Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Konferenční objektpeer-reviewedpostprint Otevřený přístup Development of Artificial Intelligence Based Module to Industrial Network Protection System(Springer Nature Switzerland AG, 2020) Holík, Filip; Doležel, Petr; Merta, Jan; Štursa, DominikThe paper deals with the software-defined networking concept applied to industrial networks. This innovative concept supports network programmability and dynamic implementation of customized features, including security related ones. In a previous work of the authors, the industrial network protection system (INPS) was designed and implemented. The INPS provides complex security features of various traditional and modern security solutions within a single system. In this paper, the AI module, which is one of the crucial parts of the INPS, is dealt with. In particular, a detailed report focused on the development of the AI module decision function is provided. As a result, an artificial neural network, used for the network traffic evaluation in the AI module, is developed and comprehensively tested.Konferenční objektpeer-reviewedpostprint Otevřený přístup On possibilities of human head detection for person flow monitoring system(Springer Nature Switzerland AG, 2019) Doležel, Petr; Štursa, Dominik; Škrabánek, PavelAlong with the development of human society, economy, industry and engineering, as well as with growing population in the world's biggest cities, various approaches to person detection have become the subject of great interest. One approach to developing a person detection system is proposed in this paper. A high-angle video sequence is considered as the input to the system. Then, three classification algorithms are considered: support vector machines, pattern recognition neural networks and convolutional neural networks. The results showed very little difference between the classifiers, with the overall accuracy more than 95% over a testing set.Konferenční objektpeer-reviewedpostprint Omezený přístup Neural Network for Smart Adjustment of Industrial Camera - Study of Deployed Application(Springer, 2018) Doležel, Petr; Honc, DanielSince machine vision is gaining more and more interest lately, it is necessary to deal with correct approaches to visual data acquisition in industry. As a particular part of this complex problematics, a technique for the industrial camera exposure time and image sensor gain tuning is presented in this contribution. In comparison to other approaches, a human expert photographer is used instead of explicitly defined cost function. His knowledge is transformed into an artificial expert system represented by a feedforward neural network. The expert system then provides the suitable exposure time and image sensor gain to gather sharp and balanced images.Konferenční objektpeer-reviewedpostprint Omezený přístup Possibilities of Piecewise-Linear Neural Network Training Using Levenberg-Marquardt Algorithm and Hybrid Differential Evolution(Vysoké učení technické v Brně, 2016) Gago, Lumír; Doležel, PetrThis article is focused on the comparison of the learning of an artificial neural network with a hyperbolic tangent activation function and an artificial neural network with a linear saturated activation function in hidden layers. The learning is performed by a Levenberg-Marquardt algorithm and hybrid differential evolution. For evaluating of learning characteristics, there is calculated a comprehensive set of statistical variables. The results are analysed and shown as a table for each experiment. An empirical result discussed at the end of the paper is, that the approximation qualities of both networks under examination are similar.