Pu­bli­ka­tio­nen

[ 2023 ] [ 2022 ] [ 2021 ] [ 2020 ] [ 2019 ] [ 2018 ] [ 2017 ] [ 2016 ] [ 2015 ] [ 2014 ] [ 2013 ] [ 2012 ] [ 2011 ] [ 2010 ] [ 2007 ] [ 2006 ]

2023 [ nach oben ]

  • 1.
    Eider, M., Sick, B., Berl, A.: Context-aware recommendations for extended electric vehicle battery lifetime. Sustainable Computing: Informatics and Systems (SUSCOM). 37, 100845 (2023).
     

2022 [ nach oben ]

  • 1.
    Beddar-Wiesing, S., D’Inverno, G.A., Graziani, C., Lachi, V., Moallemy-Oureh, A., Scarselli, F., Thomas, J.: Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs. arXiv e-prints. arXiv:2210.03990 (2022).
     
  • 2.
    Nivarthi, C.P., Vogt, S., Sick, B.: Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting. International Conference on Machine Learning and Applications (ICMLA). IEEE (2022).
     
  • 3.
    Nivarthi, C.P.: Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 47–59. kassel university press (2022).
     
  • 4.
    Huhnstock, R., Reginka, M., Sonntag, C., Merkel, M., Dingel, K., Sick, B., Vogel, M., Ehresmann, A.: Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes. Scientific Reports. 12, 1–10 (2022).
     
  • 5.
    Loeser, I., Braun, M., Gruhl, C., Menke, J.-H., Sick, B., Tomforde, S.: The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Energies. 15, 881 (2022).
     
  • 6.
    Beddar-Wiesing, S.: Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs. ACM/SIGAPP Symposium on Applied Computing (SAC). bll. 604–609. ACM (2022).
     
  • 7.
    Moallemy-Oureh, A.: Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs. ACM/SIGAPP Symposium on Applied Computing (SAC). bll. 600–603. ACM (2022).
     
  • 8.
    Pham, T.: Stream-Based Active Learning in Changing Environments under Verification Latency. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 152–164. kassel university press (2022).
     
  • 9.
    Rösch, K., Heidecker, F., Truetsch, J., Kowol, K., Schicktanz, C., Bieshaar, M., Sick, B., Stiller, C.: Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving. IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI (2022).
     
  • 10.
    Draude, C., Gruhl, C., Hornung, G., Kropf, J., Lamla, J., Leimeister, J.M., Sick, B., Stumme, G.: Social Machines. Informatik Spektrum. 45, 38–42 (2022).
     
  • 11.
    Gruhl, C., Tomforde, S., Sick, B.: Self-Aware Microsystems. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 126–127. IEEE (2022).
     
  • 12.
    Hassouna, M.: Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning. Organic Computing -- Doctoral Dissertation Colloquium 2022. kassel university press (2022).
     
  • 13.
    Krupitzer, C., Gruhl, C., Sick, B., Tomforde, S.: Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario. Information and Software Technology. 145, 106826 (2022).
     
  • 14.
    Decke, J., Engelhardt, A., Rauch, L., Degener, S., Sajjadifar, S., Scharifi, E., Steinhoff, K., Niendorf, T., Sick, B.: Predicting flow stress behavior of an AA7075 alloy using machine learning methods. Crystals. 9, 1–19 (2022).
     
  • 15.
    Kress, V., Jeske, F., Zernetsch, S., Doll, K., Sick, B.: Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories. IEEE Transactions on Intelligent Vehicles. (2022).
     
  • 16.
    Tomforde, S., Krupitzer, C. reds: Organic Computing -- Doctoral Dissertation Colloquium 2021. kassel university press (2022).
     
  • 17.
    Meier, D., Ramirez, L.V., Völker, J., Viefhaus, J., Sick, B., Hartmann, G.: Optimizing a superconducting radio-frequency gun using deep reinforcement learning. Physical Review Accelerators and Beams. 25, 104604 (2022).
     
  • 18.
    Beddar-Wiesing, S., D’Inverno, G.A., Graziani, C., Lachi, V., Moallemy-Oureh, A., Scarselli, F.: On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs. Workshp on Mining and Learning on Graphs (MLG), ECML PKDD. bll. 1–13 (2022).
     
  • 19.
    Decke, J., Schmeißing, J., Botache, D., Bieshaar, M., Sick, B., Gruhl, C.: NDNET: A Unified Framework for Anomaly and Novelty Detection. International Conference on Architecture of Computing Systems (ARCS). bll. 197–210. Springer (2022).
     
