Pu­bli­ka­tio­nen

Wissenschaftliche Publikationen des Instituts der letzten zwei Jahre

2021

  • OHODIN -- Online Anomaly Detection for Data Streams. Gruhl, Christian; Tomforde, Sven (2021).
     
  • Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. He, Yujiang; Huang, Zhixin; Sick, Bernhard (2021).
     
  • Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. Haase-Schütz, Christian; Stal, Rainer; Hertlein, Heinz; Sick, Bernhard (2021).
     
  • Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks. Schneegans, Jan; Bieshaar, Maarten; Heidecker, Florian; Sick, Bernhard (2021). 375–389.
     
  • Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. Schreiber, Jens; Sick, Bernhard (2021).
     
  • Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. Heidecker, Florian; Hannan, Abdul; Bieshaar, Maarten; Sick, Bernhard (2021). 361–374.
     
  • Self-improving system integration: Mastering continuous change. Bellman, Kirstie; Botev, Jean; Diaconescu, Ada; Esterle, Lukas; Gruhl, Christian; Landauer, Christopher; Lewis, Peter R.; Nelson, Phyllis R.; Pournaras, Evangelos; Stein, Anthony; Tomforde, Sven in Future Generation Computer Systems (2021). 117 29–46.
     
  • Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders. Möller, Felix; Botache, Diego; Huseljic, Denis; Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard (2021).
     
  • Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution. Zernetsch, Stefan; Schreck, Steven; Kress, Viktor; Doll, Konrad; Sick, Bernhard (2021).
     
  • Multi-annotator Probabilistic Active Learning. Herde, Marek; Kottke, Daniel; Huseljic, Denis; Sick, Bernhard (2021).
     
  • Novelty based Driver Identification on RR Intervals from ECG Data. Heidecker, Florian; Gruhl, Christian; Sick, Bernhard (2021). 407–421.
     
  • Novelty detection in continuously changing environments. Gruhl, Christian; Sick, Bernhard; Tomforde, Sven in Future Generation Computer Systems (2021). 114 138–154.
     
  • Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. Huseljic, Denis; Sick, Bernhard; Herde, Marek; Kottke, Daniel (2021).
     
  • Efficient Solution of Distributed MIP in Control of Networked Systems. Z.Liu; Stursberg, O. in Proc. in Applied Mathematics and Mechanics (PAMM) (2021). 20(1, Section 20) 1–2.
     
  • Distributed Control of Networked Systems with Nonlinear Dynamics and Coupling Constraints. Liu, Z.; Stursberg, O. (2021). -to appear-.
     
  • Distributed Solution of MIQP Problems Arising for Networked Systems with Coupling Constraints. Liu, Z.; Stursberg, O. (2021). -to appear-.
     
  • Polytopic Input Constraints in Learning-Based Optimal Control Using Neural Networks. Markolf, L.; Stursberg, O. (2021). -to appear-.
     
  • 3D Thermography for the Measurement of Surface Heat Dissipation. Schmoll, Robert; Schramm, Sebastian; Breitenstein, Tom; Kroll, Andreas (2021).
     
  • Multispectral Geometric Calibration of Cameras in Visual and Infrared Spectral Range. Schramm, Sebastian; Rangel, Johannes; Aguirre Salazar, Daniela; Schmoll, Robert; Kroll, Andreas in IEEE Sensors (2021). 21(2) 2159–2168.
     
  • Prediction of near surface residual stress states for hard turned specimens using data driven nonlinear models. Schott, Christopher; Wittich, Felix; Kroll, Andreas; Niendorf, Thomas (2021).
     
  • Catadioptric Stereo Optical Gas Imaging System for Scene Flow Computation of Gas Structures. Rangel, J.; Schmoll, R.; Kroll, A. in IEEE Sensors Journal (2021). 21(5) 6811–6820.
     
  • Zur approximativen Maximum-Likelihood-Schätzung dynamischer Multi-Modelle vom Typ Takagi-Sugeno: Methodik und Anwendung auf einen Servo-Pneumatikantrieb. Kroll, Andreas; Fischer, Jana in at -- Automatisierungstechnik (2021).
     
