Under the current conditions of market economy a higher education institution must increase considerably the efficiency of its educational activities control to improve its competitive ability. At present one of the most promising methods for educational process control reorganization is a business-process re-engineering. It is necessary to carry out a profound analysis of technologies for educational activities control and determine certain business-processes which require re-engineering. In the Bryansk State Technical University there is carried out a re-engineering of basic business processes of the admission campaign: a school leaver invitation, documentation reception, entrance examina-tions carrying out and their analysis, an enrollment for training. To solve this problem there was used IDEF0 methodology and developed a functional model mani-festing a structure and functions of an information sys-tem, and also information flows connecting these func-tions. A decomposition of a complex system into more simple automated processes and sub-processes in order that each of them could be designed independently is carried out. The work fulfilled allowed realizing the re-engineering and automation of the admission campaign business-processes of the Bryansk State Technical University and ordering qualitatively the activities of the entrance commission that decreased labor expendi-tures at all stages of the admission campaign increased the effectiveness of the entrance commission activities and allowed making qualitative decisions affecting institution functioning on the whole.

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Сформированы и обоснованы требования к визуальному интерфейсу информационного про-блемно-ориентированного веб-сайта «БГТУ -Абитуриент». Рассмотрены решения, которые бы-ли положены в основу разработки сайта с целью обеспечения указанных требований.Ключевые слова: приемная комиссия, аби-туриент, веб-сайт, визуальный интерфейс, цветовая гамма, макетирование. FEATURES OF DESIGN OF THE VISUAL INTERFACE FOR THE WEBSITE "BGTU-ENTRANT"Support of efficiency and high quality of a set of entrants is among the priority tasks facing the Bryansk state technical university. In the modern conditions one of the main sources of information on higher education institution for entrants is the official web site of higher education institution. The qualitative website having the modern interface providing a possibility of comfortable viewing from any, including mobile devices, containing exhaustive information and realizing the thought-over system of navigation according to documents will promote increase in interest in higher education institution from potential entrants and, as a result, to increase in efficiency and quality of a set. In article requirements to the visual interface of the web site "BGTU -the Entrant" are created and decisions which were the basis for development of the website for the purpose of support of the specified requirements are described. Features of color design of the interface which main objective is creation of such color gamma which, on the one hand, shall attract users are considered, and on the other hand -to be memorable. Detailed reasons for approaches to bread boarding of the pages of the website considering features of submission of information connected to operation of selection committee are also given.

The article discusses one of the latest ways to colorize a black and white image using deep learning methods. For colorization, a convolutional neural network with a large number of layers (Deep convolutional) is used, the architecture of which includes a ResNet model. This model was pre-trained on images of the ImageNet dataset. A neural network receives a black and white image and returns a colorized color. Since, due to the characteristics of ResNet, an input multiple of 255 is received, a program was written that, using frames, enlarges the image for the required size. During the operation of the neural network, the CIE Lab color model is used, which allows to separate the black and white component of the image from the color. For training the neural network, the Place 365 dataset was used, containing 365 different classes, such as animals, landscape elements, people, and so on. The training was carried out on the Nvidia GTX 1080 video card. The result was a trained neural network capable of colorizing images of any size and format. As example we had a speed of 0.08 seconds and an image of 256 by 256 pixels in size. In connection with the concept of the dataset used for training, the resulting model is focused on the recognition of natural landscapes and urban areas.

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