  • 20.
    Schreiber, J., Sick, B.: Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts. Energies. 15, 8062 (2022).
     
  • 21.
    Ali, M.W.: Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility. IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). bll. 57–60. IEEE (2022).
     
  • 22.
    Thomas, J.M., Moallemy-Oureh, A., Beddar-Wiesing, S., Holzhüter, C.: Graph Neural Networks Designed for Different Graph Types: A Survey. arXiv e-prints. arXiv:2204.03080 (2022).
     
  • 23.
    Westmeier, T., Botache, D., Bieshaar, M., Sick, B.: Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches. IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2022).
     
  • 24.
    Moallemy-Oureh, A., Beddar-Wiesing, S., Nather, R., Thomas, J.M.: FDGNN: Fully Dynamic Graph Neural Network. arXiv e-prints. arXiv:2206.03469 (2022).
     
  • 25.
    Herde, M., Huang, Z., Huseljic, D., Kottke, D., Vogt, S., Sick, B.: Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. arXiv e-prints. arXiv:2210.06112 (2022).
     
  • 26.
    Rauch, L., Huseljic, D., Sick, B.: Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 27–42 (2022).
     
  • 27.
    Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD sampling with approximation guarantees for the decision boundary. Machine Learning. 111, 1349–1375 (2022).
     
  • 28.
    Heidecker, F.: Detecting Corner Case in the Context of Highly Automated Driving. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 60–73. kassel university press (2022).
     
  • 29.
    He, Y., Huang, Z., Sick, B.: Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning. Workshop on Interactive Machine Learning Workshop (IMLW), AAAI. bll. 1–6 (2022).
     
  • 30.
    Ali, M.W.: Deep Adaptive Knowledge-Aware Learning for E-Mobility. Organic Computing - Doctoral Dissertation Colloquium 2022. kassel university press (2022).
     
  • 31.
    Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses. Scientific Reports. 12, 1–14 (2022).
     
  • 32.
    He, Y.: Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 125–140. kassel university press (2022).
     
  • 33.
    Dingel, K.: Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 86–98. kassel university press (2022).
     
  • 34.
    Huang, Z.: Active Learning in Multivariate Time Series Anomaly Detection. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 113–124. kassel university press (2022).
     
  • 35.
    Kottke, D., Sandrock, C., Krempl, G., Sick, B.: A Stopping Criterion for Transductive Active Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). Springer (2022).
     
  • 36.
    Huseljic, D., Herde, M., Muejde, M., Sick, B.: A Review of Uncertainty Calibration in Pretrained Object Detectors. arXiv e-prints. arXiv:2210.02935 (2022).
     
  • 37.
    Schneegans, J., Bieshaar, M., Sick, B.: A Practical Evaluation of Active Learning Approaches for Object Detection. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 49–67 (2022).
     
  • 38.
    Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: A Holistic View on Probabilistic Trajectory Forecasting -- Case Study: Cyclist Intention Detection. IEEE Intelligent Vehicles Symposium (IV). bll. 265–272. IEEE (2022).
     
  • 39.
    Herde, M., Huseljic, D., Mitrovic, J., Granitzer, M., Sick, B.: A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 1–6 (2022).
     

2021 [ nach oben ]

  • 1.
    Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
     
  • 2.
    Reginka, M., Hoang, H., Efendi, Özge, Merkel, M., Huhnstock, R., Holzinger, D., Dingel, K., Sick, B., Bertinetti, D., Herberg, F., Ehresmann, A.: Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration. Langmuir. 37, 8498–8507 (2021).
     
  • 3.
    Huhnstock, R., Reginka, M., Tomita, A., Merkel, M., Dingel, K., Holzinger, D., Sick, B., Vogel, M., Ehresmann, A.: Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes. Scientific Reports. 11, 21794 (2021).
     
  • 4.
    König, I., Bachmann, M., Bieshaar, M., Schindler, S., Lambrecht, F., David, K., Sick, B., Hornung, G., Sommer, C.: Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications. ITG-Symposium on Mobile Communication - Technologies and Applications. bll. 1–6. , Osnabrück, Germany (2021).
     
  • 5.
    Reichert, H., Lang, L., Rösch, K., Bogdoll, D., Doll, K., Sick, B., Reuss, H.-C., Stiller, C., Zöllner, J.M.: Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. IEEE International Smart Cities Conference (ISC2). bll. 1–4. IEEE (2021).
     
