CIAC COMPREHENSIVE INFORMATION-SYSTEM OF RARE-EARTHS

The CIAC (Changchun Institute of Applied Chemistry) Comprehensive information System of Rare Earths is composed of three subsystems, namely, extraction data, physicochemical properties, and reference data. This paper describes the databases pertaining to the extraction of rare earths and their physicochemical properties and discusses the relationships between data retrieval and optimization and between the structures of the extractants and the efficiency with which they are extracted. Expert systems for rare earth extraction and calculation of thermodynamic parameters are described, and an application of pattern recognition to the problems of classification of compounds of the rare earths and prediction of their properties is reported.

一种高效的分布式测控系统的数据压缩管理方法

提出了一种无损压缩的数据存储方法,对测控系统中的历史数据进行高效的管理,在监控的过程量很多的情况下,一般能达到1∶30的压缩比,而且将大大提高数据的检索效率。给出了其具体实现,该方法在类似系统中有着广泛的适用性。

基于WEB支持的市场客户管理系统的设计

本文阐述了采用Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ技术和利用ＡＳＰ实现数据的动态发布技术以及基于分布网络环境下的异地设计与制造技术，设计了基于ｗｅｂ的支持机器人异地设计制造的市场客户管理系统，从而探讨了Ｂｒｏｗｓｅｒ／Ｓｅｒｖｅｒ结构的数据库发布系统的设计方法。

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.