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30 results on '"Wlodarczyk-Sielicka, Marta"'

3. Mobile River Navigation for Smart Cities

Author

Kazimierski, Witold, Wawrzyniak, Natalia, Wlodarczyk-Sielicka, Marta, Hyla, Tomasz, Bodus-Olkowska, Izabela, Zaniewicz, Grzegorz, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Damaševičius, Robertas, editor, and Vasiljevienė, Giedrė, editor

Published
2019
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4. The Use of an Artificial Neural Network for a Sea Bottom Modelling

Author

Lubczonek, Jacek, Wlodarczyk-Sielicka, Marta, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Damaševičius, Robertas, editor, and Vasiljevienė, Giedrė, editor

Published
2018
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5. Problem of Bathymetric Big Data Interpolation for Inland Mobile Navigation System

Author

Włodarczyk-Sielicka, Marta, Wawrzyniak, Natalia, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Damaševičius, Robertas, editor, and Mikašytė, Vilma, editor

Published
2017
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7. Importance of Neighborhood Parameters During Clustering of Bathymetric Data Using Neural Network

Author

Wlodarczyk-Sielicka, Marta, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Dregvaite, Giedre, editor, and Damasevicius, Robertas, editor

Published
2016
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28. The process of modelling the elevation surface of a coastal area using the fusion of spatial data from different sensors

Author

Wlodarczyk-Sielicka, Marta, Bodus-Olkowska, Izabela, and Łącka, Małgorzata

Abstract

•Information about depth distribution in a specific water area is crucial not only for the safety of navigation but also for modelling environmental processes such as quick establishing marine-land boundary or assessments of flood risk areas.•Using elevation data from different available sources can be very convenient if one wants to do some quick analysis or needs to get coverage of a large area with such data without the need to collect data and doing surveying.•The article presents a proposal for a fusion of spatial data from different sources, during the surface modelling process on the example of a coastal area. Spatial data, used for elevation surface modelling, consist of hydrographic and topographic data and are varying in resolution, data density and area coverage.

Published
2021
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29. Automatic Classification Using Machine Learning for Non-Conventional Vessels on Inland Waters.

Author

Wlodarczyk-Sielicka, Marta and Polap, Dawid

Subjects
*NAVAL architecture, *AUTOMATIC classification, *MACHINE learning, *INLAND water transportation, *CAMCORDERS, *AUTOMATIC identification
Abstract

The prevalent methods for monitoring ships are based on automatic identification and radar systems. This applies mainly to large vessels. Additional sensors that are used include video cameras with different resolutions. Such systems feature cameras that capture images and software that analyze the selected video frames. The analysis involves the detection of a ship and the extraction of features to identify it. This article proposes a technique to detect and categorize ships through image processing methods that use convolutional neural networks. Tests to verify the proposed method were carried out on a database containing 200 images of four classes of ships. The advantages and disadvantages of implementing the proposed method are also discussed in light of the results. The system is designed to use multiple existing video streams to identify passing ships on inland waters, especially non-conventional vessels. [ABSTRACT FROM AUTHOR]

Published
2019
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