• Journal of the Korean earth science society
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• v.34 no.3
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• pp.235-248
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• 2013

The purpose of this study is to investigate a feasibility of a small mountain as a field work site on geological features in Earth sciences classes at all levels. Mt. Geumdang with the height of 305 meters from the sea level is located in the metropolitan city of Gwangju, southern part of Korea. The study reviews the human and natural geography, geological features, geomorphic resources, landscapes, and conveniences of the mountain for a possibility of meaningful field work. The population within the distance of 5 km from the mountain stands at about 620,000 and 170,000 of them are students and teachers. Mt. Geumdang has a warm temperature climate with low rainfall throughout the year, so it seems suitable for a field survey. Road network and public transportation system around the area are well-developed and easily accessible. Mt. Geumdang shows various rock type and geological structures. The basement rock is Gwangju granite, which is plutonic body of the Jurassic period. Also, granophyre (micrographic granite) and various volcanic rocks distributed as bedded tuff, lapilli tuff, and rhyolite of the Cretaceous period. Many andesitic and felsic dykes were intruded into the rock by joint system. In Mt. Geumdang, many geomorphic resources are found such as U shaped mountain, joint, fault, lamination, gnamma, tor, cliff, groove, block stream and block field, regolith, and saprolite. It has a beautiful mountain scenery including the view of whole shape of Mt. Mudeung, panoramic view of the town, Pungam lake, World Cup stadium and sunrise and sunset. Furthermore, the area has ecologic study facilities related to geology, emergency medical and convenience facilities for field works. In conclusion, Mt. Geumdang is highly feasible for geological field studies at all levels.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.679-686
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• 2006

Recently, the city railway in the Seoul Metropolitan Area (SMA) has offered a low quality of service as a passage time, because it was operated slowly. So, the people who live in modern society are not satisfied about passage time, therefore, this study tried to make that the subway in the SMA becomes a more functional and effective wide-area-transportation-network through an express train introduction's method which examined cases from abroad and current system. and then presented how express train could be applied to current system. In a case study, We used the An-San Line and Su-In Line as a examples and developed a schedule which can minimize the delaying time of subway by using Branch & Bound Algorithm. The train operational plan was loaded to consider a railroad siding, Obtained site, and the dispatch interval(three to ten minutes) for the express and local lines and finally, We presented an alternative operational plan which made by those factors.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.