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Education Resources Center Digital Technologies
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The speed of scientific and technological progress and the disappearance of certain activities associated with the penetration of automation into all areas of production and management processes are factors of possible growth for enterprises of the future. Digital integration, which integrates scientific directions, people, processes, users and data, will create the conditions for scientific and technological advances and breakthroughs, enabling scientific and economic shifts in related industries and, above all, in the global mineral market. In this regard, in 2018, for the purpose of training, research and development in the field of digital technologies for the enterprises of mineral and fuel and energy complexes, the " Educational Research Center for Digital Technologies" was established at the Mining University.
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Scientific publications

Development of course feedback questionnaires of continuing professional education in the mining industry

Date of publication: 2019-01-01
Journal: Innovation-Based Development of the Mineral Resources Sector: Challenges and Prospects - 11th conference of the Russian-German Raw Materials, 2018
Authors: Zhukovskiy, Y.L, Koteleva, N.I, Kovalchuk, M.S.

Annotation
The presented study shows the impact of changes in the content of continuing education programs on the assessment of the quality of courses by students in the center of additional vocational education. The developed system of evaluation and input and output questionnaires made it possible to identify point changes that need to be implemented to improve the quality of both the course attendees and the customers. The presented system of impact on the quality of professional development through continuous balancing of the thematic content of the program of additional professional education allows managing the development of the competencies of employees. Point changes in the proportions of the content blocks of the programs made it possible to achieve the optimal set of topics that satisfies the needs of the customer in terms of developing certain competences in the employees, as well as the course participants themselves in terms of interest in learning and personal growth. The developed approach makes it possible for the company of the customer to introduce a system of continuous professional training, advanced training and retraining of personnel by controlling the level of competence of trained employees to improve performance.
publications

DEM Calibration Approach: Implementing Contact Model

Date of publication: 2018-07-26
Journal: Journal of Physics: Conference Series
Authors: Boikov, A.V, Savelev, R.V, Payor, V.A.
ISSN:17426596

The problem of DEM contact models choice is described in the article. An analysis of the input data calibration, including a choosing the contact model, is performed. The main theoretical positions of the most popular models are considered, their comparison is made on the basis of a specially designed stand. Description and justification of the designed stand are considered. The analysis of the obtained results is carried out. Recommendations on the choice of the DEM contact model are given. The results show that it is time-efficient to use non-linear contact model for future model calibration.
publications

Synthetic data generation for steel defect detection and classification using deep learning

Keywords:Computer vision | Machine learning | Steel defect detection | Synthetic data
Date of publication: 2021-07-01
Journal: Symmetry
Authors: Boikov, A, Payor, V, Savelev, R, Kolesnikov, A.
ISSN:20738994

Q2

(Scimago)

The paper presents a methodology for training neural networks for vision tasks on synthe-sized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.
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«Совместно с Центром цифровых технологий Санкт-Петербургского горного университета мы сотрудничаем уже несколько лет в области формирования фундаментальных и прикладных задач и идей цифровизации горной промышленности.»
«Нам очень приятно быть частью процесса, которым занимается Центр цифровых технологий Санкт-Петербургского горного университета. Мы считаем, что этот центр может являться точкой сборки всех тех новых решений, которые позволят вывести горную промышленность на новый уровень.»
Комитет по топливно-энергетическому комплексу Ленинградской области выражает Вам благодарность за поддержку в проведении Фестиваля и организацию содержательной экспозиции предприятия, нацеленной на привлечение молодого поколения к профессии ТЭК.
Благодаря Вашим усилиям мы сможем и дальше воспитывать молодежь, полную сил и устремлений к знаниям и творчеству в сфере энергосбережения.
Надеемся на дальнейшее плодотворное сотрудничество в сфере энергосбережения.
Институт проблем комплексного освоения недр, Дмитрий Клебанов
Жуков Леонид Владимирович, директор подразделения SITECH компании ООО «Цеппелин Русланд»
Комитет по топливно-энергетическому комплексу, председатель комитета Андреев Ю.В.
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