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김희종 ( Hee-jong Kim ),채희영 ( Hee-young Chae ),박성준 ( Seong-jun Park ),성환우 ( Haan-woo Sung ),김종택 ( Jong-taek Kim ) 한국가축위생학회 2017 韓國家畜衛生學會誌 Vol.40 No.2
The present study was carried out to evaluate the effect of avian pox on wild bird population by investigating the avian pox infection in migratory birds of a stopover site. 3,565 birds in 116 species were examined for avian pox in migratory birds at the Heuksando island in South Korea during the spring and fall of 2011. 20 birds in 12 species were found pox-like lesions and 5 birds were diagnosed by avian pox using PCR: Pale Thrush (Turdus pallidus), Yellow-breasted Bunting (Emberiza aureola), Yellow-throated Bunting (Emberiza elegans), Rustic Bunting (Emberiza rustica), Black-faced Bunting (Emberiza spodocephala). To our knowledge, this is the first report of avian pox detected in these 5 species of the world.
Micro Inductor를 사용한 Mobile TV Tuners 용 RF front-end 설계
김희종(Hee-Jong Kim),범진욱(Joow-wook Burm) 대한전자공학회 2007 대한전자공학회 학술대회 Vol.2007 No.7
This work proposes RF front-end for Mobile TV tuner. The Proposed RF front-end is operated in L-Band(near 1.5㎓) for Direct Conversion System and employs an Micro Inductor for the purpose of reducing of layout area. Low Noise Amplifier is designed to have low noise, High Gain, and increasing stability. Passive Mixer don't flow DC currents, that make Mixer having low Flicker Noise The Proposed RF front-end decreases layout area more than 300㎛ x 800 ㎛.
그라디언트 부스팅과 균형 분류를 이용한 채무 불이행 예측
김희종(Hee-Jong Kim),김형도(Hyoung-Do Kim) 한국정보기술학회 2014 한국정보기술학회논문지 Vol.12 No.1
Gradient boosting, which is a kind of boosting technique, builds a strong prediction model by combining many weak prediction models created step by step with gradients of a loss function. Gradient boosting gives interpretable results, while missing values are managed almost without loss of information, and feature selection is performed as an integral part of the procedure. These properties make gradient boosting a good candidate for loan default prediction. As far as we know, the application of this method to loan default prediction has not been fully documented to date. This paper tries to evaluate it comparatively with other algorithms. Some alternatives for solving the imbalance characteristic of the loan default prediction problem are also analyzed in combination with the method. Gradient boosting shows the best result in AUC and improvements through the combination are also meaningful in G-mean and F-measure.