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Your search keyword '"Bartok, Juraj"' showing total 17 results
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17 results on '"Bartok, Juraj"'

1. Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas

Author

Díaz, Judith Sáinz-Pardo, Castrillo, María, Bartok, Juraj, Cachá, Ignacio Heredia, Ondík, Irina Malkin, Martynovskyi, Ivan, Alibabaei, Khadijeh, Berberi, Lisana, Kozlov, Valentin, and García, Álvaro López

Subjects
Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics
Abstract

The increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especially through deep learning models, are key tools to be used on the large amount of data that can be obtained, for example, from weather radars. In many cases, the information collected by these radars is not open, or belongs to different institutions, thus needing to deal with the distributed nature of this data. In this work, the applicability of a personalized federated learning architecture, which has been called adapFL, on distributed weather radar images is addressed. To this end, given a single available radar covering 400 km in diameter, the captured images are divided in such a way that they are disjointly distributed into four different federated clients. The results obtained with adapFL are analyzed in each zone, as well as in a central area covering part of the surface of each of the previously distributed areas. The ultimate goal of this work is to study the generalization capability of this type of learning technique for its extrapolation to use cases in which a representative number of radars is available, whose data can not be centralized due to technical, legal or administrative concerns. The results of this preliminary study indicate that the performance obtained in each zone with the adapFL approach allows improving the results of the federated learning approach, the individual deep learning models and the classical Continuity Tracking Radar Echoes by Correlation approach., Comment: Accepted for publication in Earth Science Informatics

Published
2024

3. Artificial Intelligence-Based Detection of Light Points: An Aid for Night-Time Visibility Observations.

Author

Gáborčíková, Zuzana, Bartok, Juraj, Malkin Ondík, Irina, Benešová, Wanda, Ivica, Lukáš, Hnilicová, Silvia, and Gaál, Ladislav

Subjects
*CONVOLUTIONAL neural networks, *ARTIFICIAL intelligence, *TRANSPORTATION industry, *METEOROLOGY, *CAMERAS
Abstract

Visibility is one of the key meteorological parameters with special importance in aviation meteorology and the transportation industry. Nevertheless, it is not a straightforward task to automatize visibility observations, since the assistance of trained human observers is still inevitable. The current paper attempts to make the first step in the process of automated visibility observations: it examines, by the approaches of artificial intelligence (AI), whether light points in the target area can or cannot be automatically detected for the purposes of night-time visibility observations. From a technical point of view, our approach mimics human visibility observation of the whole circular horizon by the usage of camera imagery. We evaluated the detectability of light points in the camera images (1) based on an AI approach (convolutional neural network, CNN) and (2) based on a traditional approach using simple binary thresholding (BT). The models based on trained CNN achieved remarkably better results in terms of higher values of statistical metrics, and less susceptibility to errors than the BT-based method. Compared to BT, the CNN classification method indicated greater stability since the accuracy of these models grew with increasing pixel size around the key points. This fundamental difference between the approaches was also confirmed through the Mann–Whitney U test. Thus, the presented AI-based determination of key points' detectability in the night with decent accuracy has great potential in the objectivization of everyday routines of professional meteorology. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Meteorological Phenomena Forecast Using Data Mining Prediction Methods

Author

Babič, František, Bednár, Peter, Albert, František, Paralič, Ján, Bartók, Juraj, Hluchý, Ladislav, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Jędrzejowicz, Piotr, editor, Nguyen, Ngoc Thanh, editor, and Hoang, Kiem, editor

Published
2011
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14. Machine learning in radioactive nuclides identification

Author

Hluchy Ladislav, Krammer Peter, and Bartok Juraj

Subjects
Identification methods, Identification (information), business.industry, Computer science, Nuclide, Data mining, Nuclear power, computer.software_genre, Security level, business, Realization (systems), computer, Task (project management)
Abstract

Chemistry and nuclear physics represent one of possible options of large area for practical data mining applications. Radio-active nuclides are related with many sectors, such as medicine or industry. Successful detection and identification of radio-nuclides allows the realization of specific safety precaution and then increasing of security level in nuclear power plants, or medicine institutes. Data obtained by radio spectroscopy method reach high level of relevancy. Thus these data are suitable for using data mining methods, because the presented problem corresponds with classification task. Two radio-nuclides identification methods were presented in this paper; the second method is designed specially for this purpose.

Published
2013

17. DATA MINING FOR FOG PREDICTION AND LOW CLOUDS DETECTION

Author

Bartok, Juraj, Babic, Frantisek, Bednar, Peter, Paralic, Jan, Kovac, Jozef, Bartokova, Ivana, Ladislav Hluchy, and Gera, Martin

Subjects
Meteorological prediction, aviation, fog, cloudiness, data mining, 68T30, 68T05, 68T10, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, InformationSystems_DATABASEMANAGEMENT
Abstract

his paper describes our contribution to the research of parametrized models and methods for detection and prediction of significant meteorological phenomena, especially fog and low cloud cover. The project covered methods for integration of distributed meteorological data necessary for running the prediction models, training models and then mining the data in order to be able to efficiently and quickly predict even sparsely occurring phenomena. The detection and prediction methods are based on knowledge discovery -- data mining of meteorological data using neural networks and decision trees. The mined data were mainly METAR aerodrome messages, meteorological data from specialized stations and cloud data from special airport sensors -- laser ceilometers.

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