  • 6.
    Botache, D., Bethke, F., Hardieck, M., Bieshaar, M., Brabetz, L., Ayeb, M., Zipf, P., Sick, B.: Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands. IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). bll. 120–130. IEEE, Washington, DC, USA (2021).
     
  • 7.
    Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 361–374. IEEE, Milan, Italy (2021).
     
  • 8.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward optimal probabilistic active learning using a Bayesian approach. Machine Learning. 110, 1199–1231 (2021).
     
  • 9.
    He, Y., Huang, Z., Sick, B.: Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE (2021).
     
  • 10.
    Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses. arXiv e-prints. arXiv:2108.13979 (2021).
     
  • 11.
    Gruhl, C., Hannan, A., Huang, Z., Nivarthi, C., Vogt, S.: The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 204–209. IEEE (2021).
     
  • 12.
    Schreiber, J., Vogt, S., Sick, B.: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track. bll. 118–134. Springer (2021).
     
  • 13.
    Pham, T., Kottke, D., Krempl, G., Sick, B.: Stream-Based Active Learning for Sliding Windows Under Verification Latency. Machine Learning. (2021).
     
  • 14.
    Krempl, G., Kottke, D., Pham, T.: Statistical Analysis of Pairwise Connectivity. International Conference on Discovery Science (DS). bll. 138–148. Springer (2021).
     
  • 15.
    Hetzel, M., Reichert, H., Doll, K., Sick, B.: Smart Infrastructure: A Research Junction. IEEE International Smart Cities Conference (ISC2). IEEE (2021).
     
  • 16.
    Huseljic, D., Sick, B., Herde, M., Kottke, D.: Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. International Conference on Pattern Recognition (ICPR). bll. 9172–9179. IEEE (2021).
     
  • 17.
    Bellman, K., Botev, J., Diaconescu, A., Esterle, L., Gruhl, C., Landauer, C., Lewis, P.R., Nelson, P.R., Pournaras, E., Stein, A., Tomforde, S.: Self-improving system integration: Mastering continuous change. Future Generation Computer Systems. 117, 29–46 (2021).
     
  • 18.
    Kottke, D., Herde, M., Minh, T.P., Benz, A., Mergard, P., Roghman, A., Sandrock, C., Sick, B.: scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Preprints. 2021030194 (2021).
     
  • 19.
    Schneegans, J., Eilbrecht, J., Zernetsch, S., Bieshaar, M., Doll, K., Stursberg, O., Sick, B.: Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists. Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV (2021).
     
  • 20.
    Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 57–71. Springer (2021).
     
  • 21.
    Möller, F., Botache, D., Huseljic, D., Heidecker, F., Bieshaar, M., Sick, B.: Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders. Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR. bll. 1–10 (2021).
     
  • 22.
    Gruhl, C., Tomforde, S.: OHODIN -- Online Anomaly Detection for Data Streams. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 193–197. IEEE (2021).
     
  • 23.
    Scheiner, N., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Object Detection For Automotive Radar Point Clouds -- A Comparison. AI Perspectives. 3, 6 (2021).
     
  • 24.
    Gruhl, C., Sick, B., Tomforde, S.: Novelty detection in continuously changing environments. Future Generation Computer Systems. 114, 138–154 (2021).
     
  • 25.
    Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 407–421. IEEE, Milan, Italy (2021).
     
  • 26.
    Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning. International Conference on Pattern Recognition (ICPR). bll. 10281–10288. IEEE (2021).
     
  • 27.
    Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. International Conference on Pattern Recognition (ICPR). bll. 9483–9490. IEEE (2021).
     
  • 28.
    Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 375–389. IEEE, Milan, Italy (2021).
     
  • 29.
    Zernetsch, S., Schreck, S., Kress, V., Doll, K., Sick, B.: Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution. International Conference on Pattern Recognition (ICPR). bll. 2620–2626. IEEE (2021).
     
  • 30.
    Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. International Conference on Pattern Recognition (ICPR). bll. 2663–2670. IEEE (2021).
     
  • 31.
    Al-Falouji, G., Gruhl, C., Tomforde, S.: Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 166–171. IEEE (2021).
     
  • 32.
    Bogdoll, D., Breitenstein, J., Heidecker, F., Bieshaar, M., Sick, B., Fingscheidt, T., Zöllner, J.M.: Description of Corner Cases in Automated Driving: Goals and Challenges. Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll. 1023–1028,. IEEE (2021).
     
