Publikationen

Bonenberger, C., Schneider, M., Ertel, W., Schwenker, F. (2024). A Note on Linear Time Series Prediction. In: Hotho, A., Rudolph, S. (eds) KI 2024: Advances in Artificial Intelligence. KI 2024. Lecture Notes in Computer Science(), vol 14992 . Springer, Cham. https://doi.org/10.1007/978-3-031-70893-0_3

Berens, F., Koschinski, Y., Badami, M. K., Geimer, M., Elser, S., & Reischl, M. (2024). Adaptive Training for Robust Object Detection in Autonomous Driving Environments. IEEE Transactions on Intelligent Vehicles.

Berens, F., Ambs, J., Elser, S., & Reischl, M. (2024). A Novel Approach to Light Detection and Ranging Sensor Placement for Autonomous Driving Vehicles Using Deep Deterministic Policy Gradient Algorithm. SAE International Journal of Connected and Automated Vehicles, 7(12-07-03-0019).

Haasis, J., Bonenberger, C., Schneider, M. (2024). Instance Segmentation with a Novel Tree Log Detection Dataset. In: Hotho, A., Rudolph, S. (eds) KI 2024: Advances in Artificial Intelligence. KI 2024. Lecture Notes in Computer Science(), vol 14992 . Springer, Cham. https://doi.org/10.1007/978-3-031-70893-0_23

Bonenberger, C., Scholz, S., & Scheiter, N. (2024). Data-adaptive Dynamic Simulation via Structured Dynamic Mode Decomposition. In Tagungsband Kurzbeiträge, 27. Symposium Simulationstechnik (pp. 41-44). ARGESIM Report 47 (ISBN 978-3-903347-65-6).

Scholz, S., Bonenberger, C., Scheiter, N. & Berger, L. (2024). Simulation and Control of 2-Dimensional Anisotropic Heat Conduction. In Tagungsband Kurzbeiträge, 27. Symposium Simulationstechnik (pp. 45-48). ARGESIM Report 47 (ISBN 978-3-903347-65-6).

Locherer, M., Bonenberger, C., Ertel, W. et al. (2024). Multi-label semantic segmentation of magnetic resonance images of the prostate gland. Discov Artif Intell 4, 66. https://doi.org/10.1007/s44163-024-00162-z

Bonenberger, C., Schneider, M., Schwenker, F.,  and Ertel, W. (2023). A Novel Approach to Spectral Estimation and Moving Average Model Parameter Estimation. In IEEE Signal Processing Letters, vol. 30, pp. 1367-1371, doi: 10.1109/LSP.2023.3320564

Berens, F., Elser, S., & Reischl, M. (2023, September). MEMS LiDAR Sensor Simulation for Autonomous Driving: A Novel Framework Using Open-source Tools. In ISR Europe 2023; 56th International Symposium on Robotics (pp. 298-303). VDE.

Bonenberger, C., Ertel, W., Schneider, M., Schwenker, F. (2023). Structured Nonlinear Discriminant Analysis. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_3

Arff, B., Haasis, J., Thomas, J., Bonenberger, C., Höpken, W., & Stetter, R. (2023). Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT. Applied Sciences, 13(6), 3525.

Bonenberger, C., Ertel, W., Schwenker, F., & Schneider, M. (2022). Singular spectrum analysis and circulant maximum variance frames. Advances in Data Science and Adaptive Analysis, 14(03n04), 2250008. https://doi.org/10.1142/S2424922X22500085

Bonenberger, C., Schwenker, F., Ertel, W., & Schneider, M., (2022). Cyclic Nonlinear Correlation Analysis for Time Series. In IEEE Access, vol. 10, pp. 114223-114231, doi: 10.1109/ACCESS.2022.3218163

M. Kraus, N. Wagner, W. Minker, A. Agrawal, A. Schmidt, P. Krishna Prasad and W. Ertel. KURT: A Household Assistance Robot Capable of Proactive Dialogue. In Proceedings of the 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 855-859. IEEE, 2022

Bonenberger, C., Ertel, W., Schneider, M. (2021). k-Circulant Maximum Variance Bases. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham. https://doi.org/10.1007/978-3-030-87626-5_2

Berens, F., Elser, S., & Reischl, M. (2021). Genetic algorithm for the optimal LiDAR sensor configuration on a vehicle. IEEE sensors journal, 22(3), 2735-2743.

