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The concept of a color gamut is widely used in applied colorimetry and printing technology. Characterization and adjustment of the color reproduction of printing devices is closely related to the uniformity of the color description throughout the body of the color gamut, as well as, to the calculation of the color difference. The difference also depends on how uniform the basis is. The CIE Lab color space is uneven. Despite the fact that many formulas for the color difference have been proposed to account its unevenness, there are areas in the gamut body that are difficult to describe. For a more complete understanding of this phenomenon, we introduce the concept of density of the Lab color space. The paper describes the concept of density and gives the first ideas about it. [ABSTRACT FROM AUTHOR]

Simulating the properties of complex alloys is an extremely challenging scientific task. The model should take into account a large number of uncorrelated factors, for many of which information may be absent or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods, and there are no general analytical models describing the effect of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical simulation tools that may account many implicit correlations and establish correspondences that cannot be identified by other, more familiar mathematical methods. However, networks require complex tuning to achieve high performance. Data engineering and data preprocessing also makes a great contribution. This paper focuses on combining deep network configuration selection based on physics and input engineering to simulate the solvus temperature of nickel superalloys. [ABSTRACT FROM AUTHOR]

Modeling the alloys properties is a nontrivial scientific and engineering task, since a large number of non- correlating factors must be taken into account in the model. The alloy composition may include from several to tens of chemical elements. The mechanical, chemical, and magnetic properties of the material, along with density, electrical conductivity etc., do not have an explicit dependence on the composition. The individual contributions of elements to certain properties have been more or less studied, however, paired, triple, etc. compositions have no common analytical models. Artificial neural networks (ANNs), as a relatively new modeling tool, make it possible to take into account many implicit correlations and establish correspondences that cannot be identified by other, more familiar mathematical methods. However, the ANNs do not always provide good modeling accuracy. The model performance depends on the selected configuration, training method, properly formed data, etc. The data engineering often do not attract enough modelers' attention, however, namely this might significantly increase the accuracy of the model while maintaining its relative simplicity and performance. This work is devoted to the combination of configuration selection and data engineering in modeling the properties of the heavily alloyed metal compositions on the example of nickel alloys. [ABSTRACT FROM AUTHOR]

Quantitative analysis of electronic texts is extremely relevant in the light of the avalanche-growing volume of information in the world. Operating with Big Data, works on text attribution, computer semantics and many other disciplines require the ability to quantify the spatial form of a text in a particular implementation. A single letter is a quantum of such calculations. It is convenient to determine the area of a letter using the properties of Bezier curves, which form all the symbols of electronic texts. In this work, we use this mathematical apparatus in matrix form to determine the area of an arbitrary symbol. [ABSTRACT FROM AUTHOR]

In light of the growing volume of textual information in the world, the quantitative analysis of texts presented in electronic form is an extremely urgent task of information technology. Quantification of the font design, spatial shape of text, etc. are relevant to problems in attribution, computational semantics, and the similar ones. Until now, no analytical definition of the letter curves perimeter that is unique for practical application has been proposed. The easiest way to do this is to use the mathematical apparatus of the Bezier curves. In this work, we use the splitted Bezier curves at the reduced dimension to define the perimeter of an arbitrary planar shape. [ABSTRACT FROM AUTHOR]

Based on heat-resistant nickel, superalloys are the basis for creating the critical parts of gas turbines that operate in extreme temperature conditions for the long periods of time. The alloys exhibit remarkable resistance to mechanical and chemical degradation under severe temperature and time loads. One of the major superalloys' mechanical properties is the rupture strength. The time to rupture that is related to the rupture strength is, also, of a particular engineering interest. In order to determine these features, particular tests are carried out. Conditions of the Western and Russian experiment differ, however, they might be united in neural model where test sample features form separately. In this work, we build a Deep Learning artificial neural network, teach it with the Western superalloys results, and predict the rupture strength and time to rupture. The results obtained and the prediction error distribution show the ability to unite different test results in one model and confirm wide prospective for the neural modeling of the superalloys properties. [ABSTRACT FROM AUTHOR]