  • 33.
    Zernetsch, S., Trupp, O., Kress, V., Doll, K., Sick, B.: Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information. IEEE International Smart Cities Conference (ISC2). bll. 1–7. IEEE (2021).
     
  • 34.
    Bieshaar, M., Zernetsch, S., Riepe, K., Doll, K., Sick, B.: Cyclist Motion State Forecasting -- Going beyond Detection. IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Orlando, FL, USA (2021).
     
  • 35.
    He, Y., Sick, B.: CLeaR: An adaptive continual learning framework for regression tasks. AI Perspectives. 3, 2 (2020).
     
  • 36.
    Hannan, A., Gruhl, C., Sick, B.: Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders. IEEE International Conference on Cyber Security and Resilience (CSR). bll. 1–7. IEEE (2021).
     
  • 37.
    Heidecker, F., Breitenstein, J., Rösch, K., Löhdefink, J., Bieshaar, M., Stiller, C., Fingscheidt, T., Sick, B.: An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving. IEEE Intelligent Vehicles Symposium (IV). bll. 644–651. IEEE, Nagoya, Japan (2021).
     
  • 38.
    Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI - Based On The Fly Design of Experiments in Physics and Engineering. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 150–153. IEEE (2021).
     
  • 39.
    Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems. Computer Physics Communications. 262, 107859 (2021).
     
  • 40.
    Reuse, M., Simon, M., Sick, B.: About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving. Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll. 979–987. IEEE (2021).
     
  • 41.
    Thomas, J.M., Beddar-Wiesing, S., Moallemy-Oureh, A., Nather, R.: A Note on the Modeling Power of Different Graph Types. arXiv e-prints. arXiv:2109.10708 (2021).
     
  • 42.
    Bieshaar, M., Herde, M., Huselijc, D., Sick, B.: A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
     

2020 [ nach oben ]

  • 1.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward Optimal Probabilistic Active Learning Using a Bayesian Approach. arXiv e-prints. arXiv:2006.01732 (2020).
     
  • 2.
    Scheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., Ritter, W., Dickmann, J., Dietmayer, K., Sick, B., Heide, F.: Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020).
     
  • 3.
    Scheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., Ritter, W., Dickmann, J., Dietmayer, K., Sick, B., Heide, F.: Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020).
     
  • 4.
    Henze, J., Schreiber, J., Sick, B.: Representation Learning in Power Time Series Forecasting. In: Pedrycz, W. en Chen, S.-M. (reds.) Deep Learning: Algorithms and Applications. bll. 67–101. Springer (2020).
     
  • 5.
    Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. Advances in Computational Methods for X-Ray Optics V. bll. 71–77. SPIE (2020).
     
  • 6.
    Bieshaar, M., Schreiber, J., Vogt, S., Gensler, A., Sick, B.: Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets. arXiv e-prints. arXiv:2010.05898 (2020).
     
  • 7.
    Henze, J., Siefert, M., Bremicker-Trübelhorn, S., Asanalieva, N., Sick, B.: Probabilistic upscaling and aggregation of wind power forecasts. Energy, Sustainability and Society. 10, 15 (2020).
     
  • 8.
    Kress, V., Schreck, S., Zernetsch, S., Doll, K., Sick, B.: Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 2723–2730. IEEE (2020).
     
  • 9.
    Scheiner, N., Schumann, O., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?. IEEE International Conference on Information Fusion (FUSION). bll. 1–8. IEEE (2020).
     
  • 10.
    Gruhl, C., Schmeißing, J., Tomforde, S., Sick, B.: Normal-Wishart clustering for novelty detection. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 64–69. IEEE (2020).
     
  • 11.
    Scharei, K., Heidecker, F., Bieshaar, M.: Knowledge Representations in Technical Systems -- A Taxonomy. arXiv e-prints. arXiv:2001.04835 (2020).
     
  • 12.
    Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. arXiv e-prints. arXiv:2002.02705 (2020).
     
  • 13.
    Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. International Joint Conference on Neural Networks (IJCNN). IEEE (2020).
     
  • 14.
    He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. International Joint Conference on Neural Networks (IJCNN). IEEE (2020).
     
  • 15.
    Tomforde, S., Gruhl, C.: Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 54–59. IEEE (2020).
     
