中国科学院机构知识库网格系统: 检索

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	TAEffect: Quantifying interaction risks in trust-enabled communication systems 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2022, 页码: 20 作者: Lu, Yunfeng; Fan, Xinxin; Jing, Quanliang \| 收藏 \| 浏览/下载：10/0 \| 提交时间：2023/07/12 adverse effect communication systems interactive networks network percolation trust management
	Spatio-temporal evolution and influencing factors of geopolitical relations among Arctic countries based on news big data 期刊论文 OAI收割 JOURNAL OF GEOGRAPHICAL SCIENCES, 2022, 卷号: 32, 期号: 10, 页码: 2036-2052 作者: Li Meng; Yuan Wen; Yuan Wu; Niu Fangqu; Li Hanqin \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2022/11/09 Arctic geographical relationship spatiotemporal data mining topic model interactive network big data
	Spatio-temporal evolution and influencing factors of geopolitical relations among Arctic countries based on news big data 期刊论文 OAI收割 JOURNAL OF GEOGRAPHICAL SCIENCES, 2022, 卷号: 32, 期号: 10, 页码: 2036-2052 作者: Li Meng; Yuan Wen; Yuan Wu; Niu Fangqu; Li Hanqin \| 收藏 \| 浏览/下载：21/0 \| 提交时间：2022/11/09 Arctic geographical relationship spatiotemporal data mining topic model interactive network big data
	AGUnet: Annotation-guided U-net for fast one-shot video object segmentation 期刊论文 OAI收割 PATTERN RECOGNITION, 2021, 卷号: 110, 页码: 10 作者: Yin, Yingjie; Xu, De; Wang, Xingang; Zhang, Lei \| 收藏 \| 浏览/下载：45/0 \| 提交时间：2021/01/06 Fully-convolutional Siamese network U-net Interactive image segmentation Video object segmentation
	Graph-convolutional-network-based interactive prostate segmentation in MR images 期刊论文 OAI收割 MEDICAL PHYSICS, 2020 作者: Tian, Zhiqiang; Li, Xiaojian; Zheng, Yaoyue; Chen, Zhang; Shi, Zhong \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2020/10/26 graph convolutional network interactive segmentation prostate MR image
	A Basal Ganglia Network Centric Autonomous Learning Model and Its Application in Unmanned Aerial Vehicle 会议论文 OAI收割安徽合肥, 2015年12月11-13日作者: Yi, Zeng; Guixiang, Wang; Bo, Xu; Yi Zeng \| 收藏 \| 浏览/下载：30/0 \| 提交时间：2016/12/09 Autonomous Learning Model Basal Ganglia Network Precise Encoding Uav Autonomous Learning Reinforcement Learning Interactive Environment.
	Semantic linking through spaces for cyber-physical-socio intelligence: A methodology 期刊论文 OAI收割 ARTIFICIAL INTELLIGENCE, 2011, 卷号: 175, 期号: 5-6, 页码: 988-1019 作者: Zhuge, Hai \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2019/12/16 Cyber-physical society Cyber-physical-socio intelligence Cyber-physical system Complex intelligence Future interconnection environment Interactive semantics Resource space model Semantic link network
	Interactive semantics 期刊论文 OAI收割 ARTIFICIAL INTELLIGENCE, 2010, 卷号: 174, 期号: 2, 页码: 190-204 作者: Hai Zhuge \| 收藏 \| 浏览/下载：19/0 \| 提交时间：2019/12/16 Classification Open system Semantics Semantic link network Social semantics Interaction Interactive semantics
	The compression and storage method of the same kind of medical images-DPCM (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 Medical imaging has started to take advantage of digital technology opening the way for advanced medical imaging and teleradiology. Medical images however require large amounts of memory. At over 1 million bytes per image a typical hospital needs a staggering amount of memory storage (over one trillion bytes per year) and transmitting an image over a network (even the promised superhighway) could take minutes - too slow for interactive teleradiology. This calls for image compression to reduce significantly the amount of data needed to represent an image. Several compression techniques with different compression ratio have been developed. However the lossless techniques which allow for perfect reconstruction of the original images yield modest compression ratio while the techniques that yield higher compression ratio are lossy that is the original image is reconstructed only approximately Medical imaging poses the great challenge of having compression algorithms that are lossless (for diagnostic and legal reasons) and yet have high compression ratio for reduced storage and transmission time. To meet this challenge we are developing and studying some compression schemes which are either strictly lossless or diagnostically lossless taking advantage of the peculiarities of medical images and of the medical practice. In order to increase the Signal to-Noise Ratio (SNR) by exploitation of correlations within the source signal a method of combining differential pulse code modulation (DPCM) is presented.