dr inż. Artur Gańcza | Politechnika Gdańska

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dr inż. Artur Gańcza

Kontakt:

email:
artgancz@pg.edu.pl
strona:
https://mostwiedzy.pl/artur-gancza,987194-1

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dr inż. Artur Gańcza

Publikacje:

  1. Publikacja

    Nature-based Solutions (NbS) have emerged as a sustainable approach to managing flood risks by enhancing natural water retention and reducing surface runoff in urban areas. As climate change and rapid urbanization exacerbate flood hazards, optimizing the spatial deployment of NbS is crucial for improving urban resilience and mitigating flood impacts. This study presents a comprehensive optimization framework for the spatial allocation...

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  2. Publikacja

    In this paper we propose a solution to the problem of tracking quasi-periodically varying systems based on the local basis function (LBF) approach. Within this framework, parameter trajectories are locally approximated using linear combinations of specific functions of time known as basis functions. We derive both bias and variance characteristics of LBF estimators. Additionally, we demonstrate that the computational burden associated...

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  3. Publikacja

    - Rok 2024

    This thesis is focused on the basis function method for the identification of nonstationary processes. The first chapter describes a group of models that can be identified using the basis function method. The next chapter describes the basic version of the basis function method, including its algebraic and statistical properties. The following section introduces the local basis function (LBF) method: its properties are described...

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  4. When parameters of mobile telecommunication channels change rapidly, classical adaptive filters, such as exponentially weighted least squares algorithms or gradient algorithms, fail to estimate them with sufficient accuracy. In cases like this, one can use identification methods based on explicit models of parameter changes such as the method of basis functions (BF). When prior knowledge about parameter changes is available the...

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  5. Publikacja

    - Rok 2024

    While local basis function (LBF) estimation algorithms, commonly used for identifying/tracking systems with time-varying parameters, demonstrate good performance under the assumption of normally distributed measurement noise, the estimation results may significantly deviate from satisfactory when the noise distribution is of impulsive nature, for example, heavy-tailed or corrupted by outliers. This paper introduces a computationally...

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