нейронна мережа

PERFORMANCE ANALYSIS OF CNN-ENHANCED GENETIC ALGORITHM FOR TOPOLOGICAL OPTIMIZATION IN METAMATERIAL DESIGN

The Combination of Convolutional Neural Networks (CNN) and Genetic Algorithms (GA) provides a promising approach for topological optimization of complex lattice structures. Lattice structures are commonly used as base in the design of high-performance metamaterials. This paper presents a review of the effectiveness and efficiency of the CNN-GA method. We will examine the ability of the method to generate optimal complex structures while minimizing material usage. CNN is utilized mainly as an analysis instrument.

Comparative Analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Panels

The growing demand for electricity and the need for environmentally friendly energy sources are driving the active development of renewable technologies, with solar energy playing a leading role. Photovoltaic (PV) systems are capable of converting solar radiation into electrical energy; however, their efficiency depends on the ability to adapt to changing external conditions, such as solar irradiance and ambient temperature.

Optimization of the Algorithm Flow Graph Width in Neural Networks to Reduce the Use of Processor Elements on Single-board Computers

The article presents a method for optimizing the algorithm flow graph of a deep neural network to reduce the number of processor elements (PE) required for executing the algorithm on single-board computers. The proposed approach is based on the use of a structural matrix to optimize the neural network architecture without loss of performance. The research demonstrated that by reducing the width of the graph, the number of processor elements was reduced from 3 to 2, while maintaining network performance at 75% efficiency.

Intelligent Fake News Prediction System Based on NLP and Machine Learning Technologies

The article describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.

GENERATION AND RECOGNITION OF FRACTAL CAMOUFLAGE STRUCTURES USING NEURAL NETWORKS

The paper considers a method of generating fractal camouflage structures (grids) using a randomized system of iterative functions. This method allows for changing the base structure (type of mesh), which in turn makes it possible to determine the parameters by which the object can be identified as a fractal camouflage mesh. In the mathematical description of the improved RSIF, the color range parameters (set of colors) are introduced, allowing the fractal structure to be adjusted to the colors of the landscape where the camouflage net will be applied.

Evaluation of Classification Accuracy Using Feedforward Neural Network for Dynamic Objects

This paper investigates the impact of the number of hidden layers, the number of neurons in these layers, and the types of activation functions on the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing); A/RC – (armor-piercing- incendiary); M – (missile); R – (armor-piercing shells)) using a multi-layer neural network, evaluated by a confusion matrix.

Оbject recognition system based on the Yolo model and database formation

A system for recognizing objects that are captured in real time on a video camera in a noisy environment that changes to the surrounding conditions has been built. The method of filling the database for mobile military objects was studied. For object recognition, the YOLO v8 neural network is used, which allows you to track moving and identify objects that fall into the video from the video camera. This neural network makes it possible to track objects with a change in scale, during movement with obstacles.

PRECONDITIONS FOR THE CREATION OF A MEAT FRESHNESS CONTROL AND IDENTIFICATION SYSTEM

The relevance of creating a comprehensive system for meat control and identification to determine its freshness level has been demonstrated in the study. The drawbacks of traditional organoleptic and laboratory methods commonly used for meat inspection were analyzed. The authors presented the advantages and challenges of employing an electronic nose. A design for a meat control and identification system was proposed, which includes an Arduino Uno microcontroller, Raspberry Pi, USB to TTL adapter, gas sensors, color sensor, thermal camera, and image sensor.

Method of building embeddings of signs in deep learning problems based on ontologies

This paper investigates the problem of embedding features used in datasets for training neural networks. The use of embeddings increases the performance of neural networks, and therefore is an important part of data preparation for deep learning methods. Such a process is based on semantic metrics. It is proposed to use ontologies of the subject areas to which the corresponding feature belongs for embedding. This work developed such a method and investigated its use for the task of categorizing text documents. The research results showed the advantage of the developed method.

Design of the system of automated generation of poetry works

 Features of designing a system of automated generation of poetic works, which opens up new opportunities for artistic speech and show business, especially the preparation of poems and songs have been considered. Quite often lyrics without special content become successful due to the lack of complex plots, as well as due to the unobtrusiveness and ease of perception by listeners. Well-known literature sources and available software products that can generate poetic works by combining different methods and algorithms are analyzed.