Bernhard Sick
Bernhard Sick
Professor of Intelligent Embedded Systems, University of Kassel
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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
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
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
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
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
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
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
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
Organic Computing in the Spotlight
S Tomforde, B Sick, C Müller-Schloer
arXiv preprint arXiv:1701.08125, 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
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
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
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
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
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
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
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
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
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
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
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Artículos 1–20