Contact:
- email:
- martatar@pg.edu.pl
Positions:
Assistant professor
- workplace:
- Katedra Systemów Decyzyjnych i Robotyki
Budynek A Wydziału Elektroniki, Telekomunikacji i Informatyki, EA 453
- phone:
- (58) 347 16 68

Publications:
-
Publication
The article presents a comprehensive quantitative comparison of four analytical models that, in different ways, describe the flow process in transmission pipelines necessary in the task of detecting and isolating leaks. First, the analyzed models are briefly presented. Then, a novel model comparison framework was introduced along with a methodology for generating data and assessing diagnostic effectiveness. The study presents basic...
Full text available to download
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Publication
- F. Montori
- M. S. Tatara
- P. Varga
- IEEE Transactions on Automation Science and Engineering - Year 2024
Engineering tools support the process of creating, operating, maintaining, and evolving systems throughout their lifecycle. Toolchains are sequences of tools that build on each others' output during this procedure. The complete chain of tools itself may not even be recognized by the humans who utilize them, people may just recognize the right tool being used at the right place in time. Modern engineering processes, however, do...
Full text available to download
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Publication
- B. Czaja
- M. S. Tatara (formerly: M. Tatara)
- J. M. Kelner
- Elektronika : konstrukcje, technologie, zastosowania - Year 2024
W niniejszej pracy zaproponowano i przetestowano system wizyjny służący śledzeniu lecącej piłki w celu wypracowania sterowania dla robota wieloosiowego mającego za zadanie złapanie jej. Do detekcji i lokalizacji piłki na obrazie z dwóch, prostopadle ustawionych, kamer zastosowano laplasjan filtru gaussowskiego (LoG) oraz autorski podsystem filtracji rozmytej. Estymację trajektorii lecącej piłki w przestrzeni wykonano w oparciu...
Full text to download in external service
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Publication
- Year 2022
The paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...
Full text available to download
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Publication
This article addresses the problem of choosing the optimal discretization grid for emulating fluid flow through a pipeline. The aggregated basic flow model is linearized near the operating point obtained from the steady state analytic solution of the differential equations under consideration. Based on this model, the relationship between the Courant number (μ) and the stability margin is examined. The numerically set coefficient...
Full text available to download