  • 16.
    Deist, S., Schreiber, J., Bieshaar, M., Sick, B.: Extended Coopetitive Soft Gating Ensemble. arXiv e-prints. arXiv:2004.14026 (2020).
     
  • 17.
    Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. arXiv e-prints. arXiv:2009.13853 (2020).
     
  • 18.
    He, Y., Henze, J., Sick, B.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. International Federation of Automatic Control (IFAC) World Congress. bll. 12175–12182. Elsevier (2020).
     
  • 19.
    He, Y., Henze, J., Sick, B.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. International Federation of Automatic Control (IFAC) World Congress. bll. 12175–12182. Elsevier (2020).
     
  • 20.
    Tomforde, S., Gruhl, C., Sick, B.: A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Workshop on Self -Aware Computing (SeAC), ACSOS. bll. 166–169. IEEE (2020).
     

2019 [ nach oben ]

  • 1.
    Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning. Wind Integration Workshop. , Dublin, Ireland (2019).
     
  • 2.
    König, I., Heilmann, E., Henze, J., David, K., Wetzel, H., Sick, B.: Using grid supporting flexibility in electricity distribution networks. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll. 531–544. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 3.
    Knauer, U., Styp von Rekowski, C., Stecklina, M., Krokotsch, T., Pham Minh, T., Hauffe, V., Kilias, D., Ehrhardt, I., Sagischewski, H., Chmara, S., Seiffert, U.: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote Sensing. 11, 2788 (2019).
     
  • 4.
    Stein, A., Tomforde, S.: Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 32–35. IEEE (2019).
     
  • 5.
    Schreiber, J.: 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. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll. 75–87. kassel university press, Kassel, Germany (2019).
     
  • 6.
    Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks. IEEE Intelligent Vehicles Symposium (IV). bll. 810–815. IEEE (2019).
     
  • 7.
    Heidecker, F., Bieshaar, M., Sick, B.: Towards Corner Case Identification in Cyclists’ Trajectories. ACM Computer Science in Cars Symposium (CSCS). ACM (2019).
     
  • 8.
    Krupitzer, C., Tomforde, S.: The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll. 545–554. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 9.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Start Intention Detection of Cyclists using an LSTM Network. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll. 219–228. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 10.
    Schneegans, J., Bieshaar, M.: Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll. 45–60. kassel university press, Kassel, Germany (2019).
     
  • 11.
    Bellman, K.L., Gruhl, C., Landauer, C., Tomforde, S.: Self-Improving System Integration -- On a Definition and Characteristics of the Challenge. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 1–3. IEEE (2019).
     
  • 12.
    Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles. IEEE Intelligent Vehicles Symposium (IV). bll. 642–649. IEEE, Paris, France (2019).
     
  • 13.
    Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users. IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Xiamen (2019).
     
  • 14.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Pose Based Start Intention Detection of Cyclists. IEEE International Conference on Intelligent Transportation Systems (ITSC). bll. 2381–2386. IEEE (2019).
     
  • 15.
    Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2018. kassel university press (2019).
     
  • 16.
    Rudolph, S., Tomforde, S., Hähner, J.: On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes. arXiv e-prints. arXiv:1905.04205 (2019).
     
  • 17.
    Rudolph, S., Tomforde, S., Hähner, J.: On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes. arXiv e-prints. arXiv:1905.04205 (2019).
     
  • 18.
    D’Angelo, M., Gerasimou, S., Ghahremani, S., Grohmann, J., Nunes, I., Pournaras, E., Tomforde, S.: On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). bll. 13–24. IEEE/ACM (2019).
     
  • 19.
    Rudolph, S., Tomforde, S., Hähner, J.: Mutual Influence-aware Runtime Learning of Self-adaptation Behavior. ACM Transactions on Autonomous and Adaptive Systems. 14, 4 (2019).
     
  • 20.
    Lesch, V., Krupitzer, C., Tomforde, S.: Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE (2019).
     
  • 21.
    Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. arXiv e-prints. arXiv:1901.10338 (2019).
     
  • 22.
    Goldhammer, M., Köhler, S., Zernetsch, S., Doll, K., Sick, B., Dietmayer, K.: Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning. IEEE Transactions on Intelligent Transportation Systems. 21, 3035–3045 (2019).
     
  • 23.
    Draude, C., Lange, M., Sick, B. reds: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). Gesellschaft für Informatik e.V. (2019).
     