Hofer, D., Prasad, P.K., Schneider, M., (2021). Comparison of Anomaly Detection and Solution Strategies for Household Service Robotics using Knowledge Graphs, in: 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). pp. 1–6. https://doi.org/10.1109/IICAIET51634.2021.9573970

Krishna Prasad, P., Ertel, W., 2020. Knowledge Acquisition and Reasoning Systems for Service Robots: A Short Review of the State of the Art, in: 2020 5th International Conference on Robotics and Automation Engineering (ICRAE). pp. 36–45. https://doi.org/10.1109/ICRAE50850.2020.9310835

Pranav Krishna Prasad, I.C., Benjamin Staehle, Ertel, W., 2020. Grasping Unknown Objects Using Convolutional Neural Networks, in: Intelligent Systems and Applications. Springer. https://doi.org/10.1007/978-3-030-55190-2_51

Bonenberger, C., Kathan, B., Ertel, W., 2019. Feature-Based Gait Pattern Classification for a Robotic Walking Frame, in: Workshop on Advanced Analytics and Learning on Temporal Data (ECML/PKDD). Würzburg.

Ertel, W., 2019. Artificial Intelligence, the Spare Time Rebound Effect and how the ECG would avoid it, in: International Conference: Economy for the Common Good (ECGPW-2019). Bremen.

A. Agrawal, W.E., 2018a. Automatic Nursing Care Trainer Based on Machine Learning, in: KHD@IJCAI-ECAI-2018. Stockholm, pp. 53–59.

A. Agrawal, W.E., 2018b. Machine Learning Based Virtual Ergonomics Trainer in the Field of Nursing Care, in: Zukunft Der Pflege, Innovative Technologien Für Die Pflege. p. 106.

B. Stähle, W.E., 2018. Teaching ROS efficiently to mixed skill classes, in: European Robotics Forum 2018, Tampere, Finland, March.

Chernov, I., Ertel, W., 2018. Generating Optimal Gripper Orientation for Robotic Grasping of Unknown Objects using Neural Network, in: Federated AI for Robotics Workshop (FAIR), IJCAI-ECAI-18. Stockholm.

B. Weber-Fiori, M.W., B. Stähle, S. Pfiffner, B. Reiner, W. Ertel, 2017. Marvin, ein Assistenzroboter für Menschen mit körperlicher Behinderung im praktischen Einsatz, in: Digitale Transformation von Dienstleistungen Im Gesundheitswesen III, August, 269-285. Springer, pp. 269–285.

J. Reichold, A.P., A. Agrawal, M. Thurlings, I. Cohen, B. Weber-Fiori, A. Rölle, M. Hassan, M. Dürr, U. Pfeil, others, 2017. Human-Machine Interaction in Care-Education, in: Mensch Und Computer 2017-Workshopband. Gesellschaft für Informatik eV.

Schneider, M., 2017. Expected similarity estimation for large-scale anomaly detection (PhD Thesis). Universität Ulm.

A. Agrawal, A.S., 2016. A Comparative Study of Data Clustering Algorithms, in: Baden-Württemberg Center for Applied Research Symposium on Information and Communication Systems. p. 31.

A. Agrawal, W.E., 2016. Character Recognition in Satellite Images, in: Baden-Württemberg Center for Applied Research Symposium on Information and Communication Systems. p. 43.

M. Schneider, F.R., W. Ertel, 2016a. Expected similarity estimation for large-scale batch and streaming anomaly detection, May, 305-333. Machine Learning 105, 305–333.

M. Schneider, F.R., W. Ertel, 2016b. Kernel embeddings for large-scale anomaly detection, July. International Conference on Machine Learning (ICML 2016): Anomaly Detection Workshop.

M. Schneider, G.P., W. Ertel, 2016. Constant Time EXPected Similarity Estimation for Large-Scale Anomaly Detection., in: ECAI, August-September, 12-20. pp. 12–20.

M. Schneider, 2016. Probability inequalities for kernel embeddings in sampling without replacement, in: Artificial Intelligence and Statistics. pp. 66–74.

M. Schneider, G.P., W. Ertel, 2015. Kernel feature maps from arbitrary distance metrics, in: Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz), November, 137-150. Springer, pp. 137–150.

