Electrical Load Variations Research Articles

Microfibrosis produces electrical load variations due to loss of side-to-side cell connections: a major mechanism of structural heart disease arrhythmias.

The purpose of this article is to demonstrate how adaptive changes in myocardial microstructure provide mechanisms for emergent new conduction disturbances that initiate reentrant arrhythmias. The mechanisms are based on discontinuous conduction phenomena produced by increases in cellular loading; these increases result from changes in the normal distribution of the gap junctions. Recent studies that at a microscopic level propagation in normal mature cardiac muscle is stochastic. For example, the nonuniform and irregular distribution of the gap junctions in such normal muscle produces load variations that are associated with changes in Vmax inside individual cells during both longitudinal and transverse propagation. The stochastic nature of normal propagation at a microscopic level offers considerable protection against arrhythmias by reestablishing the general trend of wavefront movement after small variations in excitation events occur. If such microscopic diversity is decreased, large fluctuations in load develop that are distributed over more cells than usual. The decrease in diversity may be caused by loss of side-to-side coupling between fibers, which produces relatively isolated groups of cells with microfibrosis. With loss of side-to-side fiber coupling, the myocardial architecture may fail to reestablish a smoothed wavefront at the macroscopic level. Spatial nonuniformities of electrical loading then give rise to conduction block and reentry.

Application of neural networks to load-frequency control in power systems

This paper describes an application of layered neural networks to nonlinear power systems control. A single generator unit feeds a power line to various users whose power demand can vary over time. As a consequence of load variations, the frequency of the generator changes over time. A feedforward neural network is trained to control the steam admission valve of the turbine that drives the generator, thereby restoring the frequency to its nominal value. Frequency transients are minimized and zero steady-state error is obtained. The same technique is then applied to control a system composed of two single units tied together through a power line. Electric load variations can happen independently in both units. Both neural controllers are trained with the back propagation-through-time algorithm. Use of a neural network to model the dynamic system is avoided by introducing the Jacobian matrices of the system in the back propagation chain used in controller training.

An Ultrasonic Shutter

The nearly total transmission of a sound wave through a plate having a thickness of an even number of quarter-wavelengths, and the nearly total reflection from the plate when the thickness is an odd number of quarter-waves, was established mathematically by Rayleigh nearly a century ago. This basic theory becomes complex when applied to some types of piezoelectric plates with their combined electrical and mechanical resonant and antiresonant frequencies. A simplified electromechanical analogy of this type plate is presented to explain how the introduction of a variable electrical load can control the frequency of optimum sound transmission through the plate by varying its effective mechanical impedance. The use of a plate of barium titanate (resonant at 1100 kilocycles) in a water channel demonstrates how the intensity of a sound beam may be controlled over a 40-decibel range by the use of a variable inductance across the plate electrodes. A carbon microphone wired directly to the plate electrodes and affording only passive control permits amplitude modulation of the sound carrier passing through the plate.