  • Toward optimal probabilistic active learning using a Bayesian approach. Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard in Machine Learning (2021).
     
  • AdaPT: Adaptable Particle Tracking for spherical microparticles in lab on chip systems. Dingel, Kristina; Huhnstock, Rico; Knie, André; Ehresmann, Arno; Sick, Bernhard in Computer Physics Communications (2021). 262 107859.
     
  • Uncertainty and Utility Sampling with Pre-Clustering. Huang, Zhixin; He, Yujiang; Vogt, Stephan; Sick, Bernhard (2021).
     
  • AI-Based On The Fly Design of Experiments in Physics and Engineering. Dingel, Kristina; Liehr, Alexander; Vogel, Michael; Degener, Sebastian; Meier, David; Niendorf, Thomas; Ehresmann, Arno; Sick, Bernhard (2021).
     
  • About the Ambiguity of Augmentation for 3D Object Detection in Autonomous Driving. Reuse, Matthias; Simon, Martin; Sick, Bernhard (2021).
     
  • Object Detection For Automotive Radar Point Clouds -- A Comparison. Scheiner, Nicolas; Kraus, Florian; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard in AI Perspectives (2021).
     
  • Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. Reichert, Hannes; Lang, Lukas; Rösch, Kevin; Bogdoll, Daniel; Doll, Konrad; Sick, Bernhard; Reuss, Hans-Christian; Stiller, Christoph; Zöllner, J. Marius (2021).
     
  • Digital Shadows in SISSY Systems: A Concept Using Generative Modelling. Al-Falouji, Ghassan; Gruhl, Christian; Tomforde, Sven (2021).
     
  • The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models. Gruhl, Christian; Hannan, Abdul; Huang, Zhixin; Nivarthi, Chandana; Vogt, Stephan (2021).
     
  • Smart Infrastructure: A Research Junction. Hetzel, Manuel; Reichert, Hannes; Doll, Konrad; Sick, Bernhard (2021).
     
  • Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information. Zernetsch, Stefan; Trupp, Oliver; Kress, Viktor; Doll, Konrad; Sick, Bernhard (2021).
     
  • A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving. Bieshaar, Maarten; Herde, Marek; Huselijc, Denis; Sick, Bernhard (2021).
     
  • Description of Corner Cases in Automated Driving: Goals and Challenges. Bogdoll, Daniel; Breitenstein, Jasmin; Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard; Fingscheidt, Tim; Zöllner, J. Marius (2021).
     
  • CLeaR: An adaptive continual learning framework for regression tasks. He, Yujiang; Sick, Bernhard in AI Perspectives (2021). 3(1) 2.
     
  • Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. Kress, Viktor; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard (2021). 2723–2730.
     
  • Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands. Botache, Diego; Bethke, Florian; Hardieck, Martin; Bieshaar, Maarten; Brabetz, Ludwig; Ayeb, Mohamed; Zipf, Peter; Sick, Bernhard (2021).
     
  • Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration. Reginka, Meike; Hoang, Hai; Efendi, Özge; Merkel, Maximilan; Huhnstock, Rico; Holzinger, Dennis; Dingel, Kristina; Sick, Bernhard; Bertinetti, Daniela; Herberg, Friedrich; Ehresmann, Arno in Langmuir (2021).
     
  • Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders. Hannan, Abdul; Gruhl, Christian; Sick, Bernhard (2021).
     
  • Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists. Schneegans, Jan; Eilbrecht, Jan; Zernetsch, Stefan; Bieshaar, Maarten; Doll, Konrad; Stursberg, Olaf; Sick, Bernhard (2021).
     
  • Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast. Schreiber, Jens; Vogt, Stephan; Sick, Bernhard (2021).
     
  • An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving. Heidecker, Florian; Breitenstein, Jasmin; Rösch, Kevin; Löhdefink, Jonas; Bieshaar, Maarten; Stiller, Christoph; Fingscheidt, Tim; Sick, Bernhard (2021).
     
