• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.107-112
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• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2852-2859
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• 2011

Planning and construction of railway for high speed trains up to 400 km/h are recently driven in Korea. High speed train is one of the environment-friendly fastest mass transportation means but its noise generated by rolling, traction and aerodynamic mechanism can cause public complaints of residents nearby railways. To cost-effectively prevent the troublesome noise in a railway planning stage, the rational railway noise prediction method considering the characteristics of trains as well as railway structures should be required but it is difficult to find an authentic method for Korean high speed trains such as KTX and KTX-II. In this study, recent railway noise prediction methods developed by EU countries are introduced and discussed for consulting before setting the framework of our own railway noise prediction model emitted by Korean high speed trains over 250 km/h. Especially, the new Schall 03 model (2006) developed by Germany and IMAGINE model (2007) suggested by an EU framework research project are intensively reviewed. In addition, research items required for the development of our own model are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1770-1782
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• 2011

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for computers or other devices. In other words, BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways consisting of nerves and muscles. This is particularly useful for facilitating communication for people suffering from paralysis. Due to the low bit rate, it takes much more time to translate brain activity into commands. Especially it takes much time to input characters by using BCI-based typewriters. In this paper, we propose a brain-operated typewriter which is accelerated by a language prediction model. The proposed system uses three kinds of strategies to improve the entry speed: word completion, next-syllable prediction, and next word prediction. We found that the entry speed of BCI-based typewriter improved about twice as much through our demonstration which utilized the language prediction model.

• Transactions of Materials Processing
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• v.8 no.6
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• pp.583-590
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• 1999

New forward slip model has been developed for the precise prediction of rolling speed in the hot strip finishing mill. Besides those influential factors such as neutral point, work roll diameter, friction coefficient, bite angle and the thickness at each side of entry and delivery of the rolls, roll torque was specifically taken into account in this study. To consider the effect of width change on forward slip, calibration factors obtained from rolling torque has been added to new prediction model and refining method has also been developed to reduce the speed unbalance between adjacent stands. The application of the new model showed a good agreement in rolling speeds between the predictions and the actual measurements, and the standard deviation of prediction error has also been significantly reduced.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.144-152
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• 2019

According to the National Police Agency, point-to-point speed enforcement system is being installed and operated in 97 sections across the country. It is more effective than other enforcement systems in terms of stabilizing the traffic flow and inhibiting the kangaroo effect. But it is only 5.1% of the total enforcement systems. The National Police Agency is also aware that its operation ratio is very low and it is necessary to expand point-to-point speed enforcement system. Hence, this study aims to provide the expansion basis of the point-to-point speed enforcement operation through analysis of the quantitative effects and development the accident prediction model. Firstly, this study analyzed the effectiveness of point-to-point speed enforcement system. Naive before-after study and comparison group method(C-G Method) were used as methodologies of analyzing the effectiveness. The result of using the naive before-after study was significant. Total accidents, EPDOs and casualty crashes decreased by 42.15%, 70.64% and 45.30% respectively. And average speed and the ratio of exceeding speed limit decreased by 6.92% and 20.50%p respectively. Moreover, using the C-G method total accidents, EPDOs and casualty crashes decreased by 31.35%, 66.62% and 10.04% respectively. And average speed and the ratio of exceeding speed limit decreased by 3.49% and 56.65%p respectively. Secondly, this study developed a prediction model for the probability of casualty crash. It was dependant on factors of traffic volume, ratio of exceeding speed limit, ratio of heavy vehicle, ratio of curve section, and presence of point-to-point speed enforcement. Finally, this study selected the most danger sections to the major highway and evaluated proper installation sections to the recent installation section by applying the accident prediction model. The results of this study are expected to be useful in establishing the installation standards for the point-to-point speed enforcement system.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.19-27
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• 2015

Technologies of wind power generation for development of alternative energy technology have been accumulated over the past 20 years. Wind power generation is environmentally friendly and economical because it uses the wind blowing in nature as energy resource. In order to operate wind power generation efficiently, it is necessary to accurately predict wind speed changing every moment in nature. It is important not only averagely how well to predict wind speed but also to minimize the largest absolute error between real value and prediction value of wind speed. In terms of generation operating plan, minimizing the largest absolute error plays an important role for building flexible generation operating plan because the difference between predicting power and real power causes economic loss. In this paper, we propose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecast model made to analyze the wind speed forecast given by the Meteorological Administration and pattern value for considering seasonal property of wind speed as well as changing trend of past wind speed. The wind speed forecast given by the Meteorological Administration is the forecast in respect to comparatively wide area including wind generation farm. But it contributes considerably to make accuracy of wind speed prediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in more detail, the greater accuracy will be obtained.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.2
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• pp.136-147
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• 2009

The computational prediction method of speed performance for a ship with vortex generators is proposed. The Reynolds averaged Navier-Stokes equation has been solved together with the application of Reynolds stress turbulence model. The computations are carried out under identical conditions of the experimental method, i.e., towing and self-propulsion calculations without and with vortex generators. The speed performance in full scale is obtained through analyzing the computational results in model scale according to the revised model-ship performance analysis method of ITTC'78 with considering the vortex generators into account. The characteristics of resistance, self-propulsion and wake characteristics on the propeller plane are investigated. The proposed computational prediction clearly shows the effect of vortex generators and can be applicable to the design tool for vortex generators.