A Study on the PLL Synthesizer in the Automobile-based SAR

Maste

고주파 증폭기의 시험 절차

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포항가속기연구소의 연속파 고출력 고주파 클라이스트론

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Synthesizing Image and Automated Annotation Tool for CNN based Under Water Object Detection

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

Upgrade of PLS SR Cavity Cooling Water System

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S-Band 고출력 클라이스트론 개발을 위한 RF 시스템의 장치 설계

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RF Noise Effects on Beam Instabilities and Beam Lifetime in PLS Storage Ring

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