  • Stability Analysis for State Feedback Control Systems Established as Neural Networks with Input Constraints. Markolf, Lukas; Stursberg, Olaf in Proc. of the 18th Int. Conf. on Informatics in Control, Automation and Robotics (2021). 146–155.
     
  • A Study on Model-based Optimization of Vaccination Strategies Against Epidemic Virus Spread. Liu, Zonglin; Stursberg, Olaf in Proc. of the 18th Int. Conf. on Informatics in Control, Automation and Robotics (2021). - to appear -.
     
  • Learning-Based Optimal Control of Constrained Switched Linear Systems Using Neural Networks. Markolf, Lukas; Stursberg, Olaf in Proc. of the 18th International Conference on Informatics in Control, Automation and Robotics (2021). 90–98.
     
  • On Evaluating deep-learning-based Optical Flow Methods for Gas Velocity Estimation with Optical Gas Imaging Cameras. Rangel, Johannes; Duenas, Juan; Schmoll, Robert; Kroll, Andreas (2021).
     

2020

  • Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. Haase-Schütz, Christian; Stal, Rainer; Hertlein, Heinz; Sick, Bernhard in arXiv e-prints (2020). arXiv:2002.02705.
     
  • On Scene Flow Computation of Gas Structures with Optical Gas Imaging Cameras. Rangel, J.; Schmoll, R.; Kroll, A. (2020). 174–182.
     
  • Challenges of Trajectory Planning with Integrator Models on Curved Roads. Eilbrecht, J.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 15797–15804.
     
  • Robust Point-to-Set Control of Hybrid Systems with UncertaintiesUsing Constraint Tightening. Liu, Z.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 6888–6894.
     
  • Trajectory Planning for Autonomous Vehicles combining Nonlinear Optimal Control and Supervised Learning. Markolf, L.; Eilbrecht, J.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 15817–15823.
     
  • Online Control of Affine Systems in Stochastically Modeled Contexts. Flues, P.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 3416–3422.
     
  • Balanced Stochastic Optimal Control of Uncertain Linear Systems with Constraints. Hahn, J.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 7254–7260.
     
  • Efficient Solution of Distributed MILP in Control of Networked Systems. Liu, Z.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 6805–6811.
     
  • On considering the output in space-filling test signal designs for the identification of dynamic Takagi-Sugeno models. Gringard, Matthias; Kroll, Andreas (2020).
     
  • Neither PER, nor TIM1, nor CRY2 alone are Essential Components of the Molecular Circadian Clockwork in the Madeira Cockroach. Werckenthin, A.; Huber, J.; Arnold, T.; Koziarek, S.; Plath, M.; Plath, J.A.; Stursberg, O.; Herzel, H.; Stengl, M. in PLoS ONE (2020). 15(8) 1–26.
     
  • Evaluierung von Verfahren zur optischen Bestimmung von Gasgeschwindigkeiten. Dierks, Sören; Kroll, Andreas in TM -- Technisches Messen (2020). 87(1) 66–77.
     
  • Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models. Kahl, Matthias; Kroll, Andreas (2020). (Vol. 53) 1182–1187.
     
  • On the vanishing and exploding gradient problem in Gated Recurrent Units. Rehmer, Alexander; Kroll, Andreas (2020).
     
  • Data Selection for System Identification (DS4SID) from Logged Process Records of Continuously Operated Plants. Arengas, David; Kroll, Andreas in at -- Automatisierungstechnik (2020). 68(5) 347–359.
     
  • Einsatz von maschinellem Lernen für die Rezyklat-Verarbeitung. Moritzer, Elmar; Hopp, Matthias; Wittke, Marius; Deuse, Jochen; Richter, Ralph; Schmitt, Jacqueline; Kroll, Lukas Schulte Andreas; Schrodt, Alexander in WAK -- Jahresmagazin Ingenieurwissenschaften (2020). 70–73.
     
  • Toolbox zum Testsignalentwurf für Standardtestsignale für die Identifikation von Eingrößensystemen: Prozessmodellfreie und -basierte Methoden. Himmelsbach, Matthias; Kroll, Andreas (2020).
     
