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일본 R&D의 재구성 : 일본의 R&D에서 과학의 영향력 증가
장제연,Jang, Je-Yeon 한국특허정보원 2005 Patent 21 Vol.62 No.-
본 보고서는 일본의 경제연구소(RIETI)에서 발간한 보고서로서 특허인용문에 쓰이는 일반적인 과학저널의 데이터를 대상으로 분석을 실시한 것이다.
장제연,Jang, Je-Yeon 한국특허정보원 2006 Patent 21 Vol.67 No.-
과학기술활동에 의해 창출된 발명은 주로 특허에 의해 보호되고 있으나, 특허통계ㆍ지표에 대해서 국제적인 기준이 마련되지 않아 국제비교가 어려운 실정이며, 측정요소들의 조합에 의해 특허통계ㆍ지표의 종류 또한 매우 다양하지만, 이 지표를 모두가 과학기술활동을 측정하는 데에 유용한지에 대해서는 의문시된다. 따라서 그 대안으로서 각국 국가연구개발사업의 성과평가지표 활용사례 및 활용되고 있는 특허통계ㆍ지표를 중심으로 분석ㆍ연구하고자 한다.
3D Reconstruction of Trees in Apple Orchard using a Multi Camera-based UAV System
동슈아 ( Xu-hua Dong ),장제연 ( Je-yeon Jang ),이경환 ( Kyeong-hwan Lee ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.1
3D reconstruction of trees in orchards plays an important role in the studies on the biological characteristics of fruit trees. Compared with that using vertical photogrammetry, the multi-camera oblique photography can generate higher quality of 3D models, but there is no optimized method for the multiple cameras system. Therefore, the objective of this study was to establish mathematics models that can optimize the configuration parameters such as flight height, oblique angle of cameras and then reconstruct the 3D model of apple tress using UAV-based oblique photogrammetry. This study proposed three types of ground sample distance to establish the models and used maximum curvature algorithm to solve the optimal parameters. Five Survey3 cameras were used to constitute the oblique photography system. The models was validated in simulation platform and real orchard condition. The experiment results indicated the optimization of the parameters was achieved at a flight height of 8m and a oblique angle of 55 degree. With this new approach, a better accuracy of 3D model can be achieved in real orchard condition. In the future we will count the number of fruit based on the 3D model generated by this approach.
동슈아 ( Xuhua Dong ),김우영 ( Woo-young Kim ),정육 ( Zheng Yu ),오주열 ( Ju-youl Oh ),장제연 ( Je-yeon Jang ),이경환 ( Kyeong-hwan Lee ) 한국농업기계학회 2021 한국농업기계학회 학술발표논문집 Vol.26 No.2
The assessment of 3D fruit phenotyping traits of apple trees can provide management strategies for growers of apple orchards. The estimation of quantitative distribution of apples in orchard is an important parameter for yield estimation. Since it is hard to quantify them manually, resolving apple phenotyping efficiently is critical for monitoring apple yield and promoting a better management system. Thus in current study, we have developed a novel technology for 3D mapping of three types of apple training system fields. The 3D point cloud of apples was reconstructed using high spatial and temporal multi-viewing images collected by unmanned aerial vehicles (UAVs) based multi-camera system. The extraction of information about individual apple in 3D point cloud was executed using 3D instance segmentation algorithm which includes generalized sparse convolutional neural networks, discriminative loss function, and varying density-based 3D clustering method. The developed apple traits extraction algorithm could measure the 3D position of an individual apple by sphere fitting. The accuracy of the technique was evaluated by comparing its results with manual estimates of number of apples. The results obtained from our method are in good agreement with manual estimates. The average accuracy of apple counting in three types of the fields were ~92 % followed by the linear regression (R2) of 0.92 with root-mean-square error (RMSE) value of 13.93. Thus, 3D spatial distribution of apples were achieved and analyzed by above technique. This research proposes a method that combines 3D photography with 3D instance segmentation to accurately extract individual apples from various types of apple training systems in orchards and can also be used to segment and analyze other fruits.