AN EXPERT SYSTEM FOR SOLVENT-EXTRACTION OF RARE-EARTHS

An expert system for solvent extraction of rare earths has been developed using LISP. The goal of this project was to mimic the chemists' inferential abilities to assist in the process of solvent extraction of rare earths. The system includes frequently used extractants, separation of specific rare earths, recommendation of procedures for the separation of mixtures of rare earths using (2-ethylhexyl)phosphonic acid 2-ethylhexyl monoester, selection of parameters for counter-current extraction and methods for evaluation of the technique, and the economics of the processing. The expert system runs on an IBM-PC/XT.

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.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.