ESTIMATING ILLUMINATION CHROMATICITY via KERNEL REGRESSION

We propose a simple nonparametric linear regression tool, known as kernel regression (KR), to estimate the illumination chromaticity. We design a Gaussian kernel whose bandwidth is selected empirically. Previously, nonlinear techniques like neural networks (NN) and support vector machines (SVM) are applied to estimate the illumination chromaticity. However, neither of the techniques was compared with linear regression tools. We show that the proposed method performs better chromaticity estimation compared to NN, SVM, and linear ridge regression (RR) approach on the same data set. Index Terms — Kernel regression, Color constancy 1

DETERMINATION OF THE OPTIMAL CONTENT OF METAL-BURDEN FOR DIFFERENT GRADE MIX OF STEELS, MELTED IN THE STEEL-MAKING FURNACES NO 3 RUP “BELORUSSIAN METALLURGICAL WORKS”

The program ''Calculation of the optimal metalburden composition" allowing to minimize the total cost of charging and of addition elements at the required output of molten metal of the predetermined chemical composition, has been developed on the basis of the offered technology

"Smart City" as an object of joint research as a part of an international interdisciplinary workshop

The article describes the experience of participation and joint organization of the Russian-British interdisciplinary workshop on the topic "Subjectification of Smart Cities" which took place at the University of Salford in June 2019. Exchange of experience between scientists from Russia and the UK specializing in BIM (Building Information Modeling) and GIS (geographical information systems) is extremely valuable not only in the field of developing solutions for the "smart city", but also in terms of the methodology ofsuch events, which ensures effective participants interactions. Intensive group work, in the framework of which the phased development of innovative projects was carried out, can be applied in other areas of educational, scientiic and practical activity