  • Set-based Scheduling for Highway Entry of Autonomous Vehicles. Eilbrecht, J.; Stursberg, O. in IFAC-PapersOnline (2020). 53(2) 15605–15612.
     
  • Toward Optimal Probabilistic Active Learning Using a Bayesian Approach. Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard in arXiv e-prints (2020). arXiv:2006.01732.
     
  • Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?. Scheiner, Nicolas; Schumann, Ole; Kraus, Florian; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2020). 1–8.
     
  • Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets. Bieshaar, Maarten; Schreiber, Jens; Vogt, Stephan; Gensler, André; Sick, Bernhard in arXiv e-prints (2020). arXiv:2010.05898.
     
  • Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. He, Y.; Henze, J.; Sick, B. (2020).
     
  • Probabilistic upscaling and aggregation of wind power forecasts. Henze, Janosch; Siefert, Malte; Bremicker-Trübelhorn, Sascha; Asanalieva, Nazgul; Sick, Bernhard in Energy, Sustainability and Society (2020). 10(1) 15.
     
  • Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?. Tomforde, Sven; Gruhl, Christian (2020). 54–59.
     
  • Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. Pham Minh, T.; Kottke, D.; Tsarenko, A.; Gruhl, C.; Sick, B. (2020).
     
  • Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks. Kress, V.; Schreck, S.; Zernetsch, S.; Doll, K.; Sick, B. (2020). 2723–2730.
     
  • Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. Englhardt, Adrian; Trittenbach, Holger; Kottke, Daniel; Sick, Bernhard; Böhm, Klemens in arXiv e-prints (2020). arXiv:2009.13853.
     
  • Normal-Wishart clustering for novelty detection. Gruhl, Christian; Schmeißing, Jörn; Tomforde, Sven; Sick, Bernhard (2020). 64–69.
     
  • Knowledge Representations in Technical Systems -- A Taxonomy. Scharei, Kristina; Heidecker, Florian; Bieshaar, Maarten (2020, Januarie). arXiv:2001.04835.
     
  • A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Tomforde, Sven; Gruhl, Christian; Sick, Bernhard (2020). 166–169.
     
  • Extended Coopetitive Soft Gating Ensemble. Deist, Stephan; Schreiber, Jens; Bieshaar, Maarten; Sick, Bernhard in arXiv e-prints (2020). arXiv:2004.14026.
     
  • Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. Meier, David; Hartmann, Gregor; Völker, Jens; Viefhaus, Jens; Sick, Bernhard O. Chubar, K. Sawhney (reds.) (2020). (Vol. 11493) 71–77.
     
  • Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. He, Yujiang; Henze, Janosch; Sick, Bernhard (2020). (Vol. 53) 12175–12182.
     
  • Representation Learning in Power Time Series Forecasting. Henze, Janosch; Schreiber, Jens; Sick, Bernhard W. Pedrycz, S.-M. Chen (reds.) (2020). 67–101.
     
  • Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. Scheiner, Nicolas; Kraus, Florian; Wei, Fangyin; Phan, Buu; Mannan, Fahim; Appenrodt, Nils; Ritter, Werner; Dickmann, Jurgen; Dietmayer, Klaus; Sick, Bernhard; Heide, Felix (2020).
     
  • On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning. Wittich, Felix; Kistner, Lars; Kroll, Andreas; Schott, Christopher; Niendorf, Thomas in tm -- Technisches Messen (2020). 87 732–741.
     

2019

  • Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks. Zernetsch, S.; Reichert, H.; Kress, V.; Doll, K.; Sick, B. (2019). 810–815.
     
  • Hierarchical Solution of Non-Convex Optimal Control Problems with Application to Autonomous Driving. Eilbrecht, J.; Stursberg, Olaf in European Journal of Control (2019). 50 188–197.
     
  • Towards Corner Case Identification in Cyclists’ Trajectories. Heidecker, F.; Bieshaar, M.; Sick, B. (2019).
     
  • On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes. Rudolph, Stefan; Tomforde, Sven; Hähner, Jörg in arXiv e-prints (2019). arXiv:1905.04205.
     