M. Schneider, W.E., Palm, G., 2015a. Constant Time EXPected Similarity Estimation using Stochastic Optimization. arXiv preprint arXiv:1511.05371.

M. Schneider, W.E., Palm, G., 2015b. Expected similarity estimation for large scale anomaly detection, in: Neural Networks (IJCNN), 2015 IEEE International Joint Conference on, Ireland, July, 1-8. IEEE, pp. 1–8.

M. Schneider, W.E., Palm, G., 2015c. Kernel Feature Maps from Arbitrary Distance Metrics, in: Proceedings of the 38th German Conference on Artificial Intelligence (KI), Dresden, Germany, September, 21-25.

R. Cubek, W.E., Palm, G., 2015a. A Critical Review on the Symbol Grounding Problem as an Issue of Autonomous Agents, in: Proceedings of the 38th German Conference on Artificial Intelligence (KI), Dresden, Germany, September, 21-25.

R. Cubek, W.E., Palm, G., 2015b. High-Level Learning from Demonstration with Conceptual Spaces and Subspace Clustering, in: Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, USA, May 26-30, 2015.

V. Zakharov, W.E., R. Cubek, 2015. Transparent Integration of a Real-Time Collision Safety System to a Motor Control Chain of a Service Robot, in: Proceedings of the 7th Annual International Conference on Technologies for Practical Robot Applications (TEPRA 2015), Boston, Massachusetts, USA, May 11-12, 2015.

B. Reiner, H.P., W. Ertel, Schneider, M., 2014. LAT: A simple Learning from Demonstration Method, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS”14), Chicago, Illinois, USA. https://doi.org/10.1109/IROS.2014.6943190

Schneider, M., F.Ramos, 2014. Transductive Learning for Multi-Task Copula Processes, in: Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic.

W. Ertel, B.W.-F., S. Pfiffner, B. Reiner, B. Stähle, M. Schneider, J. Schmal, Winter, M.H.-J., 2014. A service robot platform for individuals with disabilities, in: Proceedings of the 1. BW-CAR Symposium on Information and Communication Systems (SInCom), Villingen-Schwenningen, Germany. pp. 84–88.

W. Ertel, M.F., R. Lehmann, R. Medow, Meyer, A., 2014. Model Free Diagnosis of Pneumatic Systems using Machine Learning, in: 9th International Fluid Power Conference. Aachen.

N. Vien, W. Ertel, V.H. Dang, Chung, T., 2013. Monte Carlo Tree Search for Bayesian Reinforcement Learning. Applied Intelligence 39, 345–353. https://doi.org/10.1007/s10489-012-0416-2

Schädle, S., Ertel, W., 2013. Dexterous Manipulation Using Hierarchical Reinforcement Learning, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2013, Karlsruhe, Deutschland.

Bertsche, M., Fromm, T., Ertel, W., 2012. BOR3D: A Use-Case-Oriented Software Framework for 3-D Object Recognition, in: IEEE Conference on Technologies for Practical Robot Applications (TePRA).

M. Tokic, P. Ertle, G. Palm, D. Söffker, Voos, H., 2012. Robust exploration/exploitation trade-offs in safety-critical applications, in: Proceedings of the 8th International Symposium on Fault Detection, Supervision and Safety of Technical Processes, Mexico City, Mexico, IFAC.

P. Ertle, H.V., Söffker, D., 2012. Utilizing Dynamic Hazard Knowledge for Risk Sensitive Action Planning of Autonomous Robots, in: Proceedings of the IEEE International Symposium on Robotic and Sensors Environments ROSE, Magdeburg, Germany.

P. Ertle, M. Tokic, R. Cubek, H. Voos, Söffker, D., 2012. Towards learning of safety knowledge from human demonstrations, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS”12), Vilamoura, Algarve, Portugal.

P. Ertle, M. Tokic, T. Bystricky, M. Ebel, H. Voos, Söffker, D., 2012. Conceptual design of a dynamic risk-assessment server for autonomous robots, in: Proceedings of the 7th German Conference on Robotics, Pages 250-254. VDE Verlag.

Cubek, R., Ertel, W., 2012. Conceptual Similarity as a Key to High-Level Robot Programming by Demonstration, in: ROBOTIK 2012, 7th German Conference on Robotics, Munich, Germany.

