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우리나라에서 서식하고 있는 바퀴벌레의 분포에 관한 연구
권순완,오신근,윤운기,김성원,이용석,김광호,김명진,김정근,김대식,김효신,유인석,이수영,정병주,김규언,김동수,이기영,이한일 대한천식알레르기학회 1993 천식 및 알레르기 Vol.13 No.3
Studies on the infestation rate and population density of cockroaches have been investigated in Seoul, Pusan and Junbuk district for one month in May 1992. The sampling of the cockroaches in the study were conducted in the kitchens, bed- rooms, floors, and toilets in huses of 73 Allergic patients. Allergic patients who lived in Seoul, Pusan, and Junbuk district were selected. A sticky trap were used to attract cockroaches. The results are summerized as follows ' 1) The cockroaches were collected in 58 houses out of a total of 73 houses. This corresponds to a 78.1 percent infestation rate of cockroaches. The average number of cockroaches collected per house was 22.8. B. germanica was the most Common species, showing 81.2 percent of the collected cockroaches, followed by P.japonica(9.1%), P.fuligionsa(4.8% ), and P americana(4.5%). 2) P. japonica were collected almost uniformly in those three districts, but P. fuliginosa and P. americana were mainly collected in the Pusan district. 3) Houses in poor sanitation and those with an installed central heating system showed higher infestation rates. 4) The average number of cockroaches collected in patients houses whose residents sho- wed positive results to allergy skin test were 32.4. This number was significantly higher than that of cockroaches collected in the houses negative allergy skin test patients, where. 18.2 was the average number of cockroaches.
닥나무 인피섬유의 제조 지역 식별을 위한 적외선 스펙트럼 데이터 전처리 및 기계학습 모델링
이용주,권순완,김재협,차지은,강광호,김형진 한국펄프·종이공학회 2023 펄프.종이기술 Vol.55 No.5
The objective of this study was exploring the impact of spectral data preprocessing techniques on the performance of machine learning models for classifying the origin of mulberry bast fibers. The findings indicated that a selective spectral region (1800-1200 cm-1) significantly improves classification model performance. Among the classifiers tested, Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machines (SVM) demonstrated the highest accuracy. Additionally, A spectral preprocessing with the Norris-Williams algorithm effectively improved model performance within the same classifier for this dataset. These results suggest that applying machine learning modeling with spectral preprocessing can enable the origin classification of mulberry bast fibers and provide a chemical basis for classification rules beyond simple categorization.