Мікросистеми, Електроніка та Акустика: науково-технічний журнал, Т. 28, № 3(125)
Постійне посилання зібрання
Переглянути
Нові надходження
Документ Відкритий доступ Generation of Anisotropic Cloud Cover(КПІ ім. Ігоря Сікорського, 2023) Martyniuk, V. I.; Zhuikov, V. Ya.This paper introduces an advanced mathematical model for generating and analyzing cloud cover images, specifically designed to enhance photovoltaic (PV) partial shading studies. The model development involved a detailed analysis of real cloud cover images, with a particular emphasis on capturing their anisotropic spectral characteristics. This was achieved through a combination of spectral analysis and advanced image processing techniques. The research methodologically focused on developing a four-parameter model to accurately represent cloud formations' spectral properties. Key parameters were identified and fine-tuned to match the real cloud formations' characteristics. This involved analyzing the magnitude and phase spectra of the cloud covers and fitting them to a model capable of replicating these properties accurately. A significant part of the research was dedicated to formulating a novel phase spectrum generation technique. This technique was specifically designed to control the degree of similarity between the synthesized and original images, thereby ensuring the model's effectiveness in various simulation scenarios. The process involved manipulating the phase information of cloud cover images while maintaining their high-frequency components to enhance the detail and realism of the synthesized images. The model's accuracy in replicating cloud cover features was tested against traditional spectral synthesis methods. This comparative analysis involved generating cloud cover images using the developed model and established methods, then comparing these images to the original cloud covers in terms of visual similarity and approximation error. Additionally, the model was utilized to generate pseudo-random cloud cover images by varying the phase spectrum parameters. This process ensured that the generated images, while being random, adhered to the spectral characteristics of the original cloud covers. The research methodology also involved a detailed examination of the images' key characteristics, such as direction, length, and density, to ensure fidelity to the original samples. In summary, this paper details an approach to cloud cover image synthesis, with a focus on the accuracy of spectral properties and the development of an algorithm of model parameters estimation. The research highlights the use of advanced spectral analysis and image processing techniques in deriving key model parameters, leading to a significant advancement in cloud imaging for solar energy applications.Документ Відкритий доступ The Impact of Wind Power and Load Power Fluctuations on Energy Storage Sizing(КПІ ім. Ігоря Сікорського, 2023) Yaremenko, M. K.; Klen, K. S.The study presents a method of taking into account the impact of wind power and load power fluctuations on the energy storage sizing, comprised of batteries of identical capacity. To account the impact, two methods of calculating the difference between wind power generation and load consumption were presented over some time interval: 1st and 2nd order difference methods. Each of the methods can be parameterized and non-parameterized method with and without taking into account parameters respectively, where the parameters are: discharge current, required discharge duration, depth of discharge, battery capacity, Peukert’s constant, discharge time from 100% capacity, ambient temperature and wind power prediction error. Using the parameterized method compared allows to refine the value of the number of batteries. Using the 2nd order difference method compared to the 1st order difference method can significantly reduce the required number of batteries.Документ Відкритий доступ Using Information about Experimental Conditions to Predict Properties of Metamaterials(КПІ ім. Ігоря Сікорського, 2023) Krysenko, P. I.; Zoziuk, M. O.In this work, a method of increasing the amount of data for training neural networks is proposed using the possibility of using information about the experimental conditions of measuring the properties of metamaterials. It is shown that the method is flexible and effective. The results of predicting the transmission coefficient of the metamaterial for different angles of incidence of radiation and type of polarization are presented. Using the architecture presented in the work, a high rate of learning and generation of new data was obtained with an error that does not exceed 12% for experiments in one frequency range and does not exceed 31% if all experiments are used for training. The architecture of the neural network and the method by which it is possible to easily change the number and types of experimental conditions are presented.