Method First Approximation Stability Analysis of Electrical Control Systems
( Pp. 52-56)
More about authors
Artemyev Viktor S.
senior lecturer, Department of Computer Science
Plekhanov Russian University of Economics
Moscow, Russian Federation Mokrova Nataliуa V. Dr. Sci. (Eng.); professor, Department of Info-communication Technologies
National Research Technological University “MISiS”
Moscow, Russian Federation
Plekhanov Russian University of Economics
Moscow, Russian Federation Mokrova Nataliуa V. Dr. Sci. (Eng.); professor, Department of Info-communication Technologies
National Research Technological University “MISiS”
Moscow, Russian Federation
Abstract:
This article is devoted to the research and analysis of automated control systems and control of electrical equipment of technological processes of agricultural production. The first approximation method is used for evaluation the stability of the operation of electric drive control systems. Methods for assessing the stability zone of electric drive control systems, determining critical gain coefficients, and optimizing the parameters of electrical circuits included systems, in order to increase the efficiency and reliability of production chains are proposed. To solve the problem of controlling the electric drives of automated systems for harvesting and sorting agricultural crops, the method was tested, a critical gain value of 3.2 was obtained, which allows us to talk about optimizing such systems in terms of speed and load.
How to Cite:
Artemyev V.S., Mokrova N.V. Method First Approximation Stability Analysis of Electrical Control Systems. Computational Nanotechnology. 2024. Vol. 11. No. 3. Pp. 52–56. (In Rus.). DOI: 10.33693/2313-223X-2024-11-3-52-56. EDN: QGSYPS
Reference list:
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Годунов А.И., Баланян С.Т., Егоров П.С. Сегментация изображений и распознавание объектов на основе технологии сверточных нейронных сетей // Надежность и качество сложных систем. 2021. № 3 (35). С. 62–73.
Луценко В.С., Шухман А.Е. Сегментация медицинских изображений сверточными нейронными сетями // Вестник компьютерных и информационных технологий. 2022. № 6 (216): С. 40–50.
Binjun He Wenbin Hu. Image segmentation algorithm of lung cancer based on neural network model // Expert Systems. 2021. No. 39. Pp. 145–152.
Srinitya G., Sharmila D. Certain investigations on image segmentation algorithms on synthetic aperture radar images and classification using convolution neural network // Concurrency and Computation: Practice and Experience. 2021. No. 34. Pp. 73–79.
Kai Su, Xin Zhang. Convolutional neural network based image segmentation algorithm for dual-layer LCDs // SID Symposium Digest of Technical Papers. 2022. No. 53. Pp. 110–119.
Березовский И.И. Обзор сверточных нейронных сетей для сегментации медицинских изображений // Трибуна ученого. 2022. № 6. С. 59–67.
Ложкин И.А. Аугментация наборов изображений для обучения нейронных сетей при решении задач семантической сегментации // International Journal of Open Information Technologies. 2023. № 1. С. 109–117.
Xiao Qing Zhang, Guang Yu Wang. COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation // International Journal of Imaging Systems and Technology. 2021. No. 31. Pp. 38–46.
Aarthi Sundaram, Chitrakala Sakthivel. Object detection and estimation: A hybrid image segmentation technique using convolutional neural network model // Concurrency and Computation: Practice and Experience. 2022. No. 34. Pp. 65–73.
Yuma Hakumura, Taiyo Ito. Loss function for ambiguous boundaries for deep neural network (DNN) for image segmentation // Electronics and Communications in Japan. 2023. No. 4. Pp. 149–153.
Михайлов А.А. Автоматическая разметка данных для сегментации изображений документов с использованием глубоких нейронных сетей // Труды Института системного программирования РАН. 2022. № 6. С. 137–146.
Катаев М.Ю., Карташов Е.Ю., Рябухин В.В. и др. Методика сегментации изображений беспилотных летательных аппаратов с помощью нейронных сетей // Современные проблемы дистанционного зондирования Земли из космоса. 2023. № 1. С. 55–66.
Jie Yang, Yong Chen. Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa // Pest Management Science. 2024. No. 56. Pp. 91–99.
Keywords:
first approximation method, electrical equipment of agricultural production, stability, automated systems.
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