  • A Multi-Stage Clustering Framework for Automotive Radar Data. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2019). 2060–2067.
     
  • Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems. Lesch, V.; Krupitzer, C.; Tomforde, S. (2019). 1–8.
     
  • On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. D’Angelo, M.; Gerasimou, S.; Ghahremani, S.; Grohmann, J.; Nunes, I.; Pournaras, E.; Tomforde, S. (2019). 13–24.
     
  • Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices. Scheiner, Nicolas; Haag, Stefan; Appenrodt, Nils; Duraisamy, Bharanidhar; Dickmann, Jürgen; Fritzsche, Martin; Sick, Bernhard (2019). 1–10.
     
  • Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid. Schreiber, Jens in Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.) (2019). (Vol. 13) 75–87.
     
  • Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. Schreiber, Jens; Jessulat, Maik; Sick, Bernhard I. V. Tetko, V. Krurková, P. Karpov, F. Theis (reds.) (2019). 550–564.
     
  • Distributed Model Predictive Control of Wind Farms for Short-Term Grid Support. Theissen, M.; Stursberg, O. (2019). 1–5.
     
  • Recursive Feasibility and Stability of MPC with Time-Varying and Uncertain State Constraints. Liu, Z.; Stursberg, O. (2019). 1766–1771.
     
  • Start Intention Detection of Cyclists using an LSTM Network. Kress, Viktor; Jung, Janis; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard C. Draude, M. Lange, B. Sick (reds.) (2019). 219–228.
     
  • Robust Distributed MPC for Disturbed Affine Systems Using Predictions of Time-Varying Communication. Hahn, J.; Stursberg, O. (2019). 56–62.
     
  • Sichere Trajektorienplanung für autonome Fahrzeuge unter Verwendung steuerbarer und erreichbarer Mengen. Eilbrecht, J.; Heß, Daniel; Köester, F.; Stursberg, O. (2019). - to appear -.
     
  • Reducing Computation Times for Planning of Reference Trajectories in Cooperative Autonomous Driving. Eilbrecht, J.; Stursberg, O. (2019). 114–120.
     
  • Efficient Optimal Control of Hybrid Systems with Conditioned Transitions. Liu, Z.; Stursberg, O. (2019). 223–230.
     
  • Distributed Control of Networked Systems with Coupling Constraints. Liu, Z.; Stursberg, Olaf in at-Automatisierungstechnik (2019). 67(12) 1007–1018.
     
  • Zur Schätzung zulässiger Parametermengen nichtlinearer Takagi-Sugeno-Multi-Modelle mit garantierten Fehlerschranken. Wittich, Felix; Kahl, Matthias; Kroll, Andreas (2019). 247–254.
     
  • On Using Gated Recurrent Units for Nonlinear System Identification. Rehmer, Alexander; Kroll, Andreas (2019). 2504–2509.
     
  • A Data Selection Method for large Databases for System Identification of MISO Models Based on Recursive Instrumental Variables. Arengas, David; Kroll, Andreas (2019). 357–362.
     
  • On Nonlinear Empirical Modeling of Residual Stress Profiles in Hard Turning. Wittich, Felix; Kahl, Matthias; Kroll, Andreas; Zinn, Wolfgang; Niendorf, Thomas (2019). 3235–3240.
     
  • Emerging Self-Integration through Coordination of Autonomous Adaptive Systems. Lesch, V.; Krupitzer, C.; Tomforde, S. (2019). 6–9.
     
  • Pose Based Start Intention Detection of Cyclists. Kress, V.; Jung, J.; Zernetsch, S.; Doll, K.; Sick, B. (2019). 2381–2386.
     
  • Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning. Vogt, Stephan; Braun, Axel; Dobschinski, Jan; Sick, Bernhard U. Betancourt, T. Ackermann (reds.) (2019).
     
  • The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019. Krupitzer, Christian; Tomforde, Sven C. Draude, M. Lange, B. Sick (reds.) (2019). 545–554.
     
  • Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. Sandrock, C.; Herde, M.; Calma, A.; Kottke, D.; Sick, B. (2019). 1–8.
     
  • INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge), 23.-26.9.2019, Kassel, Deutschland Draude, Claude; Lange, Martin; Sick, Bernhard in LNI (2019). (Vol. P-295) GI.
     
  • From ``Normal’’ to ``Abnormal’’: A Concept for Determining Expected Self-Adaptation Behaviour. Tomforde, Sven in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 126–129.
     
  • Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Knauer, Uwe; Styp von Rekowski, Cornelius; Stecklina, Marianne; Krokotsch, Tilman; Pham Minh, Tuan; Hauffe, Viola; Kilias, David; Ehrhardt, Ina; Sagischewski, Herbert; Chmara, Sergej; Seiffert, Udo in Remote Sensing (2019). 11(23)
     
  • Explicit Consideration of Resilience in Organic Computing Design Processes. Tomforde, S.; Gelhausen, P.; Gruhl, C.; Haering, I.; Sick, B. (2019). 1–6.
     
  • Limitations of Assessing Active Learning Performance at Runtime. Kottke, Daniel; Schellinger, Jim; Huseljic, Denis; Sick, Bernhard in arXiv e-prints (2019). arXiv:1901.10338.
     
  • Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning. Goldhammer, M.; Köhler, S.; Zernetsch, S.; Doll, K.; Sick, B.; Dietmayer, K. in IEEE Transactions on Intelligent Transportation Systems (2019). 1–11.
     
  • Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks. Schneegans, Jan; Bieshaar, Maarten in Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.) (2019). (Vol. 13) 45–60.
     
  • Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime. Stein, A.; Tomforde, S. (2019). 32–35.
     
  • Mutual Influence-aware Runtime Learning of Self-adaptation Behavior. Rudolph, Stefan; Tomforde, Sven; Hähner, Jörg in ACM Transactions on Autonomous and Adaptive Systems (2019). 14(1) 4:1–4:37.
     
  • Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2019). 642–649.
     
  • Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields. Hanika, Tom; Herde, Marek; Kuhn, Jochen; Leimeister, Jan Marco; Lukowicz, Paul; Oeste-Reiß, Sarah; Schmidt, Albrecht; Sick, Bernhard; Stumme, Gerd; Tomforde, Sven; Zweig, Katharina Anna in arXiv e-prints (2019). arXiv:1905.07264.
     
  • Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. Schreiber, Jens; Buschin, Artjom; Sick, Bernhard K. David, K. Geihs, M. Lange, G. Stumme (reds.) (2019). 585–598.
     
  • Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition. Botache, Diego; Dandan, Liu; Bieshaar, Maarten; Sick, Bernhard C. Draude, M. Lange, B. Sick (reds.) (2019). 229–238.
     
  • Self-Improving System Integration -- On a Definition and Characteristics of the Challenge. Bellman, K. L.; Gruhl, C.; Landauer, C.; Tomforde, S. (2019). 1–3.
     
  • CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed. Barnes, C. M.; Bellman, K.; Botev, J.; Diaconescu, A.; Esterle, L.; Gruhl, C.; Landauer, C.; Lewis, P. R.; Nelson, P. R.; Stein, A.; Stewart, C.; Tomforde, S. (2019). 52–55.
     
  • Decision Support with Hybrid Intelligence. Calma, Adrian; Dellermann, Dominik in Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.) (2019). (Vol. 13) 143–153.
     
  • Pose Based Trajectory Forecast of Vulnerable Road Users. Kress, V.; Zernetsch, S.; Doll, K.; Sick, B. (2019).
     
  • Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2019). 5–9.
     
  • Using grid supporting flexibility in electricity distribution networks. König, Immanuel; Heilmann, Erik; Henze, Janosch; David, Klaus; Wetzel, Heike; Sick, Bernhard K. David, K. Geihs, M. Lange, G. Stumme (reds.) (2019). 531–544.
     
  • Zur Homogenisierung von Testsignalen für die nichtlineare Systemidentifikation. Gringard, Matthias; Kroll, Andreas in at -- Automatisierungstechnik (2019). 67(10) 820–832.