Документ Відкритий доступ Human Eye Aberrometry Data Generation Using Generative Adversarial Neural Network(КПІ ім. Ігоря Сікорського, 2023) Yaroshenko, M. O.It’s obvious that for development and improvement of methods and apparatus for diagnosis and treatment of optical flaws of human eye at the modelling stage, it’s necessary to have sets of real measurements. However, data requests to clinics are accompanied by substantial amount of bureaucracy procedures and, at the same time, acquired dataset may be too small, which can be critical, for example, for training of neural networks. According to the analysis of existing publications, publicly available datasets of aberrometry data (sets of eye’s refractive flaws) are rare and consist of relatively low number of measurements. But, due to current development state of neural networks, it is possible to generate data based on real measurements. The most common solutions are methods based on the usage of the Generative Adversarial Networks (GAN). This tendency is also relevant for the modern ophthalmology, but no publications aimed at aberrometry data synthesis were found. For this reason, objective of this work is development of solution for generation of sets of human eye’s refractive errors using neural networks. Proposed solution includes generator and critic networks trained according to the Wasserstein GAN with Gradient Penalty (WGAN GP) algorithm. In order to improve training, the method of data augmentation called Data Augmentation Optimized for GAN (DAG) was used, moreover, the possibility of augmentation of aberrometry data in two forms was implemented — for both Zernike coefficient vectors and wavefront pixel images. According to the result’s evaluation, generated data has the distribution close to the real sample (Fréchet distance equals 0.7) and, at the same time, it is neither a copy of real measurements (92% creativity rate) nor duplication of a few aberration sets (diversity metric equals 3.64 which is close to the optimal 3.83). The direction of further improvement includes enhancement of existing architectures of generator and critic, search or creation of bigger training dataset and refinement of data augmentation technics.Документ Відкритий доступ A Model of a Free-Space Optical Communication Line with a Smart Reflector(КПІ ім. Ігоря Сікорського, 2023) Melnyk, I. I.; Chadyuk, V. O.The article considers a model of a free-space communication line between several closely located points on the terrain that are outside the line of sight. For the rapid deployment of communication lines with a range of up to hundreds of meters, any tall building visible to all callers can be a kind of optical signal repeater. Compared to radio communication channels, an optical communication line has a higher speed of information transmission, is insensitive to electromagnetic interference and is more protected from eavesdropping. To increase the range of the communication line and increase the signal to noise ratio, it is proposed to use a smart reflector based on the mirror of the two-axis scanner, the angular position of which is controlled by the microcontroller. The microcontroller receives information about the angle of incidence of the laser beam on the smart reflector in the form of photoelectric signals. These signals are formed by a position-sensitive photodetector and a lens in front of it. Depending on the angle of incidence of the laser beam, the position of the laser spot focused by the lens on the photodetector changes, and with it, both signals at its output. To facilitate the search of the smart reflector by transceivers, it is suggested to use an LED beacon in it.Документ Відкритий доступ Research on the Dynamic Range of Silicon Photodiodes for Optical Pyrometry Applications(КПІ ім. Ігоря Сікорського, 2023) Verbitskyi, D. O.; Voronko, A. O.Optical pyrometry is one of the main non-contact methods for precise temperature measurement of semiconductor wafers for vapour-phase epitaxy from metal-organic compounds (MOCVD). The requirements for the photocell of the pyrometer are due to the peculiarity of the process. In the pyrometer, the silicon photodiode operates in a mode that is characterized by a small bias voltage value, high sensitivity to weak light radiation, and low noise level. The main temperatures used in vapour-phase epitaxy technology depend on the semiconductor material being grown and the process parameters. Typically, process temperatures range from 500 to 1200 °C. A study of the dynamic range of a silicon photodiode for use in optical pyrometry was conducted. It was established that the minimum value of the dark current and the maximum value of the spectral sensitivity are key to obtaining the desired characteristics, namely, sensitivity to thermal radiation at a temperature of 450 °C. The peculiarities of the manufacturing technology of the planar-diffusion structure of the photodiode to achieve the necessary characteristics that ensure the production of photodiode structures with improved parameters are also considered.