The nickel-based superalloys are unique materials with complex alloying used in the manufacture of gas turbine engines. The alloys exhibit excellent resistance to mechanical and chemical degradation under the high loads and long-term isothermal exposures. The main service property of the alloy is its heat resistance, which is expressed by the tensile strength. Simulation of changes in the heat resistance is an important engineering problem, which would significantly simplify the design of new alloys. In this paper, we apply a deep learning neural network to predict the tensile strength values and to compare the predictive ability of the proposed approach. Also, the results are presented of the feed-forward neural network accounting changes in heat resistance vs isothermal exposures that are expressed in the complex Larson-Miller parameter. [ABSTRACT FROM AUTHOR]

Novel technology of the color reproduction is closely related to a variety of color prediction techniques. Along with the deterministic models, those based on the use of artificial neural networks have recently begun to appear. An important problem in the application of a neural network approach is the choice of the training set and network training algorithms. In our work, we describe the results of a computational experiment where various configurations of the artificial neural networks along the different volumes of training subsamples were simultaneously determined providing a satisfactory accuracy of the spectral reflection prediction. The results show that each subsample might be mapped to a particular network configuration. [ABSTRACT FROM AUTHOR]

Digital image processing requires substantial computations for characterization. It is since the reliable color reproduction can be achieved by establishing the correspondence between the spectral reflectance of the printed surface and the amounts of deposited inks. The processing is implemented by using different mathematical models. Most of the color prediction models engage some mathematical techniques to predict spectral reflectance for a mixture of colorants that are characterized by absorption and scattering during the light propagation. However, few attempts were made to make a model for prediction the colorants values based on an observing spectrum. This work is devoted to application of artificial neural network approach for solving the inverse problem of spectral reflection prediction. This task has been considered unsolvable as it involves solving a system of the linear differential equations, in which the number of unknowns exceeds the number of equations. Our attempt is based on the assumption that the prediction of the initial colorants from spectral data is possible by analogy with the work of the color perception system in humans. The aim of our study is to offer an approach to the framework selection. The model is built in Matlab and shows satisfactory prediction accuracy. [ABSTRACT FROM AUTHOR]

The nickel-based superalloys are unique materials with complex doping applied to manufacturing the gas turbine engine parts. The alloys show resistance to mechanical and chemical degradation under high pressure, high temperature, and long-term isothermal exposures. One of the main alloys’ service properties is the heat resistance. Numerically, it is expressed in the tensile strength values (MPa). Simulation of the heat resistance behavior is an important engineering task, which would significantly simplify the analysis of existing and designing the new alloys. In this paper, we use results of the heat resistance simulation by an artificial neural network, as well as, experimental data for approximating the changes in the heat resistance vs isothermal exposures expressed in the complex Larson-Miller parameter by a sigmoidal function. [ABSTRACT FROM AUTHOR]

Whiteness is one of the most important characteristics of a printing paper. There is a stable relationship between the printed and optical properties of the paper. At the same time, the understanding of whiteness in the paper-making and printing industries is significantly different. Such miscommunication negatively affects the quality of printed products, since the choice of a suitable paper (or cardboard) is difficult to a certain extent. One of the ways to get out of this situation is to create a simple and clear computational model of the white paper, which would not depend on any physical standards. The model would help when conducting the input control in printing plants and would allow one to establish communication between industries. This work is devoted to the description of such a model. [ABSTRACT FROM AUTHOR]

Accuracy of the color reproduction during printing is strongly determined by the fine tuning of the gradation curves of the printing systems (tone reproduction curves). The curves are constructed by sequentially changing the tone of the main dyes (most often it is CMYK). However, this approach does not take into account the effect of shading changes when initial colorants overlap. Moreover, the traditional method does not take into account the nonlinearity of the tone increment in the areas of high and low densities. We have proposed an alternative color correction model based on the gradation trajectories as an analogue of gradation curves in the CIE Lab space. The method is devoid of the main drawbacks of the common approach. The authors have previously described the approach, its extension to double overlays of colors (gradation surfaces), as well as its analytical and discrete implications. In this paper, we propose to apply the gradation trajectories as geodesic lines of double overlaps to determine the color of the “true” initial colorants, which are free of nonlinearities in the high density domain. An experimental test was performed using an electrophotographic printer. [ABSTRACT FROM AUTHOR]