Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Marcin Cwiek"'
Autor:
Jakub Nalepa, Michal Myller, Marcin Cwiek, Lukasz Zak, Tomasz Lakota, Lukasz Tulczyjew, Michal Kawulok
Publikováno v:
Remote Sensing, Vol 13, Iss 8, p 1532 (2021)
Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are
Externí odkaz:
https://doaj.org/article/105a8d211f3a49c2b4ca348d98ec2e4e
Autor:
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok, Marcin Cwiek, Tomasz Lakota, Nicolas Longepe, Jakub Nalepa
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::830c87a292883e9c7730769f18598b70
https://doi.org/10.1109/igarss46834.2022.9884919
https://doi.org/10.1109/igarss46834.2022.9884919
Autor:
Michal Kawulok, Tomasz Lakota, Jakub Nalepa, Marcin Cwiek, Michal Myller, Lukasz Zak, Lukasz Tulczyjew
Publikováno v:
Remote Sensing; Volume 13; Issue 8; Pages: 1532
Remote Sensing, Vol 13, Iss 1532, p 1532 (2021)
Remote Sensing, Vol 13, Iss 1532, p 1532 (2021)
Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa0eb39b24c27ebe66dbec4b75ccf543
Autor:
Michael P. Hayball, Michal Kawulok, Marcin Cwiek, Pawel Wawrzyniak, Pawel Ulrych, Szymon Piechaczek, Wojciech Dudzik, Janusz Szymanek, M. Marcinkiewicz, Grzegorz Mrukwa, Pablo Ribalta Lorenzo, Barbara Bobek-Billewicz, Jakub Nalepa
Publikováno v:
ICIP
Data augmentation helps improve generalization of deep neural networks, and can be perceived as implicit regularization. It is pivotal in scenarios in which the amount of ground-truth data is limited, and acquiring new examples is costly and time-con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f924af439acd2829bd0dc7b8d0170f68
https://doi.org/10.1109/icip.2019.8803423
https://doi.org/10.1109/icip.2019.8803423
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319677910
ICMMI
ICMMI
Job shop scheduling problem (JSSP) is an NP-hard optimization problem which has been widely studied in the literature due to its practical applicability. In this paper, we show how to model a workflow using a modified version of JSSP, in which a give
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a0809d39c60504147173c328e3bb963
https://doi.org/10.1007/978-3-319-67792-7_49
https://doi.org/10.1007/978-3-319-67792-7_49
Autor:
Marcin Cwiek, Jakub Nalepa
Publikováno v:
Intelligent Information and Database Systems ISBN: 9783319544717
ACIIDS (1)
ACIIDS (1)
Exact cover problem is a well-known NP-complete decision problem to determine if the exact cover really exists. In this paper, we show how to solve a modified version of the famous Hexomino puzzle (being a noteworthy example of an exact cover problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0ef9e4f5c30fdb5adadf9c12a10d4da
https://doi.org/10.1007/978-3-319-54472-4_39
https://doi.org/10.1007/978-3-319-54472-4_39
Publikováno v:
Intelligent Information and Database Systems ISBN: 9783662493809
ACIIDS (1)
ACIIDS (1)
In this paper, we show how to generate challenging benchmark tests for rich vehicle routing problems (VRPs) using a new heuristic algorithm (termed HeBeG—Heuristic Benchmark Generator). We consider a modified VRP with time windows, in which the dep
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4dfb47652dc5ae3260a9b09a57d9102a
https://doi.org/10.1007/978-3-662-49381-6_38
https://doi.org/10.1007/978-3-662-49381-6_38
Publikováno v:
IJCNN
Solving the job shop scheduling problem (JSP) is a vital research topic due to its wide practical applicability. It is an NP-hard discrete optimization problem which was transformed into a plethora of variants reflecting other real-life scenarios. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e21bd35b6cf0452902eb32f4bb40fc72
https://doi.org/10.1109/ijcnn.2015.7280409
https://doi.org/10.1109/ijcnn.2015.7280409
Autor:
Marcin Cwiek, Jakub Nalepa
Publikováno v:
GECCO (Companion)
This paper presents a fast genetic algorithm (GA) for solving the flexible job shob scheduling problem (FJSP). The FJSP is an extension of a classical NP-hard job shop scheduling problem. Here, we combine the active schedule constructive crossover (A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8fbf38091b8a53d097e58b18268f897
https://doi.org/10.1145/2598394.2602280
https://doi.org/10.1145/2598394.2602280