T. Fromm, B.S., Ertel, W., 2012. Robust Multi-Algorithm Object Recognition Using Machine Learning Methods, in: IEEE International Conference on Multisensor Fusion and Information Integration, Hamburg, Germany.

Tokic, M., Ammar, H.B., 2012. Teaching reinforcement learning using a physical robot, in: Proceedings of the Workshop on Teaching Machine Learning at the 29th International Conference on Machine Learning, Edinburgh, UK.

Tokic, M., Palm, G., 2012a. Adaptive exploration using stochastic neurons, in: Proceedings of the 22nd International Conference on Artificial Neural Networks (ICANN”12), Lausanne, Switzerland.

Tokic, M., Palm, G., 2012b. Gradient algorithms for exploration/exploitation trade-offs: Global and local variants, in: Proceedings of the 5th INNS IAPR TC3 GIRPR International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR”12), Trento, Italy.

Vien, N., Ertel, W., 2012a. Learning via Human Feedback in Continuous State and Action Spaces, in: 2012 AAAI Fall Symposium Series, Robots Learning Interactively from Human Teachers, (RLIHT), Arlington, Virginia, USA.

Vien, N., Ertel, W., 2012b. Monte Carlo Tree Search for Bayesian Reinforcement Learning, in: 11th International Conference on Machine Learning and Applications (ICMLA 2012) December 12-15, Boca Raton, Florida, USA.

Vien, N., Ertel, W., 2012c. Reinforcement learning combined with human feedback in continuous state and action spaces, in: IEEE Conference on Development and Learning / EpiRob 2012 (ICDL-EpiRob), San Diego, USA.

Cubek, R., Ertel, W., 2011. Learning and Execution of High-Level Concepts with Conceptual Spaces and PDDL, in: 3rd Workshop on Learning and Planning, ICAPS (21st International Conference on Automated Planning and Scheduling). Freiburg, Germany.

Ertle, P., Gamrad, D., Voos, H., Söffker, D., 2010. Action Planning for Autonomous Systems with respect to Safety Aspects, in: In Proc. IEEE International Conference on Systems Man and Cybernetics (SMC) 2010. Istanbul, Turkey.

Ertle, P., Söffker, D., 2010. Towards Risk Analysis to enable Safe Service Robotics, in: Interface and Interaction Design for Learning and Simulation Environments. Berlin.

P. Ertle, D.S., H. Voos, 2010a. Development of Safe Autonomous Mobile Service Robots using an Active Integrated Approach, in: In Proceedings of the International Symposium on Robotics ISR 2010. Munich, Germany.

P. Ertle, D.S., H. Voos, 2010b. On Risk Formalization of On-Line Risk Assessment for Safe Decision Making in Robotics, in: In Proceedings of the 7th IARP Workshop on Technical Challenges for Dependable Robots in Human Environments. Toulouse, France.

Schneider, M., Cubek, R., Fromm, T., Ertel, W., 2010. Combining Gaussian Processes and Conventional Path Planning in a Learning from Demonstration Framework, in: Proceedings of the Eurobot Conference 2010. Rapperswil-Jona (CH).

Schneider, M., Ertel, W., 2010. Robot Learning by Demonstration with Local Gaussian Process Regression, in: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). Taipeh, Taiwan.

Voos, H., Ammar, H.B., 2010. Non-linear tracking and landing controller for quadrotor aerial robot, in: Proc. IEEE Multi-Conference on Systems and Control. Yokohama, Japan.

Voos, H., Ammar, H.B., Ertel, W., 2010. Controller Design for Quadrotor UAV”s using Reinforcement Learning, in: Proc. IEEE Multi-Conference on Systems and Control. Yokohama, Japan.

Voos, H., 2009. Model Predictive Collaborative Motion Planning and Control of Mobile Robots including Safety Aspects, in: In Proc 14th Int. Conf. on Advanced Robotics (ICAR). Munich, Germany.

Voos, H., Ertle, P., 2009. Online Risk Assessment for Safe Autonomous Mobile Robots - A Perspective, in: In Proceedings of the 7th Workshop on Advanced Control and Diagnosis, Zielona Góra. Poland.

W. Ertel, R.C., M. Schneider, Tokic, M., 2009. The Teaching-Box: A Universal Robot Learning Framework, in: Proceedings of the 14th International Conference on Advanced Robotics (ICAR 2009). Munich.