Кафедра авіаційних комп'ютерно-інтегрованих комплексів (НОВА)
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Відповідальний за розділ: Провідний фахівець кафедри авіаційних комп'ютерно-інтегрованих комплексів інституту інформаційно-діагностичних систем Шугалєй Людмила Петрівна. E-mail: shugaley2005@ukr.net
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Browsing Кафедра авіаційних комп'ютерно-інтегрованих комплексів (НОВА) by Subject "004.032.26(045)"
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Item A Comprehensive Framework for Underwater Object Detection Based on Improved YOLOv8(National Aviation University, 2024-03-29) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Savchenko, Mykhailo; Савченко, Михайло ВолодимировичUnderwater object detection poses unique challenges due to issues such as poor visibility, small densely packed objects, and target occlusion. In this paper, we propose a comprehensive framework for underwater object detection based on improved YOLOv8, addressing these challenges and achieving superior performance. Our framework integrates several key enhancements including Contrast Limited Adaptive Histogram Equalization for image preprocessing, a lightweight GhostNetV2 backbone, Coordinate Attention mechanism, and Deformable ConvNets v4 for improved feature representation. Through experimentation on the UTDAC2020 dataset, our model achieves 82.35% precision, 80.98 % recall, and 86.21 % mean average precision at IoU = 0.5. Notably, our framework outperforms the YOLOv8s model by a significant margin, while also being 15.1% smaller in terms of computational complexity. These results underscore the efficiency of our proposed framework for underwater object detection tasks, demonstrating its potential for real-world applications in underwater environments.Item Automated Camouflage Design Based on Artificial Intelligence(National Aviation University, 2024-06-28) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Bashenko, M. O.; Башенко, Микита ОлександровичThe article discusses the development of a camouflage uniform production system for civilian use with an emphasis on survival and hunting. Effective camouflage requires precise reproduction of the colors, textures, and patterns of specific landscapes, increasing hunting success and unnoticed movement. The technological process of fabric dyeing, necessary for the production of high-quality camouflage uniforms, includes fabric preparation, dyeing and strict quality control.. Fabric preparation includes cleaning, soaking, bleaching, and mercerization to ensure uniform dye absorption and durability. Dyeing methods vary by fabric type, with reactive dyes for natural fibers and disperse dyes for synthetics. Quality control includes visual inspections and tests for colorfastness under various conditions. Advanced dyeing techniques such as continuous dyeing, spray dyeing, stencil dyeing and digital printing have been analyzed to offer certain advantages. Machines like the Mimaki TX300P handle various fabric widths with high precision and reliability, enhancing efficiency. Automation using the Mimaki TX300P streamlines the dyeing process, optimizing ink consumption and integrating fabric loading, printing, and cutting systems. A customer relationship management system further automates garment creation, enhancing design, order management, and quality control. Tools like CLO3D enable detailed 3D modeling and accurate pattern reproduction. The customer relationship management system coordinates production stages and provides precise paint usage recommendations, ensuring efficient resource management and high-quality outcomes. In conclusion, developing and automating fabric dyeing processes for camouflage uniforms involve advanced technologies and meticulous quality control, ensuring durable, colorfast camouflage clothing that blends effectively into natural environments for civilian use.Item Hybrid neural network optimization system based on ant algorithms(National Aviation University, 2020-07-06) Sineglazov, Victor; Chumachenko, Olena; Omelchenko, Dmytro; Синєглазов, Віктор Михайлович; Чумаченко, Олена Іллівна; Омельченко, Дмитро ВалерійовичThe ant multi-criteria algorithm for feed forward neural networks training is proposed. It is used two criteria: the error of generalization and complexity. It is represented a review of neural network learning using swarm algorithms. As a result of training it is determined a structure of neural network (a number of layers and neurons in then) and the values of weight coefficients and biases. Modification of well-known algorithms consists in using the concept of Pareto optimality. It is done the research of proposed algorithm on the example of multilayer perceptron for the approximation problem solution.Item Intelligent System of Generation of Camouflage Patterns Based on Artificial Intelligence Technologies(National Aviation University, 2024-06-28) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Nikulin, Dmytro; Нікулін, Дмитро ОлеговичThe work is devoted to the development of an intelligent system for generating camouflage patterns based on artificial intelligence technologies. A generative-competitive network is used as an intellectual element of this system. To solve the problem of the collapse mode, the architecture of progressively growing GANs (ProGAN) is used. The system allows you to generate completely new camouflage patterns for the selected area by iteratively improving the pattern. Due to the mechanism of restrictions, it is possible to fix the desired aspects of the drawing (color scheme, pattern, number of colors) from an existing drawing and adapt it to the desired area. The system provides the possibility of generating micropatterns on the drawings to improve camouflage at close distances. When evaluating a camouflage pattern, the system takes into account additional parameters, such as angle (from the ground and air), time and weather.Item Structural-parametric Synthesis of Capsule Neural Networks(National Aviation University, 2023-12-27) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Kudriev, Denys; Кудрєв, Денис ОлексійовичThis work is dedicated to the structural-parametric synthesis of capsule neural networks. A methodology for structural-parametric synthesis of capsule neural networks has been developed, which includes the following algorithms: determining the most influential parameters of the capsule neural network, a hybrid machine learning algorithm. Using the hybrid algorithm, the optimal structure and values of weight coefficients are determined. The hybrid algorithm consists of a genetic algorithm and a gradient algorithm (Adam). 150 topologies of capsule neural networks were evaluated, with an average evaluation time of one generation taking 10 hours. Chromosomes and weights are stored in the generation folder. The chromosome storage format is JSON, using the jsonpickle library for writing. Also, when forming a new generation, chromosome files from previous generations are used as a "cache". If a chromosome of the same structure exists, the accuracy is assigned immediately to avoid unnecessary training of neural networks. As a result of using the hybrid algorithm, the optimal topology and parameters of the capsule neural network for classification tasks have been found.