
mgr inż. Kajetan Zielonacki
Zatrudnienie
- Doktorant w Szkoła Doktorska PG
- Asystent w Katedra Inteligentnych Systemów Sterowania i Wspomagania Decyzji
Obszary badawcze
Kontakt dla biznesu
- Lokalizacja
- Al. Zwycięstwa 27, 80-219 Gdańsk
- Telefon
- +48 58 348 62 62
- biznes@pg.edu.pl
Media społecznościowe
Kontakt
- kajetan.zielonacki@pg.edu.pl
Asystent
- Katedra Inteligentnych Systemów Sterowania i Wspomagania Decyzji
- Wydział Elektrotechniki i Automatyki
- Miejsce pracy
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Budynek Wydz. Elektr.
pokój E-11 otwiera się w nowej karcie - kajzielo@pg.edu.pl
Wybrane publikacje
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PLC-based Implementation of Stochastic Optimization Method in the Form of Evolutionary Strategies for PID, LQR, and MPC Control
Programmable logic controllers (PLCs) are usually equipped with only basic direct control algorithms like proportional-integral-derivative (PID). Modules included in engineering software running on a personal computer (PC) are usually used to tune controllers. In this article, an alternative approach is considered, i.e. the development of a stochastic optimizer based on the (μ,λ) evolution strategy (ES) in a PLC. For this purpose,...
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Neural network model of ship magnetic signature for different measurement depths
This paper presents the development of a model of a corvette-type ship’s magnetic signature using an artificial neural network (ANN). The capabilities of ANNs to learn complex relationships between the vessel’s characteristics and the magnetic field at different depths are proposed as an alternative to a multi-dipole model. A training dataset, consisting of signatures prepared in finite element method (FEM) environment Simulia...
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Ship Magnetic Signature Classification Using GRU-Based Recurrent Neural Networks
Magnetic signatures represent the magnetic field generated by a ship’s ferromagnetic components and provide valuable information for identifying vessels not only in naval operations, but also in civil passages. The topic of accurate modelling of these signatures is relevant to this day, but also the complexity of the model necessary to accurately predict the ship’s magnetic field. This paper presents the implementation of a deep,...
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