Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на этот ресурс: http://dspace.opu.ua/jspui/handle/123456789/15196
Название: INTELLIGENT INFORMATION TECHNOLOGIES TO SUPPORT DECISIONMAKING WHEN APPLYING THE CAD/CAM/CAE SYSTEM OF DESIGN AND USING ADDITIVE TECHNOLOGIES
Авторы: Panda, A.
Dyadyura, K.
Smorodin, A.
Dmitrishin, D.
Antoshchuk, S.
Ключевые слова: Medical devices
3D printing
Nonlinear dynamic
Tissue engineering
Bone scaffolds
Additive manufacturing
Convolutional neural networks
Medical imaging
Artificial neural networks
Дата публикации: 2024
Библиографическое описание: Panda A. INTELLIGENT INFORMATION TECHNOLOGIES TO SUPPORT DECISIONMAKING WHEN APPLYING THE CAD/CAM/CAE SYSTEM OF DESIGN AND USING ADDITIVE TECHNOLOGIES / A. Panda, K. Dyadyura, A. Smorodin, D. Dmitrishin, S. Antoshchuk // MM Science Journal, 2024-June, 2024. - 7332-7339.
Краткий осмотр (реферат): The direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. The relevance is due to the fact that the development of an intelligent decision-making support system based on neural network modeling, the development of methods for their training, tabagato-criterion optimization of design processes, will allow the creation of three-dimensional solid models of defects taking into account their spatial structure and bone substitutes for the synthesis of biomaterials with controlled composition, porosity and mechanical strength, which are optimal for a specific area of bone replacement, which will increase the effectiveness of treatment and prosthetics in orthopedics and traumatology. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization, and training is proposed. A modification of the method of learning neural networks based on gradient descent, based on the application of the theory of nonlinear dynamics, is proposed. Corresponding theoretical provisions have been developed.
URI (Унифицированный идентификатор ресурса): http://dspace.opu.ua/jspui/handle/123456789/15196
Располагается в коллекциях:2024



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