• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.1-14
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• 2022

This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.729-741
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• 2015

This study examined the applicability of MBLRP (Modified Bartlett-Lewis Rectangular Pulse) rainfall generation model for an urban flood simulation which is a type of Poisson cluster rainfall generation model. This study constructed XP-SWMM model for Namgajwa area of Hongjecheon basin, which is a two-dimensional pipe network-surface flood simulation program and computed a flood discharge and a flooded area with input data of synthetic rainfall time series of 200 years that were generated by the MBLRP model. This study compared the data of flood with synthetic rainfall and flood with corresponding values which were based on design rainfall. The results showed that the flooded area computed with MBLRP model was somewhat smaller than the corresponding values on the basis of the design. A degree of underestimation was from 8% (5 year) to 34% (200 year) and the degree of underestimation increased as a return period increased. This study is meaningful in that it proposes methodology that enables quantifiability of uncertain variables which are related to a flooding through Monte Carlo analysis of urban flooding simulation and applicability and limitations thereof.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.