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 Convolutional Neural Network for Sound Processing - Study of Deployed Application(IEEE (Institute of Electrical and Electronics Engineers), 2019) Doležel, Petr; Štursa, Dominik; Honc, DanielPest birds are considered as a special kind of vermin, since, in most of countries, their legal position does not enable their direct extermination. Therefore, in order to protect the agricultural areas indirectly from pest birds, the robust and highly selective pest bird sensor is necessary to design. In this contribution, the pest bird detection unit, based on a convolutional neural network, is presented. The convolutional neural network itself is used for the decision making about the pest bird occurrence, while sound recordings are used as input data. The testings, presented at the end of the contribution, proved a very high accuracy of the detection unit, with the results indispensably improved in comparison to previously presented approaches.Konferenční objektpeer-reviewedpostprint Omezený přístup Predictive Controller Based on Feedforward Neural Network with Rectified Linear Units(Springer Nature Switzerland AG, 2019) Doležel, Petr; Honc, Daniel; Štursa, DominikThis paper deals with a nonlinear Model Predictive Control with a special form of the process model. Controller uses for the prediction purposes a locally valid linear sub-models. The sub-models are obtained from a neural model with the rectifier activation function in hidden neurons. Simulation example is given to demonstrate proposed solution - neural model design and predictive controller application.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 Thermal Process Control Using Neural Model and Genetic Algorithm(Springer Nature Switzerland AG, 2019) Honc, Daniel; Doležel, Petr; Merta, JanPredictive Controller of a laboratory thermal process is presented in the paper. Process model is approximated by a neural network. On-line optimization is done by a genetic algorithm. Control algorithm is tested on the laboratory thermal process and compared to the standard control methods like predictive controller with the transfer and state-space linear model and the quadratic programming optimization method or a PI controller.Konferenční objektpeer-reviewedpostprint Otevřený přístup Predictive Control of Nonlinear Plant Using Piecewise-Linear Neural Model(IEEE (Institute of Electrical and Electronics Engineers), 2017) Honc, Daniel; Doležel, Petr; Gago, LumírA special form of a predictive controller is presented in this paper. Based on previous authors' work, a piecewise-linear neural model of nonlinear plant to be controlled is adopted to local linearization. The linearized model is then used for control action evaluation using a predictive controller. Although the linearization using piecewise-linear neural network is simple and efficient, it provides the model in a nonstandard form. Therefore, the proposed predictive controller is designed in order to handle that nonstandard model without any customization. At the end of the paper, the illustrative example demonstrates the main features of the introduced solution.