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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Článekpeer-reviewedpublished version Otevřený přístup Optimization of a Depiction Procedure for an Artificial Intelligence-Based Network Protection System Using a Genetic Algorithm(MDPI, 2021) Doležel, Petr; Holík, Filip; Merta, Jan; Štursa, DominikThe current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more important. Another serious phenomenon that is apparent these days and that must not be discounted is the growing use of artificial intelligence techniques for carrying out network attacks. To combat these attacks, effective protection methods must also utilize artificial intelligence. Hence, we are introducing a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based network-traffic-handling controller. This decision procedure is based on a convolutional neural network that processes the incoming flow characteristics and provides a decision; the procedure can be understood as a firewall rule. The main advantage of this decision procedure is its depiction process, which has the ability to transform the incoming flow characteristics into a graphical structure. Graphical structures are regarded as very efficient data structures for processing by convolutional neural networks. This article's main contribution consists of the development and improvement of the depiction process using a genetic algorithm. The results presented at the end of the article show that the decision procedure using an optimized depiction process brings significant improvements in comparison to previous experiments.Článekpeer-reviewedpublished Omezený přístup COMPUTATIONALLY SIMPLE NEURAL NETWORK APPROACH TO DETERMINE PIECEWISE-LINEAR DYNAMICAL MODEL(České vysoké učení technické v Praze, 2017) Doležel, Petr; Heckenbergerová, JanaThe article introduces a new technique for nonlinear system modeling. This approach, in comparison to its alternatives, is straight and computationally undemanding. The article employs the fact that once a nonlinear problem is modeled by a piecewise-linear model, it can be solved by many efficient techniques. Thus, the result of introduced technique provides a set of linear equations. Each of the equations is valid in some region of state space and together, they approximate the whole nonlinear problem. The technique is comprehensively described and its advantages are demonstrated on an example.Článekpeer-reviewedpostprint Otevřený přístup Weight Initialization Possibilities for Feedforward Neural Network with Linear Saturated Activation Functions(Elsevier Science BV, 2016) Doležel, Petr; Škrabánek, Pavel; Gago, LumírInitial weight choice is an important aspect of the training mechanism for feedforward neural networks. This paper deals with a particular topology of a feedforward neural network, where symmetric linear saturated activation functions are used in a hidden layer. Training of such a topology is a tricky procedure, since the activation functions are not fully differentiable. Thus, a proper initialization method for that case is even more important, than dealing with neural networks with sigmoid activation functions. Therefore, several initialization possibilities are examined and tested here. As a result, particular initialization methods are recommended for application, according to the class of the task to be solved.Článekpeer-reviewedpublished version Omezený přístup Simulation of predictive control algorithm for rotary furnace producing magnesite sinter(Trans Tech Publications, 2015) Mariška Martin; Taufer Ivan; Koštial Imrich; Doležel Petr; Palička PavolThe paper describes the design of predictive control algorithm for rotary furnace control. At the beginning of the paper, there are defined the main aims of the predictive control. Then, the functional diagram using artificial neural networks as a reference model is proposed and, eventually, the functionality of described approach is demonstrated on a simulated example.