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정하규(Ha Kyu Jung),정종혁(Jong Hyuk Jung),이현욱(Hyun Wook Lee),권원태(Won Tae Kwon),김상길(Sang Gil Kim),전숙례(Sook Lye Jeon) 대한기계학회 2007 대한기계학회 춘추학술대회 Vol.2007 No.5
Water resource can be examined using biological sensors. Algae has been one of the biological sensors used to evaluate and monitor the water pollution. The monitoring system, however, could determine whether the examined water was safe or not. It needs additional expensive chemical test to figure out the cause of the water pollution. In this study, an endeavor is given to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve(FIC) from algae using monitoring system. Fundamental curves are obtained from the experiments with specified amount of toxicant. Baysian method is utilized to determine the unknown toxicant in the water by comparing it with the fundamental curves. The results shows that the proposed method works fairly well.
GE AdvanceTM 양전자방출단층촬영기의 표준 성능평가
정하규(Ha Kyu Jeong),김희중(Hee Joung Kim),손혜경(Hye Kyung Son),봉정균(Jung Kyun Bong),정해조(Hai Jo Jung),전태주(Tae Joo Jeon),김재삼(Jae Sam Kim),이종두(Jong Doo Lee),유형식(Hyung Sik Yoo) 대한핵의학회 2001 핵의학 분자영상 Vol.35 No.2
N/A Purpose: The purpose of this study was to establish optimal imaging acquisition conditions for the GE AdvanceTM PET imaging system by performing the acceptance tests designed by National Electrical Manufacturers Association (NEMA) protocol and General Electric Medical Systems (GEMS) test procedures. Materials and Methods: Performance tests were carried out with 18FDG radioactivity source and phantoms by using a standard acquisition mode. Transaxial resolution and scatter fraction tests were performed with a line source and axial resolution with a point source, respectively. A cylindrical phantom made of polymethylmethacrylate (PMMA) was used to measure sensitivity, count rate losses and randoms, uniformity correction, and attenuation inserts were added to measure remaining tests. The test results were acquired in a diagnostic acquisition mode and analyzed mainly on high sensitivity mode. Results: Transaxial resolution and axial resolution were measured as average of 4.65 mm and 3.98 mm at 0 cm, and 6.02 mm and 6.71 mm at 20 cm on high sensitivity mode, respectively. Average scatter fraction was 9.87%, and sensitivity was 225.8 kcps/ Ci/cc of trues. Activity at 50% deadtime was 4.6 Ci/cc, and the error of count rate correction at that activity was from 1.49% to 3.83%. Average nonuniformity for total slice was 8.37%. The accuracy of scatter correction was -0.95%. The accuracies of attenuation correction were 5.68% for air, 0.04% for water and -6.51% for polytetrafluoroethylene (PTFE). Conclusion: The results satisfied most acceptance criteria, indicating that the GE AdvanceTM PET system can be optimally used for clinical applications. (Korean J Nucl Med 2001;35:100-112)
기술노트 : 조류를 이용한 수계모니터링 시스템에서 뉴럴 네트워크에 의한 실시간 독성물질 판단
정종혁 ( Jong Hyuk Jung ),정하규 ( Ha Kyu Jung ),권원태 ( Won Tae Kwon ) 한국물환경학회 ( 구 한국수질보전학회 ) 2008 한국물환경학회지 Vol.24 No.1
Biological and chemical sensors are the two most frequently used sensors to monitor the water resource. Chemical sensor is very accurate to pick up the types and to measure the concentration of the chemical substance. Drawback is that it works for just one type of chemical substance. Therefore a lot of expensive monitoring system needs to be installed to determine the safeness of the water, which costs too much expense. Biological sensor, on the contrary, can judge the degree of pollution of the water with just one monitoring system. However, it is not easy to figure out the type of contaminant with a biological sensor. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve (FIC) from a biological monitoring system. Wem-tox values are calculated from the amount of flourescence of contaminated and reference water. Curve fitting is executed to find the representative curve of the raw data of Wem-tox values. Then the curves are digitalized at the same interval to train the neural network model. Taguchi method is used to optimize the neural network model parameters. The optimized model shows a good capacity to figure out the toxicant from FIC.