小管道离散泡状流的形成与发展

航天(尤其是大型载人航天及深空探测)技术的发展,激发了对微重力气液两相流研究的强烈需求和兴趣。在航天应用中小直径

Using Hyperspectral Imagery and GA-PLS Algorithm to Estimate Chemical Oxygen Demand Concentration of Water in River Network

【Objective】The hyperspectral remote sensing has proven potential to monitor water quality, but issues such as data redundancy and susceptibility to environmental variation could affect its accuracy and reliability. The genetic algorithm-partial least squares (GA-PLS) algorithm with a function to select sensitive spectral variables could resolve these problems. The GA-PLS algorithm was mainly used in retrieval of the optically active parameters such as transparency, chlorophyll-a, suspended matter and turbidity in surface water bodies. The purpose of this paper is to combine it with hyperspectral retrieval model to estimate chemical oxygen demand (COD) concentration of water in the river network in the Pearl River estuary.【Method】Hyperspectral imageries and COD concentration of 146 samples taken from water bodies in the Pearl River estuary were collected, and the characteristic bands of the hyperspectral reflectance data were screened using the GA-PLS algorithm to retrieve the COD concentration. The differences in retrieval accuracy between different band combinations were compared.【Result】The COD concentration retrieved from the hyperspectral imageries based on the GA-PLS algorithm is more accurate than that calculated using the full-spectrum PLS model. The minimum RMSEP of the method was 4.887 mg/L, 11.4% less than that of the full-spectrum PLS model. Using 74 filtered bands, accounting for 2.9% of the full bands, the model was still stable and accurate. Some characteristic bands obtained by the GA-PLS algorithm have physical interpretation, indicating that the screening results were rational.【Conclusion】The GA-PLS algorithm can be used to screen characteristic bands from the hyperspectral imageries to reduce the number of data and simplify the model as a result. It can accurately estimate COD of water in river networks

中国科学院科学数据网格建设的进展

中国科学院科学数据网格是在中国科学院科学数据库的基础上构建的数据网格群,其目的是整合物理上分布的科学数据资源,形成逻辑统一的数据资源视图,提供透明的数据访问,并基于这些科学数据和网格上的先进的计算与存储设施,提供科学数据分析、处理和可视化,实现便捷的科学数据应用服务,形成中国科学院科学数据应用环境的发展构架。作为中国科学院科学数据网格建设的第一阶段,首先进行了网格总中心和四个学科领域的科学数据网格试点:化学数据网格、空间科学数据网格、微生物与病毒学数据网格和人地系统科学数据网格的建设。本文介绍了本阶段建设的主要进展与成果,并对未来工作进行了展望