Bernhard Sick

5.0k citations

284 papers · 3.8k · 2 hit papers · h-index 28

Impact in

Artificial Intelligence top 0.5%
- Anomaly Detection Techniques and Applications
- Solar Radiation and Photovoltaics
- Neural Networks and Applications
Signal Processing top 1%
- Time Series Analysis and Forecasting

Papers in

Artificial Intelligence 154
- Anomaly Detection Techniques and Applications 43
- Neural Networks and Applications 34
- Machine Learning and Algorithms 21
- Machine Learning and Data Classification 19
Signal Processing 37
- Time Series Analysis and Forecasting 26

Co-authors: André Gensler (13 shared papers)Janosch Henze (8 shared papers)Nils Kristian Raabe (1 shared paper)Thiemo Gruber (14 shared papers)Sven Tomforde (33 shared papers)Christian C. Gruber (10 shared papers)Konrad Doll (27 shared papers)Nils Appenrodt (5 shared papers)
Journals: Information Sciences (9 papers)Scientific Reports (5 papers)Lecture notes in computer science (28 papers)Energies (3 papers)Machine Learning (3 papers)
Partner nations: Germany United States Finland

In The Last Decade

Bernhard Sick

258 papers receiving 3.6k citations

Bernhard Sick's Hit Papers

align trajectories log scale

Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks 2016 · 479 citations

What are hit papers?

Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.

Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.

2016 Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
2002 Mechanical Systems and Signal Processing

Peers

Countries citing papers authored by Bernhard Sick

Since Specialization

Citations

This map shows the geographic impact of Bernhard Sick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bernhard Sick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernhard Sick more than expected).

Fields of papers citing papers by Bernhard Sick

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernhard Sick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bernhard Sick. The network helps show where Bernhard Sick may publish in the future.

Co-authors

The 25 scholars most cited alongside Bernhard Sick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bernhard Sick Line = papers co-authored together Bernhard Sick links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 284 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks André Gensler, Janosch Henze, Bernhard Sick, Nils Kristian Raabe Hit paper breakdown →	2016	479
2	ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH Mechanical Systems and Signal Processing ·Bernhard Sick Hit paper breakdown →	2002	374
3	Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ·Oliver Buchtala, Maciej Klimek, Bernhard Sick	2005	110
4	Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar arXiv (Cornell University) ·Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt, Werner Ritter, Juergen Dickmann, Klaus Dietmayer, Bernhard Sick, Felix Heide	2020	108
5	Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ·Christian C. Gruber, Thiemo Gruber, Sebastian Forster, Bernhard Sick	2009	107
6	Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations IEEE Transactions on Pattern Analysis and Machine Intelligence ·E Fuchs, Thiemo Gruber, J Nitschke, Bernhard Sick	2010	100
7	Trajectory prediction of cyclists using a physical model and an artificial neural network Stefan Zernetsch, Sascha Kohnen, Michael Goldhammer, Konrad Doll, Bernhard Sick	2016	65
8	On the versatility of radial basis function neural networks: A case study in the field of intrusion detection Information Sciences ·Dominik Fisch, Alexander Hofmann, Bernhard Sick	2010	51
9	On-line motif detection in time series with SwiftMotif Pattern Recognition ·Erich Pinzón-Fuchs, Thiemo Gruber, J Nitschke, Bernhard Sick	2009	46
10	Object detection for automotive radar point clouds – a comparison Nicolas Scheiner, Florian Kraus, Nils Appenrodt, Juergen Dickmann, Bernhard Sick	2021	44
11	Quantitative Emergence -- A Refined Approach Based on Divergence Measures Dominik Fisch, Martin Jänicke, Bernhard Sick, Christian Müller-Schloer	2010	42
12	Online Intrusion Alert Aggregation with Generative Data Stream Modeling IEEE Transactions on Dependable and Secure Computing ·Alexander Hofmann, Bernhard Sick	2009	42
13	Wave-front reconstruction with a Shack–Hartmann sensor with an iterative spline fitting method Applied Optics ·Sascha Groening, Bernhard Sick, Klaus Donner, Johannes Pfund, Norbert Lindlein, Johannes Schwider	2000	38
14	Feature selection for intrusion detection: an evolutionary wrapper approach A. Hofmann, Timo Horeis, Bernhard Sick	2005	37
15	Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data Information Sciences ·Tobias Reitmaier, Adrian Calma, Bernhard Sick	2014	37
16	A Multi-Stage Clustering Framework for Automotive Radar Data arXiv (Cornell University) ·Nicolas Scheiner, Nils Appenrodt, Juergen Dickmann, Bernhard Sick	2019	35
17	Engineering and Mastering Interwoven Systems OPUS (Augsburg University) ·Sven Tomforde, Jörg Hähner, Hella Seebach, Wolfgang Reif, Bernhard Sick, Arno Wacker, Ingo Scholtes	2014	33
18	Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles arXiv (Cornell University) ·Nicolas Scheiner, Nils Appenrodt, Juergen Dickmann, Bernhard Sick	2019	33
19	Radar-based Feature Design and Multiclass Classification for Road User Recognition arXiv (Cornell University) ·Nicolas Scheiner, Nils Appenrodt, Juergen Dickmann, Bernhard Sick	2018	33
20	Camera based pedestrian path prediction by means of polynomial least-squares approximation and multilayer perceptron neural networks Michael Goldhammer, Sebastian Köhler, Konrad Doll, Bernhard Sick	2015	31

About Bernhard Sick

Bernhard Sick is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Automotive Engineering and Computer Networks and Communications, having authored 284 papers that have together received 3.8k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (43 papers), Neural Networks and Applications (34 papers), Autonomous Vehicle Technology and Safety (28 papers), Time Series Analysis and Forecasting (26 papers), Energy Load and Power Forecasting (22 papers), Network Security and Intrusion Detection (21 papers), Machine Learning and Algorithms (21 papers) and Machine Learning and Data Classification (19 papers). The work is most often cited by research in Artificial Intelligence (1.7k citations), Signal Processing (543 citations), Computer Vision and Pattern Recognition (633 citations), Computer Networks and Communications (548 citations) and Instrumentation (79 citations). Bernhard Sick has collaborated with scholars based in Germany, United States and Finland. Frequent co-authors include André Gensler, Janosch Henze, Nils Kristian Raabe, Thiemo Gruber, Sven Tomforde, Christian C. Gruber, Konrad Doll, Nils Appenrodt, Juergen Dickmann and Nicolas Scheiner. Their work appears in journals such as Information Sciences, Scientific Reports, Lecture notes in computer science, Energies and Machine Learning.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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Bernhard Sick

Bernhard Sick's Hit Papers

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All Works

About Bernhard Sick

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