• 한국도로학회논문집
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• 제19권2호
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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.

• 한국도로학회논문집
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• 제20권2호
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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.

• 한국대기환경학회지
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• 제24권2호
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• pp.133-142
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• 2008

이 연구에서는 2005년 5월부터 2006년 6월까지 5차례에 걸쳐 서울시 신촌동에 위치한 도로변에서 입자상 PAHs의 농도 분포 특성을 살펴보았다. 도로변에서 입자상 PAHs 농도 분포는 측정시기별로 다른 양상을 보였지만, 2005년 11월을 제외하고는 벤젠고리가 $5{\sim}6$개로 구성된 고분자량 성분들의 농도가 높았다. 입자상 PAHs의 도로변 고유한 특성을 살펴보기 위해 기존 대기와 터널에서 측정한 입자상 PAHs 농도들과 비교하였다. 터널 결과에 비해 상대적으로 도로변에서는 BbF, Ind, BghiP 등의 고분자량 성분들의 비율이 높았는데, 이것은 동력계 시험을 바탕으로 알려진 자동차의 PAHs 배출 특성과 유사하였다. 대기에서는 도로변에 비해 Phen, Pyr, Flt이 높았지만, 도로변에서는 이들 성분들이 고분자량 성분들과 비슷한 비율로 분포하였다. 서울과 고산 대기 중 입자상 PAHs에는 석탄 연소를 포함한 여러 배출원에서 배출된 PAH 성분들이 혼합되어 있는 반면, 도로변에서 측정한 이 연구결과는 자동차 배출에 의한 영향이 지배적이었기 때문인 것으로 여겨졌다. 도로변에서 입자상 PAHs의 주요 배출원을 추정하기 위하여 특정 성분들의 농도비를 분석한 바에 의하면, 일반적으로 도로변에서는 자동차 배출에 의한 영향이 지배적이었고, 자동차 중에서도 경유 자동차 배출의 영향이 컸던 것으로 판단되었다. 2005년 9월과 11월에는 석탄과 바이오매스 연소의 영향이 보였는데, 이는 도로변이 대기와 혼재되어 나타난 결과로 추정된다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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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.