  • 24.
    Schreiber, J., Buschin, A., Sick, B.: Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll. 585–598. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 25.
    Schreiber, J., Jessulat, M., Sick, B.: Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing. bll. 550–564. Springer, Cham (2019).
     
  • 26.
    Tomforde, S.: From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour. IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W. bll. 126–129. IEEE (2019).
     
  • 27.
    Tomforde, S., Gelhausen, P., Gruhl, C., Haering, I., Sick, B.: Explicit Consideration of Resilience in Organic Computing Design Processes. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–6. VDE (2019).
     
  • 28.
    Lesch, V., Krupitzer, C., Tomforde, S.: Emerging Self-Integration through Coordination of Autonomous Adaptive Systems. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 6–9. IEEE (2019).
     
  • 29.
    Botache, D., Dandan, L., Bieshaar, M., Sick, B.: Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll. 229–238. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 30.
    Calma, A., Dellermann, D.: Decision Support with Hybrid Intelligence. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll. 143–153. kassel university press, Kassel, Germany (2019).
     
  • 31.
    Sandrock, C., Herde, M., Calma, A., Kottke, D., Sick, B.: Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE (2019).
     
  • 32.
    Hanika, T., Herde, M., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields. arXiv e-prints. arXiv:1905.07264 (2019).
     
  • 33.
    Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS. IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). bll. 5–9. IEEE (2019).
     
  • 34.
    Scheiner, N., Haag, S., Appenrodt, N., Duraisamy, B., Dickmann, J., Fritzsche, M., Sick, B.: Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices. International Radar Symposium (IRS). bll. 1–10. , Ulm, Germany (2019).
     
  • 35.
    Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: A Multi-Stage Clustering Framework for Automotive Radar Data. IEEE International Conference on Intelligent Transportation Systems (ITSC). bll. 2060–2067. IEEE (2019).
     

2018 [ nach oben ]

  • 1.
    Krempl, G., Lemaire, V., Kottke, D., Calma, A., Holzinger, A., Polikar, R., Sick, B. reds: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. (2018).
     
  • 2.
    Tomforde, S., Dehling, T., Haux, R., Huseljic, D., Kottke, D., Scheerbaum, J., Sick, B., Sunyaev, A., Wolf, K.-H.: Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE (2018).
     
  • 3.
    Jahn, A., Tomforde, S., Morold, M., David, K., Sick, B.: Towards Cooperative Self-adapting Activity Recognition. International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS). bll. 215–222 (2018).
     
  • 4.
    Kottke, D., Calma, A., Huseljic, D., Sandrock, C., Kachergis, G., Sick, B.: The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning. International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     
  • 5.
    Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices. IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, Turin, Italy (2018).
     
  • 6.
    Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P., Schmeißing, J.: Smart Device Stealing and CANDIES. International Conference on Agents and Artificial Intelligence (ICAART). bll. 247–273. Springer (2018).
     
  • 7.
    Calma, A., Reitmaier, T., Sick, B.: Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data. Information Sciences. 456, 13–33 (2018).
     
  • 8.
    Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models. Informatics. 5, 38 (2018).
     
  • 9.
    Heck, H., Sick, B., Tomforde, S.: Security Issues in Self-Improving System Integration - Challenges and Solution Strategies. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 176–181. IEEE (2018).
     
  • 10.
    Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations. Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll. 27–48. Springer (2018).
     
  • 11.
    Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Feature Design and Multiclass Classification for Road User Recognition. IEEE Intelligent Vehicles Symposium (IV). bll. 779–786. IEEE, Changshu, China (2018).
     
  • 12.
    Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts. International Conference on Power and Renewable Energy (ICPRE). bll. 1–6 (2018).
     
  • 13.
    Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press (2018).
     
  • 14.
    Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. International Journal of Machine Learning and Cybernetics. 9, 927–945 (2018).
     
  • 15.
    Calma, A., Oeste-Reiß, S., Sick, B., Leimeister, J.M.: Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration. Hawaii International Conference on System Sciences (HICSS) (2018).
     
  • 16.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Human Pose Estimation in Real Traffic Scenes. IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Bangalore, India (2018).
     
  • 17.
    Breker, S., Rentmeister, J., Sick, B., Braun, M.: Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques. Applied Soft Computing. 70, 195–207 (2018).
     
  • 18.
    Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P.: Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection. International Conference on Agents and Artificial Intelligence (ICAART) (2018).
     
