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	A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network 期刊论文 OAI收割 ACTA MECHANICA SINICA, 2023, 卷号: 39, 期号: 3, 页码: 322302 作者: Xu SF(许盛峰); Sun ZX(孙振旭); Huang RF(黄仁芳); Guo DL(郭迪龙); Yang GW(杨国伟) \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2023/04/20 Physics informed neural network Flow field reconstruction Particle image velocimetry Cosine annealing algorithm Experimental fluid dynamics
	Microstructure, Mineralogical Characterization and the Metallurgical Process Reconstruction of the Zinc Calcine Relics from the Zinc Smelting Site (Qing Dynasty) 期刊论文 OAI收割 MATERIALS, 2021, 卷号: 14, 期号: 8, 页码: 15 作者: Xiao, Ya; Zhou, Wenli; Mo, Linheng; Chen, Jianli; Li, Meiying \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2022/07/01 archeological evidence zinc metallurgy zinc calcine roasting characterization experimental reconstruction
	Research and application of spectral reconstruction technology based on periodic structure 会议论文 OAI收割 Xiamen, China, 2020-08-25 作者: Liu, Bin; Wei, Ru Yi; Shi, Yi Shi; Shi, Lei; Zhang, Zai Kun \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2020/12/29 spectral reconstruction hole array experimental verification diffraction
	Reconstruction of the Plasma Boundary of EAST Tokamak Using Visible Imaging Diagnostics 期刊论文 OAI收割 IEEE TRANSACTIONS ON PLASMA SCIENCE, 2018, 卷号: 46, 期号: 6, 页码: 2162-2169 作者: Zhang, Heng; Xiao, Bingjia; Luo, Zhengping; Hang, Qin; Yang, Jianhua \| 收藏 \| 浏览/下载：67/0 \| 提交时间：2019/11/12 Experimental advanced superconducting tokamak (EAST) nuclear fusion energy plasma boundary reconstruction plasma control visible imaging diagnostics
	Lossless wavelet compression on medical image (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：42/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.