We studied the thermal diffusion behavior of mixtures of benzene and heptane isomers by reverse nonequilibrium molecular dynamics. For n-heptane/benzene mixtures, we investigated the concentration ...dependence of the Soret coefficient. The Soret coefficient for equimolar mixtures of the three heptane isomers 3-methylhexane, 2,3-dimethylpentane, and 2,4-dimethylpentane in benzene has been calculated. Compared to the experimental data, the simulation results show the same trend in dependence of the mole fraction and degree of branching. The negative Soret coefficient indicates the enrichment of alkanes in the warm side. In the case of the heptane isomers in benzene, we could study the influence of the difference in shape and size on the thermal diffusion behavior at constant mass. In the simulation as well as in the experiment, we found that the Soret coefficients become higher with increasing degree of branching. Such behavior cannot be explained only by mass and size effects. The effect of the molecular shape needs to be considered additionally.
Factor analysis is used in a number of applications. One example is image recognition, where it is often necessary to learn representations of the underlying components of images, such as objects, ...object-parts, or features. Another example is data compression when original data is transformed into a space of lower dimension. The goal of factor analysis is to find the underlying factors (factor loadings) and the contributions of these factors into the original observations (factor scores).
Recently, we have proposed the method of Boolean factor analysis based on the ability of the Hopfield-like network to create attractors for factors
19. It shows that an obstacle to using this network for Boolean factor analysis is the appearance of two global spurious attractors that have no relation to internal structure of analyzed signals. To eliminate these attractors we had to modify the common architecture of Hopfield network, adding a special inhibitory neuron. The existence of two global attractors and their elimination by the special inhibitory neuron were illustrated by Frolov et al.
19 only by some computer simulations. Since the appearance of those attractors is a novel important phenomenon, in this paper we investigate it both analytically and by additional computer simulations, to prove its validity, and explain its origin.
A balanced simulation model of stand-alone power supply project based on solar-wind generation and a hybrid energy storage consisting of a lithium-ion battery and a hydrogen energy storage with ...storage in the form of compressed gas is presented. The control system ensures the balance reliability of the power supply. The model is designed for the design of elements and systems of stand-alone power supply, including process control systems. To model and synthesize the control system of an object, the project is divided into subsystems and elements in order to take into account the mutual influence of their relationships on the properties of the control object as a whole. The subsystems do not reflect the unevenness of the transient modes of the equipment. This approach assume that the resulting annual balance is determined mainly by the established operating modes of the equipment. The initial data on the intensity of solar radiation, wind speed and temperature are hourly average data for the calendar year for the city of Moscow, as well as a typical daily load schedule for industrial enterprises in single-shift operation. Modeling is carried out in Matlab Simulink using the Simscape multiphysical modeling library. The simulation results reflect the implementation of power balancing based on the proposed calendar plan for seasonal energy storage, as well as quantitative and qualitative characteristics of the power supply project.
Learning of objects from complex patterns is a long-term challenge in philosophy, neuroscience, machine learning, data mining, and in statistics. There are some approaches in literature trying to ...solve this difficult task consisting in discovering hidden structure of high-dimensional binary data and one of them is Boolean factor analysis. However there is no expert independent measure for evaluating this method in terms of the quality of solutions obtained, when analyzing unknown data. Here we propose information gain, model-based measure of the rate of success of individual methods. This measure presupposes that observed signals arise as Boolean superposition of base signals with noise. For the case whereby a method does not provide parameters necessary for information gain calculation we introduce the procedure for their estimation. Using an extended version of the ”Bars Problem” generation of typical synthetics data for such a task, we show that our measure is sensitive to all types of data model parameters and attains its maximum, when best fit is achieved.
PDF
