Computing hysteresis points of nonlinear equations depending on two parameters

A direct method is described for computing a hysteresis point (double turning point) corresponding to a cusp point of a system ofn nonlinear equations inn variables depending on two parameters. By addition of two equations a minimally extended system ofn+2 nonlinear equations is constructed for which the hysteresis point is an isolated solution. An efficient implementation of Newton's method is presented not requiring evaluations of second derivatives of the original problem. Two numerical examples show the efficiency of theQ-quadratically convergent method.     

Mechanisms of Active Fuzzy Expertise

Consideration was given to the decision making procedures based on the fuzzy messages of experts whose preferences on the set of collective decisions can also be fuzzy.     

Mixed mode fatigue crack growth predictions

This work is aimed at developing a predictive capability for the quantitative assessment of crack growth under fatigue loadings. The crack growth rate relation, $\text{Δa}\text{ΔN}$, may involve all three stress intensity factors k₁-k₃ such that the direction of crack growth may not be known in advance and must be predicted from a preassumed criterion. In principle, both the stress amplitude and the mean stress level should be included in the original expression for $\text{Δa}\text{ΔN}$.The strain energy density factor range, ΔS, is found to be a convenient parameter for predicting fatigue crack growth and can be applied expediently to examine the combined influence of crack geometry, complex loadings and material properties. Assumed is the accumulation of energy, $\text{ΔW}\text{ΔV}$, stored in an element ahead of the crack which triggers subcritical crack growth upon reaching a number of loading cycle, say ΔN. The proposed $\text{δa}\text{ΔN}$ relationship includes both the stress amplitude and mean stress effects.     

Eliminating memory for fragmentation within partitionable SIMD/SPMDmachines

Efficient data layout is an important aspect of the compilation process. A model for the creation of perfect memory maps for large-scale parallel machines capable of user-controlled partitionable single-instruction-multiple data/single-program-multiple data (SIMD/SPMD) operation is developed. The term perfect implies that no memory fragmentation occurs and ensures that the memory map size is kept to a minimum. A major constraint on solving this problem is based on the single program nature of both the SIMD and SPMD modes of parallelism. It is assumed that all processors within the same submachine used identical addresses to access corresponding data items in each of their local memories. Necessary and sufficient conditions are derived for being able to create perfect memory maps, and results are applied to several partitionable interconnection networks     

Phase transformations in highly disperse powders during their rapid heating and cooling

Phase transformations in particles of ultrafine powders of graphite, hexagonal boron nitride, and quartz during rapid heating and cooling by passage through a laser beam were investigated. A continuous infrared laser with a wavelength of 10.6 μm was used as a heat source through which the powders were recycled several times. Methods of concentrating the product phases are described. Particles of diamond, carbides, cubic boron nitride, koesite and stishovite were obtained in the mixed products.     

Bounds on the number of samples needed for neural learning.

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.