通过Cite Space与VOSviewer软件对2010~2021年熔盐储能技术的相关文献进行可视化分析,明确了熔盐储能研究现状与热点。结果表明,随着时间的增加,发文量逐渐增加;国内主要以国家自然科学基金和地方自然科学基金为主,以熔盐、储能光热发...通过Cite Space与VOSviewer软件对2010~2021年熔盐储能技术的相关文献进行可视化分析,明确了熔盐储能研究现状与热点。结果表明,随着时间的增加,发文量逐渐增加;国内主要以国家自然科学基金和地方自然科学基金为主,以熔盐、储能光热发电、太阳能,储能材料等为研究热点;国外主要以英国伯明翰大学和西班牙莱里达大学为主,形成了熔盐储能、系统性、高温熔融盐的腐蚀、熔盐影响因素等4大聚类主题。学术成果在英文期刊Solar Energy、Solar Energy Materials And Solar Cells和中文期刊《无机盐工业》《低碳世界》和《广州化工》等期刊发文量较大。展开更多
Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,...Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.展开更多
文摘通过Cite Space与VOSviewer软件对2010~2021年熔盐储能技术的相关文献进行可视化分析,明确了熔盐储能研究现状与热点。结果表明,随着时间的增加,发文量逐渐增加;国内主要以国家自然科学基金和地方自然科学基金为主,以熔盐、储能光热发电、太阳能,储能材料等为研究热点;国外主要以英国伯明翰大学和西班牙莱里达大学为主,形成了熔盐储能、系统性、高温熔融盐的腐蚀、熔盐影响因素等4大聚类主题。学术成果在英文期刊Solar Energy、Solar Energy Materials And Solar Cells和中文期刊《无机盐工业》《低碳世界》和《广州化工》等期刊发文量较大。
文摘Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
