• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.406-414
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• 2015

Existing relative researches for traffic were studied under favorable weather or excluding impact of weather. This study present traffic volume variation according to rainfall intensity in national highway provincial road and rainfall-factor. Continuous traffic count section match AWS after selecting to analyze provincial road 256 section. Weekdays ADT(Average Daily Traffic) and rainfall-factor are influenced by rainfall a little because of business travel. But non-weekdays ADT and rainfall-factor are influenced much more than weekdays because of leisure travel. Estimated AADT(Annual Average Daily Traffic) by adjusting rainfall-factor is lower MAPE than non-adjusting rainfall factor. So, rainfall have to be considered when estimating AADT. ADT decrease according to rainfall intensity, continuous studies considered rainfall intensity are needed when road design and operation.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1779-1786
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• 2014

Artificial Intelligence-based techniques have been applied to problems where mathematical relations can not be presented due to complicatedness of the physical process. A representative example in hydraulics is the local scour around bridge piers. This study presents a GEP model for predicting the local scour around bridge piers. The model is trained by 64 laboratory data to build the regression equation, and the constructed model is verified against 33 laboratory data. Comparisons between the models with dimensional and normalized variables reveals that the GEP model with dimensional variables predicts better. The proposed model is now applied to two field datasets. It is found that the MAPE of the scour depths predicted by the GEP model increases compared with the predictions of local scours in laboratory scale. In addition, the model performance increases significantly when the model is trained by the field dataset rather than the laboratory dataset. The findings suggest that apart from the ANN model, GEP model is a sound and reliable model for predicting local scour depth.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.159-168
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• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.6-9
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• 2003

In this paper, we studied on the difference of speaker adaptation according to the phoneme classification for Korean Speech recognition. In order to study of speech adaptation according to the weight of difference of phoneme as recognition unit, we used SCHMM as recognition system. And Speaker adaptation method used in this paper was MAPE(Maximum A Posteriori Probability Estimation), Linear Spectral Estimation. In order to evaluate the performance of these methods, we used 10 Korean isolated numbers as the experimental data. It is possible for the first and the second methods to be carried out unsupervised learning and used in on-line system. And the first method was shown performance improvement over the second method, and hybrid adaptation showed the better recognition results than those which performed each method. And the result of Speaker adaptation using the variable weight according to the phoneme had better than the result using fixed weight.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.21-28
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• 2007

We suggest a new spatial linear interpolation method to substitute linear interpolation method which widely used in transportation engineering to impute the missing daily traffic volume. We layout daily traffic volume which is time series data over the virtual lattice space to consider the spatial correlation. We used Moran Index to evaluate the spatial correlations among daily traffic volume in same week and same date traffic volume by week considering the circularity of daily traffic volume. For real application, we used daily traffic volume on November, 2004 provided by Korea Institute of Construction Technology(KICT) and transformed daily traffic volume to 4 times 7 virtual lattice space to reflect the spatial correlation. Finally we showed that the spatial linear interpolation method has good performance for missing data imputation based on MAPE, RMSE, and Theil's U criteria.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.421-421
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• 2018

효율적인 수자원 관리를 위하여 전국유역조사, 수자원 장기종합계획 등 다양한 사업이 수행되고 있으며, 이를 위하여 유출해석은 필수적인 항목이라 할 수 있다. 유출해석을 위하여 수문모형 또는 관측소의 유량자료가 활용되고 있으나, 이는 기존에 관측된 유량자료를 바탕으로 구축된 수위-유량관계 곡선식(Rating-curve)을 활용하여 재생산된 자료라 할 수 있다. 즉, 수위자료는 매시간 관측소에서 측정이 되지만, 유량자료의 경우 측정이 어려울 뿐만 아니라 변동성 및 불확실성이 크기 때문에 시계열 수위를 곡신식을 통해 유량으로 변환하여 활용하고 있다. 이와 같이 수위-유량관계 곡선식의 정확성이 수문자료 생산에 핵심 요소임에도 불구하고 이에 대한 연구는 제한적이며, 특히 홍수터 등의 영향을 고려하여 분할된 곡선의 천이점 접합시 곡선식의 정확도 향상을 위한 연구도 드문 편이다. 따라서 본 연구에서는 구간 분할된 곡선의 최적 천이점 선정을 위하여 Particle Swarm Optimization(PSO)기법을 활용하였으며, 총 5개 구간까지 구간별 목적함수로 RMSE, RSR, 결정계수 적용시 특성변화에 대한 연구를 수행하였다. 구간에 대하여 절대적인 오차를 산정하는 RMSE를 활용하는 경우 저수위 부분에 대한 오차가 증가하는 것을 확인할 수 있었으며, 상대적인 오차인 RSR, 결정계수를 활용하는 경우 전체 구간에 대한 오차를 보완할 수 있는 것으로 나타났다. PSO기법을 활용하여 도출된 곡선식에 대해서는 구간 및 전체구간에 대한 오차(RMSE, 결정계수, RSR, MAPE)를 활용하여 불확실성을 검토할 수 있도록 하였고, 잔차분석을 통한 이상치 및 회귀곡선에 대한 정규성 검토를 수행할 수 있는 툴을 개발하였다. 레이팅 커브를 작성하는데 있어 최적화 알고리즘을 활용하여 구간분할시 천이점 선정의 자동화로 천이점 선정에 소요되는 시간을 대폭 감축할 수 있을 뿐만 아니라, 구간별 오차를 종합적으로 고려하여 우수한 품질의 레이팅 커브를 도출할 수 있는 기반을 구축하였다.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.65-76
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• 2016

Self-adaptive software is software that adapts by itself to system requirements about the recognized problems without stopping the software cycle. In order to reduce the unnecessary adaptation in the system having the critical points, we propose proactive approach which can predict the future operation after a critical point. In this paper, we predict the future operation after a critical point using a hybrid model to deal with the characteristics of the observed data with the linear and non-linear pattern. The operation of the prediction method is determined on a timing decision indicator based on the prediction accuracy. The two main points of contributions of this paper are to reduce uncertainty about the future operation by predicting the situation after a critical point using hybrid model and to reduce unnecessary adaptation implementation by deciding a timing based on a timing decision indicator.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.7-12
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• 2019

The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.