• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.2
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• pp.133-142
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• 2008

Five intensive measurements of particulate PAHs were made at a roadside in Seoul from May 2005 to June 2006. The average concentration of particulate PAHs was $15.1{\pm}10.6ng\;m^{-3}$. The high concentrations of particulate fluoranthene and pyrene were observed in November 2005 due to the influence of the lower ambient temperature. Compared to the previous results at tunnel and ambient sites in Seoul, larger fraction of the high molecular PAH compounds which consist with five or six benzene rings, was observed at a roadside. This might indicate high influence of vehicle emission at a roadside. The distribution of diagnostic ratios for specific PAH compounds indicated that the influence of vehicular emission, especially diesel vehicular emission seems to be high at a roadside.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2839-2844
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• 2008

A PEMFC(proton exchange membrane fuel cell) is a good candidate for residential power generation to be cope with the shortage of fossil fuel and green house gas emission. The attractive benefit of the PEMFC is to produce electric power as well as hot water for home usage. Typically, thermal management of vehicular PEMFC is to reject the heat from the PEMFC to the ambient air. Different from that, the thermal management of PEMFC for RPG is to utilize the heat of PEMFC so that the PEMFC can be operated at its optimal efficiency. In this study, dynamic thermal management system is modeled to understand the response of the thermal management system during dynamic operation. The thermal management system of PEMFC for RPGFC is composed of two cooling circuits, one for controling the fuel cell temperature and the other for heating up the water for home usage. Dynamic responses and operating strategies of the PEMFC system are investigated during load changes.