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Загальна кількість знайдених документів : 8 Представлено документи з 1 до 8 |
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| 1. | 
Pukhkaiev D. S. Advanced approach to web service discovery and selection [Електронний ресурс] / D. S. Pukhkaiev, O. O. Oleksenko, T. M. Kot, L. S. Globa, A. Schill // Information and telecommunication sciences. - 2014. - Vol. 1, no. 1. - С. 42-47. - Режим доступу: http://nbuv.gov.ua/UJRN/Telnau_2014_1_1_9 Web Service Composition (WSC) is a process that helps to save much programming and cost effort by reusing existing components - web services. This process consists of two major stages - Web Service Discovery and Selection (WSD, WSS). This paper presents an overview of current state-of-the-art WSD and WSS methods. It also provides an analysis and highlighting of major problems like lack of support of the syntactical description in fuzzy logic algorithms in WSD and complex approach shortage in WSS problem. Moreover WSC approach and SLA-Aware WSC System are presented. | ||
| 2. | 
Oleksenko O. O. Invariable Content Components of Communication Competence Formation [Електронний ресурс] / O. O. Oleksenko // International journal of education and science. - 2018. - Vol. 1, No. 3-4. - С. 18. - Режим доступу: http://nbuv.gov.ua/UJRN/intjeds_2018_1_3-4_11 | ||
| 3. |
Kolbina T. V. Formation of Students’ Creative Personality by Means of Foreign Languages [Електронний ресурс] / T. V. Kolbina, O. O. Oleksenko // International journal of education and science. - 2019. - Vol. 2, No. 1. - С. 7-13. - Режим доступу: http://nbuv.gov.ua/UJRN/intjeds_2019_2_1_3 | ||
| 4. | 
Khudov H. Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm [Електронний ресурс] / H. Khudov, O. Makoveichuk, I. Khizhnyak, O. Oleksenko, Y. Khazhanets, Y. Solomonenko, I. Yuzova, Y. Dudar, S. Stetsiv, V. Khudov // Eastern-European journal of enterprise technologies. - 2022. - № 2(9). - С. 6-13. - Режим доступу: http://nbuv.gov.ua/UJRN/Vejpte_2022_2(9)__3 This paper reports an improved method for processing the image of a vehicle's license plate when shooting with a smartphone camera. The method for processing the image of a vehicle's license plate includes the following stages: enter the source data; split the video streaming into frames; preliminary process the image of a vehicle's license plate; find the area of a vehicle's license plate; refine character recognition using the signature of a vehicle's license plate; refine character recognition using the combined results from frames in the streaming video; obtain the result of processing. Experimental studies were conducted on the processing of images of a vehicle's license plate. During the experimental studies, the license plate of a military vehicle (Ukraine) was considered. The original image was the color image of a vehicle. The results of experimental studies are given. A comparison of the quality of character recognition in a license plate has been carried out. It was established that the improved method that uses the combined results from streaming video frames works out efficiently at the end of the sequence. The improved method that employs the combined results from streaming video frames operates with numerical probability vectors. The assessment of errors of the first and second kind in processing the image of a license plate was carried out. The total accuracy of finding the area of a license plate by known method is 61 % while the improved method's result is 76 %. It has been established that the minimization of errors of the first kind is more important than reducing errors of the second kind. If a license plate is incorrectly identified, these results would certainly be discarded at the character recognition stage.This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following: highlighting brightness channels in the Red-Green-Blue color space; - using a particle swarm method in the image in each channel of brightness of the RGB color space; image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel. Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems. A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following: the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment); the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue); - the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue). It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %. | ||
| 5. | 
Kolbina T. Implementation of Distance Learning in Ukraine [Електронний ресурс] / T. Kolbina, O. Oleksenko // Educational challenges. - 2020. - Vol. 25, Iss. 1. - С. 46-54. - Режим доступу: http://nbuv.gov.ua/UJRN/znpkhnpu_ped_2020_25_1_7 | ||
| 6. |
Khudov H. Increasing of the accuracy of determining the coordinates of an aerial object in the two-position network of small-sized radars [Електронний ресурс] / H. Khudov, A. Berezhnyi, O. Oleksenko, V. Maliuha, I. Balyk, M. Herda, A. Sobora, Y. Bridnia, V. Chepurnyi, V. Gridina // Eastern-European journal of enterprise technologies. - 2023. - № 5(9). - С. 6–13. - Режим доступу: http://nbuv.gov.ua/UJRN/Vejpte_2023_5(9)__3 | ||
| 7. |
Khudov H. Improving a method for detecting and measuring coordinates of a stealth aerial vehicle by a network of two small-sized radars [Електронний ресурс] / H. Khudov, A. Berezhnyi, S. Yarosh, O. Oleksenko, M. Khomik, I. Yuzova, A. Zvonko, S. Yarovyi, S. Glukhov, A. Sobora // Eastern-European journal of enterprise technologies. - 2023. - № 6(9). - С. 6–13. - Режим доступу: http://nbuv.gov.ua/UJRN/Vejpte_2023_6(9)__3 | ||
| 8. |
Khudov H. Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle [Електронний ресурс] / H. Khudov, S. Yarosh, O. Kostyria, O. Oleksenko, M. Khomik, A. Zvonko, B. Lisohorskyi, P. Mynko, S. Sukonko, T. Kravets // Eastern-European journal of enterprise technologies. - 2024. - № 1(9). - С. 6–13. - Режим доступу: http://nbuv.gov.ua/UJRN/Vejpte_2024_1(9)__3 | ||
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