On the basis of introducing the Fibonacci computing in computer algebra system of Maple. Firstly,we described a fast computing algorithm of large Fibonacci number. Furthermore,by using Lucas series instead of Fibonacci series,we put forward the advanced Fibonacci searching method applied in optimization theory.

Secondly, we propose a corrected Randomized Insertion Algorithm for Delaunay Triangulation and a speedily computing algorithm of grid\|index which may increase efficiency.

We studied the realization of the optimization mapping of the wavefront computing algorithm from the representive layer to the architecture layer. Two SIMD based parallel algorithms, the doubling algorithm and the partitioning algorithm are proposed according to the existing parallel computing theory.

For the task scheduling strategy, the Min-min algorithm is modified and the Trust-Min-min which enhances the validity of grid computing algorithm is proposed.

A multi-stage algorithm is a computing algorithm consisting of a sequence of nested loop constructs to be executed sequentially.

This study proposes a software quality evaluation model and its computing algorithm.

The computing algorithm may be realized on a digital computer.

Consideration is given to the working principle of the primary transducer, as the chief unit in the device, and to a computing algorithm for calculating the essential arterial pressure parameters as a systole and diastole in a digital artery.

A computing algorithm, based on the geometry of certain reachable sets, is presented for fixed terminal time optimal regular problems having differential equations

The separation of water(up to 0.5m%)and components lighter than n- pentane from crude oil by distillation method is a newly developed technique of crude oil stabilization.The light hydrocarbon fraction recovered is used as the feed to the ethylene plant located close to the oil field. This stabilizer has some characteristic features distinct from conventional fractionating columns:(1)The feed has a very wide boiling point range,(2) Water-rich phase is formed in rectifying section,and results a three-phase distillation...

The separation of water(up to 0.5m%)and components lighter than n- pentane from crude oil by distillation method is a newly developed technique of crude oil stabilization.The light hydrocarbon fraction recovered is used as the feed to the ethylene plant located close to the oil field. This stabilizer has some characteristic features distinct from conventional fractionating columns:(1)The feed has a very wide boiling point range,(2) Water-rich phase is formed in rectifying section,and results a three-phase distillation zone,(3)Multi-side withdrawls of water-rich phase and multi- intermediate reboilers are used.The current available two-phase distillation computing algorithms are not applicable to such a complex system. For solving this problem,the author has developed a mathematical model for describing the three-phase flash and distillation of water-containing crude oil mixtures,and rigorous iteration procedures proposed. The crude oil was cut into 18-27 fractions or components(including defined and pseudocomponents),the critical constants and acentric factors of pseudo- components were estimated by Cavett or Kesler-Lee Correlation.PR and SRK models were selected for performing phase equilibria and enthalpy calculations. The proposed stage-to-stage computing algorithm was based on the flash calculation of each stage,which can automatically determine whether a water- rich phase will be splitted under the given conditions.The initial composition profiles assumed has significant effect on the convergence of iteration calcul- ation,the author has proposed simple and effective methods for setting the initial profiles.For accelerating the computing process,an on-line regression technique was employed.The rigorous K-values obtained from the second or third iteration cycle were fitted to a polynomial function of stage tempera- ture,and with this simplified function applied to the succedent calculations in two-phase zone. The computer program TPDS developed in this work has been successfully applied to the design and case studies of crude oil stabilizers in Daqing oil field.With slight modification,this program could be applied to other three- phase distillation systems.

Similar to Claerbout' s idea, it is possible to transform the counterpart of the full wave equation in ray theory, the Eikonal equation into the form of the upgoing and/or downgoing wave equations. By application of the Engquist-Osher scheme to the transformed conservative partial differential equation, we can compute the seismic travel times of any point in a mesh with any velocity structure. The paper proves that, the flux in the conservative partial differential equation 'is a convex function. The monotone...

Similar to Claerbout' s idea, it is possible to transform the counterpart of the full wave equation in ray theory, the Eikonal equation into the form of the upgoing and/or downgoing wave equations. By application of the Engquist-Osher scheme to the transformed conservative partial differential equation, we can compute the seismic travel times of any point in a mesh with any velocity structure. The paper proves that, the flux in the conservative partial differential equation 'is a convex function. The monotone conservative finite difference scheme with first-order accuracy we used here can satisfy the accuracy demands. Its computing speed is much faster than any other travel time computing algorithms currently used. And it is vectorizable.

Fuzzy set theory is adopted in a new neural computing algorithm-fuzzy neural' computing. It has been proved that the new algorithm gets rid of two fatal defects facing most of the existing neural computing algorithms, known as the slow training speed and network parameter sensibility. It is well suited to general pattern classification tasks. Application to the partitioning of electrical circuits show that Fuzzy neural computing is superior to the Kohonen's self-organizing neural...

Fuzzy set theory is adopted in a new neural computing algorithm-fuzzy neural' computing. It has been proved that the new algorithm gets rid of two fatal defects facing most of the existing neural computing algorithms, known as the slow training speed and network parameter sensibility. It is well suited to general pattern classification tasks. Application to the partitioning of electrical circuits show that Fuzzy neural computing is superior to the Kohonen's self-organizing neural computing both in the sense of training speed and final results.