Digitální knihovnaUPCE
 

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

Procházet

Filtry

2019 - 20202

Nastavení

Autor: search.filters.author.Doležel, PetrAutor: search.filters.author.Štursa, DominikPředmět: search.filters.subject.umělá neuronová síťTyp: search.filters.itemtype.ConferenceObject

Search Results

Nyní se zobrazuje 1 - 2 z 2
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Development of Artificial Intelligence Based Module to Industrial Network Protection System
    (Springer Nature Switzerland AG, 2020) Holík, Filip; Doležel, Petr; Merta, Jan; Štursa, Dominik
    The 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.
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    On possibilities of human head detection for person flow monitoring system
    (Springer Nature Switzerland AG, 2019) Doležel, Petr; Štursa, Dominik; Škrabánek, Pavel
    Along 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.