  • 19.
    Kromat, T., Dehling, T., Haux, R., Peters, C., Sick, B., Tomforde, S., Wolf, K.-H., Sunyaev, A.: Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung. Multikonferenz Wirtschaftsinformatik. , Lüneburg, Germany (2018).
     
  • 20.
    Schlegel, B., Mrowca, A., Wolf, P., Sick, B., Steinhorst, S.: Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms. IEEE International Conference on Big Data Analysis (ICBDA). IEEE, Shanghai, China (2018).
     
  • 21.
    Zernetsch, S., Kress, V., Sick, B., Doll, K.: Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network. IEEE Intelligent Vehicles Symposium (IV). bll. 1–6. IEEE (2018).
     
  • 22.
    Schreiber, J., Bieshaar, M., Gensler, A., Sick, B., Deist, S.: Coopetitive Soft Gating Ensemble. Workshop on Self-Improving System Integration (SISSY), FAS*W. IEEE, Trento, Italy (2018).
     
  • 23.
    Reitberger, G., Zernetsch, S., Bieshaar, M., Sick, B., Doll, K., Fuchs, E.: Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure. IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Maui, HI (2018).
     
  • 24.
    Bieshaar, M., Zernetsch, S., Hubert, A., Sick, B., Doll, K.: Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble. IEEE Transactions on Intelligent Vehicles. 3, 534–544 (2018).
     
  • 25.
    Tomforde, S., Kantert, J., Müller-Schloer, C., Bödelt, S., Sick, B.: Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness. Transactions on Computational Collective Intelligence. 28, 193–220 (2018).
     
  • 26.
    Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning. Informatik Spektrum. 41, 52–55 (2018).
     
  • 27.
    Scharei, K., Herde, M., Bieshaar, M., Calma, A., Kottke, D., Sick, B.: Automated Active Learning with a Robot. Archives of Data Science, Series A (Online First). 5, 16 (2018).
     
  • 28.
    Gruhl, C., Tomforde, S., Sick, B.: Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 198–203. IEEE (2018).
     
  • 29.
    Herde, M., Kottke, D., Calma, A., Bieshaar, M., Deist, S., Sick, B.: Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques. International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     
  • 30.
    Calma, A., Stolz, M., Kottke, D., Tomforde, S., Sick, B.: Active Learning with Realistic Data -- A Case Study. International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     
  • 31.
    Gensler, A., Sick, B., Vogt, S.: A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies. Renewable and Sustainable Energy Reviews. 96, 352–379 (2018).
     
  • 32.
    Gensler, A., Sick, B.: A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation. arXiv e-prints. arXiv:1803.06344 (2018).
     
  • 33.
    Calma, A., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiss, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, A.K.: A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques. Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll. 150–159. VDE, London, UK (2018).
     

2017 [ nach oben ]

  • 1.
    Bieshaar, M.: Where is my Device? Detecting the Smart Device’s Wearing Position in the Context of Active Safety for Vulnerable Road Users. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 27–37. kassel university press, Kassel, Germany (2017).
     
  • 2.
    Bellman, K., Botev, J., Hildmann, H., Lewis, P.R., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design: Exploring Social Potential. IEEE Technology and Society Magazine. 36, 72–80 (2017).
     
  • 3.
    Calma, A., Sick, B.: Simulation of Annotators for Active Learning: Uncertain Oracles. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 49–58 (2017).
     
  • 4.
    Stein, A., Rudolph, S., Tomforde, S., Hähner, J.: Self-Learning Smart Cameras -- Harnessing the Generalisation Capability of XCS. International Joint Conference on Computational Intelligence (IJCCI). , Funchal, Portugal (2017).
     
  • 5.
    Bannach, D., Jänicke, M., Rey, V.F., Tomforde, S., Sick, B., Lukowicz, P.: Self-Adaptation of Activity Recognition Systems to New Sensors. arXiv e-prints. arXiv:1701.08528 (2017).
     
  • 6.
    Kantert, J., Tomforde, S., Müller-Schloer, C., Edenhofer, S., Sick, B.: Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems. International Conference on Agents and Artificial Intelligence (ICAART). bll. 39–50. , Porto, Portugal (2017).
     
  • 7.
    Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 1–10. IEEE (2017).
     
  • 8.
    Gensler, A., Sick, B.: Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria. Pattern Analysis and Applications. 21, 543–562 (2017).
     
  • 9.
    Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight. arXiv e-prints. arXiv:1701.08125 (2017).
     
