The purpose of this research is to analyze the effect of financial elements (asset growth, liability growth, equity growth, revenue growth, and profit growth) on stock price performance and to analyze the growth of Corporate Social Responsibility (CSR) costs as a moderating effect. The technique analysis used is regression analysis. Samples in this analysis are manufacturing firms listed on the Indonesian Stock Exchange (IDX) for the period 2014-2018. The use of regression models for hypothesis testing must fulfill several applicable assumptions such as Normality Test, Heteroscedasticity Test, Multicollinearity Test, Autocorrelation Test, Model Fit Test, Determination Coefficient Test, and Hypothesis Test. Data analysis used two research models, namely model 1 and model 2. Model 1 is without the moderating variable, and model 2 is with the moderating variable, that is, CSR cost growth. Based on the result of the regression analysis, it can be inferred that the asset, revenue, and profit growth have a positive impact on stock price results. Liabilities and equity growth do not affect stock price performance. Operating expense growth has a significant effect on price performance. CSR cost growth can moderate the effect of growth in financial statement elements on stock price performance but is not significant.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.416-416
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• 2022

RCP(Representative concentrate pathways) 기후변화 시나리오에서는 2100년까지 연강 강수량과 강우지속시간이 지속적으로 증가할 것으로 예측하고 있다. 이에 도시지역의 하천 범람 및 내수 침수 예방을 위해 방재성늑목표의 상향조정과 방재 인프라의 설계년수 재조정 등에 대한 논의가 진행되고 있다. 하지만 수재해 예방을 위한 강우설계기준에서는 무강우 지속시간의 설정을 확보할 수 있는 최대 규모의 모집단을 통해 산정하는 방식으로 제시되고 있어 최근 기후변화로 인한 강우특성의 변화가 반영되기에 어려움이 있다. 본 연구에서는 부산광역시 강우자료의 모집단을 10년, 20년, 30년으로 구분하여 자기상관계수(Autocorrelation coefficient, AC) 분석, 변동계수(Coefficient of variation, CV) 분석, 연평균 강우사상 발생 개수(Average annual number of rainfall event, NRE) 분석 방법을 적용하여 비교 분석을 수행하였다. 그 결과, 자기상관계수 분석 방법과 변동계수 분석 방법에서는 모집단의 규모가 클수록 튀는 자료를 상쇄하여 안정적인 결과를 나타내는 반면, 연평균 강우사상 발생 개수를 확인 하였을 때는 대상기간에 큰 영향을 받지 않는 것으로 확인되었다. 또한, 모집단의 규모가 클수록 무강우 지속시간이 길어지는 것으로 분석되어 기후변화로 인한 최근의 강우특성을 반영하기 위해서는 모집단의 규모를 크게 잡는 것이 효과적이지 않을 수 있다는 점을 확인하였다. 이는 강우자료 분석 시 분석방법 및 모집단의 규모를 달리하여 무강우 지속시간을 산정해보고, 설계 목적에 따라 적정한 의사결정이 필요함을 시사한다.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.169-186
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• 2018

The social influence of the elderly population will accelerate in a rapidly aging society. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran's I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.68-79
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• 2021

The recent occurrence of vacant houses in urban areas is a remarkable social problem. One of the physical declines, the occurrence of vacant houses, accelerates various social and economic declines, such as a decline in population and a slump in the commercial district. Vacant houses have regional characteristics and spatial influence, and it is necessary to approach them locally in order to grasp the exact status of vacant houses. Therefore, in this study, the effect of urban decline on the occurrence of vacant homes was examined by region using global Moran's I and Geographic Weighted Regression(GWR) model. As a result of the analysis, there were spatial autocorrelation and heterogeneity in the occurrence of vacant houses in each eup·myeon·dong, Busan metropolitan city. In addition, there is a difference in the influence of each variable of urban decline on the occurrence of vacant houses, and even the same variable of urban decline has different effects on the occurrence of vacant houses in different regions. Therefore, it is expected that a more efficient vacant home management plan can be presented if the GWR model is used to analyze the coefficient values differentiated by region and categorize the occurrence of vacant houses.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.