Analysis of the methodological approaches in connection with problem solving the extrapolation of object trajectory

Kozhemyako, Volodymyr P.; Timchenko, Leonid I.; Kutaev, Yuriy; Gertsiy, Alexander A.; Yarovyy, Andriy; Kokryatskaya, Nataly I.; Grebenyuk, N.P.; Poplavskyy, Olexandr A.

doi:10.1117/12.507492

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A possible solution of the problem of transfer of large volumes of information is to use a fiber-optic cable, but laying such cable is rather expensive, even on short distances. At the same time, this problem can be solved through application of the laser-based technologies (Khosrofian and Garetz, 1983 ; Kozhemyako et al, 2003 ; Basov et al, 1998 ), one of the most promising models of information transfer for the near future. In this case, for instance, tons of full-length films and virtual worlds could be transferred to any part of the globe in a blink of an eye.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the high-performance PLD-based sectioning method for classification of the shape of optical object images

Tymchenko¹,

Petrovskiy²,

Kokryatskaya³

et al. 2013

SpringerPlus

View full text Add to dashboard Cite

An analysis of the sectioning method to control a shape of the laser beam spot is conducted in this paper. A possibility is discussed to use a form factor for solving the problem raised, and the experimental study of the sectioning method and the form factor for control of the shape of the laser beam spot was conducted. A PLD-based technical implementation of the method was realized.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-2-692) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling of the high-performance PLD-based sectioning method for classification of the shape of optical object images

Tymchenko¹,

Petrovskiy²,

Kokryatskaya³

et al. 2013

SpringerPlus

View full text Add to dashboard Cite

show abstract

Method for processing and classification of laser beam images using PLD

Petrovskyi

Tymchenko

Semenyuk

et al. 2012

2012 IX International Symposium on Telecommunications (BIHTEL)

View full text Add to dashboard Cite

Comparative Analysis of Image Recognition Methods Obtained From Sensors of the Visible Spectrum

2020

View full text Add to dashboard Cite

Today, in an era of integration of artificial intelligence systems in almost every industry, very demand are studies of classification methods that, prior to their introduction into decision support systems. Compare analysis of the secant planes method, method of the potentials and potential method in the field of receptors are shown in the paper. At first, in introduction, authors shew needs of autonomic systems of adaptive perception on visible diapason of specter. As particularly aim, these methods are compared by criteria of speed, accuracy and amount of storage used after training. As general idea we are looking for we are looking for methodic of the best combination of method for different condition on observe field of visual spectral diapason. Theories of the every method are presented, and then tables of compare analysis of results are shown. Step-by-step comparative experiments are described in detail. Changes at each step are shown in detail in the tables of the corresponding signs. Moreover, at the end of the paper, comparative characteristics of each method with the same learning time in same type of experiments for each method are presented. As a result, in the first group of tables , we see a difference in the recognition time and the amount of memory required for correct operation. Those are truth tables for two points, three points, two points and two planes, three points and two planes, three points and three planes, three points and seven planes. The conclusion gives a thorough explanation of where to use the best method. The needs of the system for computing resources in the application of each mode are presented and corresponding dependencies are derived. Next, If you train several times on the same object (ie, train several times), you can expect that the errors in the breakdown of the receptor space will be different. In this case, you can improve the performance of the algorithm by parallelizing its process into several threads. Using this method simultaneously and independently of each other on the same image is multi-threaded learning on multiple computer kernels. When recognizing new objects, they will refer to some image, not necessarily the same. The final decision is made by "vote" - the object refers to the image to which it was attributed to a greater number of parallel streams.

show abstract

Analysis of the methodological approaches in connection with problem solving the extrapolation of object trajectory

Cited by 4 publications

References 0 publications

Modeling of the high-performance PLD-based sectioning method for classification of the shape of optical object images

Modeling of the high-performance PLD-based sectioning method for classification of the shape of optical object images

Method for processing and classification of laser beam images using PLD

Comparative Analysis of Image Recognition Methods Obtained From Sensors of the Visible Spectrum

Contact Info

Product

Resources

About