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.     

High-voltage bridge for measuring spontaneous polarization,dielectric constants,and conductivity of ferroelectric crystals

Translated from Izmeritel'naya Tekhnika, No. 9, pp. 34–35, September, 1989.     

Thermal image analysis for polygraph testing

We have designed, developed, and tested a very promising thermal image analysis method for polygraph testing. The method achieved a correct classification rate of CCR= 84% on the test population to our avail. This method, once refined, can serve as an additional channel for increasing the reliability and accuracy of traditional polygraph examination. We extract subtle facial temperature fluctuation patterns through nonlinear heat transfer modeling. The modeling transforms raw thermal data to blood flow rate information. Then, we use the slope of the average periorbital blood flow rate as the feature of a binary classification scheme. The results come to support our previous laboratory findings about the importance of periorbital blood flow in anxious states.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.     

Comparing VVR-M reactor fuel assembly heat technological capabilities

Translated from Atomnaya Énergiya, Vol. 67, No. 2, pp. 97–100, August, 1989.