  • 10.
    Müller-Schloer, C., Tomforde, S.: Organic Computing -- Techncial Systems for Survival in the Real World. Birkhäuser Verlag (2017).
     
  • 11.
    Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2016. kassel university press (2017).
     
  • 12.
    Wolf, J.-H., Dehling, T., Haux, R., Sick, B., Sunyaev, A., Tomforde, S.: On Methodological and Technological Challenges for Proactive Health Management in Smart Homes. International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH). bll. 209–212. , Athens, Greece (2017).
     
  • 13.
    Tomforde, S., Kantert, J., Sick, B.: Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures. International Conference on Agents and Artificial Intelligence (ICAART). bll. 96–106. , Porto, Portugal (2017).
     
  • 14.
    Beyer, C., Bieshaar, M., Calma, A., Heck, H., Kottke, D., Würtz, R.: Learning Without Ground Truth. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Bochum, Germany (2017).
     
  • 15.
    Calma, A., Kottke, D., Sick, B., Tomforde, S.: Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 109–116. IEEE, Tucson, AZ (2017).
     
  • 16.
    Stein, A., Rauh, D., Tomforde, S., Hähner, J.: Interpolation in the eXtended Classifier System: An Architectural Perspective. Journal of Systems Architecture. 75, 79–94 (2017).
     
  • 17.
    Würtz, R.P., Tomforde, S., Calma, A., Kottke, D., Sick, B.: Interactive Learning Without Ground Truth. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 1–4. kassel university press, Kassel, Germany (2017).
     
  • 18.
    Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations. Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll. 83–93. Springer, Cham, Switzerland (2017).
     
  • 19.
    Kantert, J., Tomforde, S., Scharrer, R., Weber, S., Müller-Schloer, C., Edenhofer, S.: Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems. Journal of Systems Architecture. 75, 68–78 (2017).
     
  • 20.
    Gruhl, C.: Highly Autonomous Learning in Collaborative, Technical Systems. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Kassel, Germany (2017).
     
  • 21.
    Bieshaar, M., Reitberger, G., Kress, V., Zernetsch, S., Doll, K., Fuchs, E., Sick, B.: Highly Automated Learning for Improved Active Safety of Vulnerable Road Users. ACM Computer Science in Cars Symposium (CSCS). ACM, Munich, Germany (2017).
     
  • 22.
    Kottke, D.: Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 67–78. kassel university press, Kassel, Germany (2017).
     
  • 23.
    Bieshaar, M., Reitberger, G., Zernetsch, S., Sick, B., Fuchs, E., Doll, K.: Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence. Automatisiertes und vernetztes Fahren Symposium (AAET). bll. 67–87. , Braunschweig, Germany (2017).
     
  • 24.
    Schlegel, B., Sick, B.: Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity. Workshop on Data Science and Big Data Analytics (DSBDA), ICDM. bll. 69–78. IEEE (2017).
     
  • 25.
    Bieshaar, M., Zernetsch, S., Depping, M., Sick, B., Doll, K.: Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure. IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Yokohama, Japan (2017).
     
  • 26.
    Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 2–14 (2017).
     
  • 27.
    Calma, A.: Case Study on Pool-based Active Learning with Human Oracles. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 39–49. kassel university press, Kassel, Germany (2017).
     
  • 28.
    Gruhl, C., Beer, F., Heck, H., Sick, B., Bühler, U., Wacker, A., Tomforde, S.: A Concept for Intelligent Collaborative Network Intrusion Detection. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. VDE (2017).
     

2016 [ nach oben ]

  • 1.
    Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., Sick, B.: Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network. IEEE Intelligent Vehicles Symposium (IV). bll. 833–838. IEEE, Gothenburg, Sweden (2016).
     
  • 2.
    Jänicke, M., Tomforde, S., Sick, B.: Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models. Workshop on Self-Improving System Integration (SISSY), ICAC. bll. 285–291. IEEE, Würzburg, Germany (2016).
     
  • 3.
    Heck, H., Wacker, A., Rudolph, S., Gruhl, C., Sick, B., Tomforde, S.: Towards Autonomous Self-tests at Runtime. IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W. bll. 98–99. IEEE (2016).
     
  • 4.
    Fisch, D., Gruhl, C., Kalkowski, E., Sick, B., Ovaska, S.J.: Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers. Information Sciences. 370--371, 476–496 (2016).
     
  • 5.
    Lewis, P., Bellman, K., Botev, J., