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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.
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MSP430 기반 저전력 뇌 신경자극기 S/W 설계 및 구현
홍상표(Sangpyo Hong),권성호(Cheng-Hao Quan),심현민(Hyun-Min Shim),이상민(Sangmin Lee) 대한전자공학회 2016 전자공학회논문지 Vol.53 No.7
인체 삽입형 뇌 신경자극기는 소비전력에 있어서 효율적인 구조로 설계되어야 한다. 이들 자극신호는 파형이 단순하고, MCU(micro controller unit)의 대기시간은 실행시간보다 훨씬 긴 특성을 가짐에도 불구하고, 이러한 특성을 고려한 저전력 설계가 되어 있지 않다. 본 논문에서는 자극신호 특성에 기반하는 저전력 알고리즘을 제안한다. 또한 뇌 신경자극기 S/W, NMS(neuro modulation simulation)의 설계 및 구현 결과도 제시한다. 저전력 알고리즘 구현을 위해, 기존 뇌 신경자극기 프로그램의 함수별 수행(running) 시간을 분석하여, 실행(execution) 시간과 대기(waiting) 시간을 도출하였다. 그리고 AMLPM(active mode-low power mode) 전환시간을 추정하여 저전력 알고리즘 구현에 반영하였다. 본 논문에서 제안하는 저전력 알고리즘은 자극신호의 특성을 이용하여 출력을 다수의 구간으로 분할하고, MCU를 구간별 AM 또는 LPM으로 운용한다. 제안하는 알고리즘의 검증을 위해, 외부 제어프로그램을 개발하여 알고리즘의 동작상태를 확인하였고, 오실로스코프를 이용하여 출력신호의 정확성을 확인하였다. 검증 결과, 제안하는 저전력 알고리즘을 적용할 경우, 기존 뇌 신경자극기 대비 소모전류를 76.31% 감소시킴을 확인 할 수 있었다. A power-efficient neuromodulator is needed for implantable systems. In spite of their stimulation signal’s simplicity of wave shape and waiting time of MCU(micro controller unit) much longer than execution time, there is no consideration for low-power design. In this paper, we propose a novel of low-power algorithm based on the characteristics of stimulation signals. Then, we designed and implement a neuromodulation software that we call NMS(neuro modulation simulation). In order to implement low-power algorithm, first, we analyze running time of every function in existing NMS. Then, we calculate execution time and waiting time for these functions. Subsequently, we estimate the transition time between active mode (AM) and low-power mode (LPM). By using these results, we redesign the architecture of NMS in the proposed low-power algorithm: a stimulation signal divided into a number of segments by using characteristics of the signal from which AM or LPM segments are defined for determining the MCU power reduces to turn off or not. Our experimental results indicate that NMS with low-power algorithm reducing current consumption of MCU by 76.31 percent compared to NMS without low-power algorithm.
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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.
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