Search results
- 1.0577938 - ÚTIA 2024 RIV GB eng J - Journal Article
Flusser, M. - Somol, Petr
Efficient anomaly detection through surrogate neural networks.
Neural Computing & Applications. Roč. 34, č. 23 (2022), s. 20491-20505. ISSN 0941-0643. E-ISSN 1433-3058
Institutional support: RVO:67985556
Keywords : Anomaly detector * Neural network * Model transfer * Detector ensemble * High-performance anomaly detection
OECD category: Automation and control systems
Impact factor: 6, year: 2022
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2023/RO/somol-0577938.pdf https://link.springer.com/article/10.1007/s00521-022-07506-9
Permanent Link: https://hdl.handle.net/11104/0347645 - 2.0568579 - ÚI 2023 RIV CZ eng L4 - Software
Hlinka, Jaroslav - Pidnebesna, Anna - Tani Raffaelli, Giulio - Hartman, David - Převorovský, Zdeněk - Chlada, Milan - Kovanda, Martin - Prášek, P. - Berka, Z. - Svoboda, R.
Library of software modules for detecting extreme events.
Internal code: TN01000024/13-V02 ; 2022
Technical parameters: K dosažení popsaných detekcí jsou použity pokročilé metody využívající konkrétní strukturu problému. Knihovna je navržena v programovacím jazyce Python. Má strukturu centrální části (TN01000024/13-)V2.1 s obecnými analytickými moduly, doplněné specializovaným modulem V2.2 pro analýzu dat z non-destructive testing (NDT), a specializovaným modulem V2.3 pro analýzu video dat.
Economic parameters: Výsledkem je knihovna softwarových modulů schopných detekovat náhlé události a detekovat přechody mezi různými režimy studovaného systému. Klíčovou funkcionalitou je detekce zásadních změn v systému zvolené kritické infrastruktury. Tato schopnost umožňuje operátorům efektivně detekovat extrémní události a stavové přechody. LICENCE: Modul V2.1: GNU Affero General Public License v3.0. Modul V2.2: bez licence, avšak všechna práva vyhrazena. Modul V2.3: Modul pro analýzu videodat je dostupný účastníkům projektu v neveřejném repozitáři, ostatním zájemcům bude nabízen pod komerční licencí.
R&D Projects: GA TA ČR(CZ) TN01000024
Institutional support: RVO:67985807 ; RVO:61388998
Keywords : anomaly detection * non-destructive testing * video analysis * crowd dynamics
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8); Audio engineering, reliability analysis (UT-L)
https://www.ciirc.cvut.cz/research-education/projects/nck-kui/sub13/v2/
Permanent Link: https://hdl.handle.net/11104/0339865 - 3.0536614 - ÚI 2021 RIV DE eng C - Conference Paper (international conference)
Šabata, T. - Holeňa, Martin
Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings.
Proceedings of the Workshop on Interactive Adaptive Learning. Aachen: Technical University & CreateSpace Independent Publishing, 2020 - (Kottke, D.; Krempl, G.; Lemaire, V.; Holzinger, A.; Calma, A.), s. 72-77. CEUR Workshop Proceedings, 2660. ISSN 1613-0073.
[IAL 2020: International Workshop on Interactive Adaptive Learning /4./. Virtual Ghent (BE), 14.09.2020-14.09.2020]
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Active Learning * Anomaly detection * LSTM-Autoencoder * Time series
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2660/ialatecml_shortpaper1.pdf
Permanent Link: http://hdl.handle.net/11104/0314366File Download Size Commentary Version Access 0536614-aw.pdf 0 527.4 KB volně online Publisher’s postprint open-access - 4.0532709 - ÚSMH 2021 RIV US eng J - Journal Article
Ambrosino, F. - Thinová, L. - Briestenský, Miloš - Šebela, S. - Sabbarese, C.
Detecting time series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes.
Acta Geodaetica et Geophysica. Roč. 55, č. 3 (2020), s. 405-420. ISSN 2213-5812. E-ISSN 2213-5820
R&D Projects: GA MŠMT(CZ) LM2015079
Institutional support: RVO:67985891
Keywords : Time series analysis * Hybrid method * Anomaly detection * Radon as tracer * Earthquake
OECD category: Inorganic and nuclear chemistry
Impact factor: 1.324, year: 2020
Method of publishing: Limited access
https://link.springer.com/article/10.1007/s40328-020-00298-1
Permanent Link: http://hdl.handle.net/11104/0311121 - 5.0522404 - ÚI 2021 RIV GB eng J - Journal Article
Kopp, M. - Pevný, T. - Holeňa, Martin
Anomaly explanation with random forests.
Expert Systems With Applications. Roč. 149, 1 July (2020), č. článku 113187. ISSN 0957-4174. E-ISSN 1873-6793
R&D Projects: GA ČR GA17-01251S
Grant - others:GA ČR(CZ) GA18-21409S
Program: GA
Institutional support: RVO:67985807
Keywords : Anomaly detection * Anomaly explanation * Classification rules * Feature selection * Random forests
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 6.954, year: 2020
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.eswa.2020.113187
Permanent Link: http://hdl.handle.net/11104/0306903 - 6.0507118 - ÚTIA 2020 RIV US eng C - Conference Paper (international conference)
Flusser, M. - Pevný, T. - Somol, Petr
Density-Approximating Neural Network Models for Anomaly Detection.
ACM SIGKDD 2018 Workshop. New York: ACM, 2018, s. 1-8. ISBN 978-1-4503-5552-0.
[ACM SIGKDD 2018 Workshop. London (GB), 20.08.2018]
Institutional support: RVO:67985556
Keywords : neural network * anomaly detection
OECD category: Robotics and automatic control
http://library.utia.cas.cz/separaty/2019/RO/somol-0507118.pdf
Permanent Link: http://hdl.handle.net/11104/0298560 - 7.0506360 - ÚI 2020 RIV CH eng C - Conference Paper (international conference)
Kalina, Jan - Vidnerová, Petra
Robust Training of Radial Basis Function Neural Networks.
Artificial Intelligence and Soft Computing. Proceedings, Part I. Cham: Springer, 2019 - (Rutkowski, L.; Scherer, R.; Korytkowski, M.; Pedrycz, W.; Tadeusiewicz, R.; Zurada, J.), s. 113-124. Lecture Notes in Computer Science, 11508. ISBN 978-3-030-20911-7. ISSN 0302-9743.
[ICAISC 2019: International Conference on Artificial Intelligence and Soft Computing /18./. Zakopane (PL), 16.06.2019-20.06.2019]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : Machine learning * Outliers * Robustness * Subset selection * Anomaly detection
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0297617 - 8.0447917 - ÚI 2016 RIV DE eng C - Conference Paper (international conference)
Kopp, Martin - Holeňa, Martin
Evaluation of Association Rules Extracted during Anomaly Explanation.
Proceedings ITAT 2015: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2015 - (Yaghob, J.), s. 143-149. CEUR Workshop Proceedings, V-1422. ISBN 978-1-5151-2065-0. ISSN 1613-0073.
[ITAT 2015. Conference on Theory and Practice of Information Technologies /15./. Slovenský Raj (SK), 17.09.2015-21.09.2015]
R&D Projects: GA ČR GA13-17187S
Institutional support: RVO:67985807
Keywords : anomaly detection * anomaly interpretation * association rules * confidence boost * random forest
Subject RIV: IN - Informatics, Computer Science
Permanent Link: http://hdl.handle.net/11104/0249671File Download Size Commentary Version Access a0447917.pdf 0 684.3 KB Publisher’s postprint require - 9.0432410 - ÚI 2015 RIV CZ eng C - Conference Paper (international conference)
Kopp, Martin - Pevný, T. - Holeňa, Martin
Interpreting and Clustering Outliers with Sapling Random Forests.
ITAT 2014. Information Technologies - Applications and Theory. Part II. Prague: Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 61-67. ISBN 978-80-87136-19-5.
[ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014]
R&D Projects: GA ČR GA13-17187S
Grant - others:GA ČR(CZ) GPP103/12/P514
Institutional support: RVO:67985807
Keywords : anomaly detection * anomaly interpretation * clustering * decision trees * feature selection * random forest
Subject RIV: IN - Informatics, Computer Science
Permanent Link: http://hdl.handle.net/11104/0236773File Download Size Commentary Version Access 0432410.pdf 26 156.4 KB Publisher’s postprint open-access