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Coumatetralyl과 Flocoumafen 원제 및 제품 제형별 시궁쥐에 대한 살서효과 평가
박성빈(Seong Bin Park),엄태일(Tae Il Eom),김현경(Hyun Kyung Kim),김길하(Gil-Hah Kim) 한국농약과학회 2021 농약과학회지 Vol.25 No.2
Rodenticide effects of Norway rat (Rattus norvegicus) for each formulation of products using coumatetralyl and flocoumafen were experimented with non-choice and choice tests. The 99% lethal dose (LD99) values of coumatetralyl and flocoumafen, which are the active ingredient of rodenticide, were 0.0176 g/100 g and 0.00512 g/100 g, respectively. In the non-choice test of 3 types of coumatetralyl product (tracking powder, paste and rice type) and 2 types of flocoumafen product (rice and pellet type), the mortality was 100%, but there was a difference between the products in the food intake and the change in weight. In the choice test, 100% mortality was observed in all products except tracking powder (T.P) type of coumatetralyl, and the T.P. type was the most poison-shyness to food intake. There was a difference in lethal time according to experimental method and product formulation, but all showed high rodenticide effect. However, if there is an alternative food in an outdoor environment, rodenticide effect may be reduced due to the rejection of the intake of rodenticide. Therefore, it is expected that these data could be helpful for developmental of rodenticide formulation.
공정자료만을 이용한 모델링 및 최적화 에서 Data Reconciliation 과 Gross Error Detection
박선원 ( Sun Won Park ),엄태일 ( Tae Il Eom ),김인원 ( In Won Kim ) 한국화학공학회 1995 Korean Chemical Engineering Research(HWAHAK KONGHA Vol.33 No.5
Measured process data are usually containing random errors and gross errors. These measured data do not satisfy process constraints such as the mass and energy balances that describe a process. For the use of these error-contained data in process analysis and optimization, the preprocessing steps such as gross error identification and elimination, and data reconciliation(data rectification) are prerequisite. The existing methods are based on mathematical and statistical techniques, but recently neural networks were investigated for data rectification. In this study, autoassociative neural networks(AAN) and robust ANN(RAAN) were applied for the data rectification of process data of CSTR. The performance of RAAN proved to be superior to that of AAN in the data rectification. We conclude that the use of AAN and RAAN appears to be a promising tool for data rectification.