Bernhard Sick

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Sven TomfordeUniversität Kiel, Intelligent SystemsVerified email at informatik.uni-kiel.de
Maarten BieshaarRobert Bosch GmbHVerified email at de.bosch.com
Christian GruhlUniversity of KasselVerified email at uni-kassel.de
André GenslerFreelance Data Scientist / ML EngineerVerified email at uni-kassel.de
Denis HuseljicUniversity of KasselVerified email at uni-kassel.de
Daniel KottkeResearcher, Kassel UniversityVerified email at uni-kassel.de
Adrian CalmaIntelligent Embedded Systems Lab, University of KasselVerified email at uni-kassel.de
Marek HerdeUniversity of KasselVerified email at uni-kassel.de
Janosch HenzeUniversity of KasselVerified email at uni-kassel.de
Paul LukowiczProfessor of Computer Science, DFKI and RPTU KaiserslauternVerified email at dfki.de
Jörg HähnerUniversität AugsburgVerified email at informatik.uni-augsburg.de
Viktor KressResearch Associate, University of Applied Sciences AschaffenburgVerified email at th-ab.de
Tobias ReitmaierDepartment of Intelligent Embedded Systems, University of KasselVerified email at uni-kassel.de
Nicolas ScheinerMercedes-Benz AG, University of KasselVerified email at daimler.com
Jens SchreiberUniversity of KasselVerified email at uni-kassel.de
Edgar KalkowskiPh.D. student, Intelligent Embedded Systems Lab, University of Kassel, GermanyVerified email at uni-kassel.de
Arno WackerBundeswehr University Munich, Germany, Computer Science (Privacy & Compliance))Verified email at unibw.de
Yujiang HeUniversity of KasselVerified email at uni-kassel.de
Sarah Oeste-ReißResearch Group Leader (BMBF AI Program), University of KasselVerified email at uni-kassel.de
Hannes ReichertPhD Student, University of Applied Sciences AschaffenburgVerified email at th-ab.de

Bernhard Sick

Professor of Intelligent Embedded Systems, University of Kassel

Verified email at uni-kassel.de - Homepage

Machine Learning Pattern Recognition Autonomous Learning Intelligent Systems Organic Computing


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Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks A Gensler, J Henze, B Sick, N Raabe 2016 IEEE international conference on systems, man, and cybernetics (SMC …, 2016	761	2016
On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research B Sick Mechanical Systems and Signal Processing 16 (4), 487-546, 2002	589	2002
Online signature verification with support vector machines based on LCSS kernel functions C Gruber, T Gruber, S Krinninger, B Sick IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40 …, 2010	182	2010
Evolutionary optimization of radial basis function classifiers for data mining applications O Buchtala, M Klimek, B Sick Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 35 …, 2005	176	2005
Online segmentation of time series based on polynomial least-squares approximations E Fuchs, T Gruber, J Nitschke, B Sick IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (12), 2232 …, 2010	164	2010
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar N Scheiner, F Kraus, F Wei, B Phan, F Mannan, N Appenrodt, W Ritter, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020	132	2020
Online intrusion alert aggregation with generative data stream modeling A Hofmann, B Sick IEEE transactions on dependable and secure computing 8 (2), 282-294, 2011	89	2011
Trajectory prediction of cyclists using a physical model and an artificial neural network S Zernetsch, S Kohnen, M Goldhammer, K Doll, B Sick 2016 IEEE Intelligent Vehicles Symposium (IV), 833-838, 2016	88	2016
Organic Computing in the Spotlight S Tomforde, B Sick, C Müller-Schloer arXiv preprint arXiv:1701.08125, 2017	82	2017
On-line motif detection in time series with SwiftMotif E Fuchs, T Gruber, J Nitschke, B Sick Pattern Recognition 42 (11), 3015-3031, 2009	74	2009
Intentions of Vulnerable Road Users--Detection and Forecasting by Means of Machine Learning M Goldhammer, S Köhler, S Zernetsch, K Doll, B Sick, K Dietmayer IEEE Transactions on Intelligent Transportation Systems, 2019	73	2019
Engineering and mastering interwoven systems S Tomforde, J Hähner, H Seebach, W Reif, B Sick, A Wacker, I Scholtes ARCS 2014; 2014 Workshop Proceedings on Architecture of Computing Systems, 1-8, 2014	72	2014
On the versatility of radial basis function neural networks: A case study in the field of intrusion detection D Fisch, A Hofmann, B Sick Information Sciences 180 (12), 2421-2439, 2010	68	2010
Feature selection for intrusion detection: an evolutionary wrapper approach A Hofmann, T Horeis, B Sick 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004	68	2004
Evolutionary optimization of radial basis function networks for intrusion detection A Hofmann, B Sick Proceedings of the International Joint Conference on Neural Networks, 2003 …, 2003	66	2003
Quantitative emergence--A refined approach based on divergence measures D Fisch, M Jänicke, B Sick, C Müller-Schloer 2010 Fourth IEEE International Conference on Self-Adaptive and Self …, 2010	65	2010
Let us know your decision: pool-based active training of a generative classifier with the selection strategy 4DS T Reitmaier, B Sick Information Sciences 230, 106-131, 2013	63	2013
Wave-front reconstruction with a Shack-Hartmann sensor with an iterative spline fitting method S Groening, B Sick, K Donner, J Pfund, N Lindlein, J Schwider Applied Optics 39 (4), 561-567, 2000	58	2000
Transductive active learning–A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data T Reitmaier, A Calma, B Sick Information Sciences 293, 275-298, 2015	57	2015
Temporal data mining using shape space representations of time series E Fuchs, T Gruber, H Pree, B Sick Neurocomputing 74 (1-3), 379-393, 2010	57	2010

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