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LandGEM 모델을 이용한 청주권 생활폐기물 매립장의 매립지가스 발생상수 및 메탄 잠재발생량 산정
홍상표(Hong Sangpyo) 한국환경보건학회 2008 한국환경보건학회지 Vol.34 No.6
Methane is a potent greenhouse gas and methane emissions from landfill sites have been linked to global warming. In this study, LandGEM (Landfill Gas Emission Model) was applied to predict landfill gas quantity over time, and then this result was compared with the data surveyed on the site, Cheongju Megalo Landfill. LandGEM allows the input of site-specific values for methane generation rate (k) and potential methane generation capacity Lo, but in this study, k value of 0.04/yr and Lo value of 100 m³/ton were considered to be most appropriate for reflecting non-arid temperate region conventional landfilling like Cheongju Megalo Landfill. Relatively high discrepancies between the surveyed data and the predicted data about landfill gas seems to be derived from insufficient compaction of daily soil-cover, inefficient recovery of landfill gas and banning of direct landfilling of food waste in 2005. This study can be used for dissemination of information and increasing awareness about the benefits of recovering and utilizing LFG (landfill gas) and mitigating greenhouse gas emissions. Methane is a potent greenhouse gas and methane emissions from landfill sites have been linked to global warming. In this study, LandGEM (Landfill Gas Emission Model) was applied to predict landfill gas quantity over time, and then this result was compared with the data surveyed on the site, Cheongju Megalo Landfill. LandGEM allows the input of site-specific values for methane generation rate (k) and potential methane generation capacity Lo, but in this study, k value of 0.04/yr and Lo value of 100 m³/ton were considered to be most appropriate for reflecting non-arid temperate region conventional landfilling like Cheongju Megalo Landfill. Relatively high discrepancies between the surveyed data and the predicted data about landfill gas seems to be derived from insufficient compaction of daily soil-cover, inefficient recovery of landfill gas and banning of direct landfilling of food waste in 2005. This study can be used for dissemination of information and increasing awareness about the benefits of recovering and utilizing LFG (landfill gas) and mitigating greenhouse gas emissions.
마상용(Sangyong Ma),홍상표(Sangpyo Hong),심현민(Hyeon-min Shim),권장우(Jang-Woo Kwon),이상민(Sangmin Lee) 대한전기학회 2016 전기학회논문지 Vol.65 No.9
According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject’s neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM’s correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.