• International Journal of Fluid Machinery and Systems
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• v.4 no.1
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• pp.133-139
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• 2011

In this paper, Based on Two-dimensional Flow Theory, adopting quasi-orthogonal method and point-by-point integration method to design the impeller of the low specific speed centrifugal pump by code, and using RANS (Reynolds Averaged N-S) Equation with a standard k-${\varepsilon}$ two-equation turbulence model and log-law wall function to solve 3D turbulent flow field in the impeller of the low specific speed pump. An analysis of the influences of the blade profile on velocity distributions, pressure distributions and pump performance and the investigation of the flow regulation pattern in the impeller of the centrifugal pump are presented. And the result shows that this method can be used as a new way in low speed centrifugal pump impeller design.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.17-33
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• 2005

Highway design speed is a very important design element which determines highway design level. When determining highway design speed, one would estimate it utilizing the most likelihood of design speed and vehicle operating speed relationship. Existing operating speed prediction models only include highway geometric characteristics and their impacts on speed, which usually can not consider the impact of highway design speed on surrounding roadway environment and land use pattern. If this happens, excessive highway construction cost and huge environmental impact can occur. In this research project, a new vehicle operating speed prediction model was developed which can reflect the effect of surrounding roadway environment into vehicle speed prediction. The followings are the research findings : Firstly, highway terrain types and land use pattern on national roads were classified and integrated into drivers' visual recognition pattern. This was performed using a data management software. Secondly, the developed highway terrain types and land use pattern were related to vehicle speeds and it was found that there were significant statistical differences among vehicle speed for each different terrain and land use pattern. Thirdly. the General Linear Model analysis was employed to analyze the effects of highway geometric features, terrain types, and land use patterns. For two-lane highway and four-lane highway tested in this research project, it was found that R squares were 0.67 and 0.85, respectively. Additionally an optimal highway design speed range table, based on this research project. was proposed for practical use. This table can be reliably used on South Korean national road design, but discretion is required for applying this table to other types of highways including provincial roads and municipal roads.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.778-779
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.780-781
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• 2005

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.320-328
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• 2011

The down town passage segment which follows in the straight line which follows recently in high speed of the railway and rail construction is increasing. Also according to quality of life improvement of the citizens whom follows in national income increase the resident demand only becomes larger day by day about a environmental creation which is comfortable and house environmental etc. Demand of the citizens is not the problem of today yesterday about like this railway mean of transportation and with the fact that continuously will increase in future. This study is to predict and reduce railway noise from the conventional PSC-beam bridges which passes through urban areas under the government strateges of speed and weight increases of railway. The purpose of this study is to recommend a proper noise prediction method for designing pleasant roadside environments. The railway design including existing line reconstructions should minimize curved alignment to increase train speed to 180~200km/hr under the government's long-term planing such as the 4th Comprehensive National Development Plan (2000~2020), National Intermodal Transportation Plan (2000~2019) and National Railroad Network Establishment Plan (2006~2015), Since the PSC-beam bridges are mainly used for bridge structures urban areas, noise measurements were performed and analyzed to recommend the noise prediction methods for each type and speed of train respectively.

• Journal of Environmental Science International
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• v.31 no.7
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• pp.617-635
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• 2022

The effect of coupled data assimilation (DA) on the meteorological prediction in the west coastal region of Korea was evaluated using a coupled atmosphere-ocean model (e.g., COAWST) in the spring (March 17-26) of 2019. We performed two sets of simulation experiments: (1) with the coupled DA (i.e., COAWST_DA) and (2) without the coupled DA (i.e., COAWST_BASE). Overall, compared with the COAWST_BASE simulation, the COAWST_DA simulation showed good agreement in the spatial and temporal variations of meteorological variables (sea surface temperature, air temperature, wind speed, and relative humidity) with those of the observations. In particular, the effect of the coupled DA on wind speed was greatly improved. This might be primarily due to the prediction improvement of the sea surface temperature resulting from the coupled DA in the study area. In addition, the improvement of meteorological prediction in COAWST_DA simulation was also confirmed by the comparative analysis between SST and other meteorological variables (sea surface wind speed and pressure variation).

• 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 Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• 시스템엔지니어링워크숍
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• s.1
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• pp.60-64
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• 2003

In this study, we have analyzed the cost of korea high-speed railway system. The predicted cost in planning phase and adjustment data to 5th year are collected. Then, predicted cost is compared with adjustment in year/item/system base. We make a project history table for criteria to review project history and research & development activity. We have developed CBS(cost breakdown structure) and allocated adjustment data to them. It is shown that cost prediction related to research & development activity in planning phase is relatively correct.