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작물재배 환경이 Arbuscular 내생균근 균의 상대밀도와 작물체의 인(P) 함량에 미치는 영향
이국한 ( Kook Han Lee ),안승원 ( Seoung Won Ann ),이창홍 ( Chang Hong Lee ),이인봉 ( In Bong Lee ),채수천 ( Soo Cheon Chae ),김혜영 ( Hye Young Kim ),김영칠 ( Young Chil Kim ) 한국환경과학회 2012 한국환경과학회지 Vol.21 No.5
Available phosphorus(P2O5) in conventionally cultivated soil was more abundant in two fold than that of organically cultivated soil. Relative density of Arbuscular Mycorrhizal Fungi (AMF) was higher in organically cultivated soil, That of welsh onion cultivated soil was the highest, that of strawberry was followed and then that of pepper, respectively. Relative density of AMF was inversely proportioned to available soil phosphorus. Phosphorus content of crop and relative density of AMF were more abundant in organically cultivated crop or soil. However available soil phosphorus content was much in conventionally cultivated soil. The phosphorus contents between soil and crop were negatively correlated. The phosphorus content of crop was increased as the relative density of AMF increased. Relative density of AMF in the organically cultivated soil and phosphorus content of the crop with organic cultivation were higher than those of conventionally cultivated.
에지특징에 근거한 이미지영역 분리와 DCT를 이용한 얼굴 인식
박수봉,이인범 東新大學校 1998 論文集 Vol.10 No.-
In this paper, we propose a face recognition algorithm which extract characteristics of image using edge and DCT(Discrete Cosine Transform). In this algorithm, training vectors of neural networks is the extracted data. With the same luminesce and distance, the fixed CCD camera, human face was captured. Edge characteristics of face images is concentrated in eye bows and mouth. Therefore, using edge characteristics of face images, it was segmented with square region. we determined this area to the characteristics region of face images, and contains eye bows, eyes, nose and mouth. Also, after executing DCT for this square region, we extracted feature vector. This feature vector was normalized and set the input vector of neural networks. Simulation results show 100% recognition for 30 face images when face images were learned and 94% recognition rate when face images weren't learned. Also, in case of DCT processing, the proposed algorithm reduced 55% operation time than to process all face images.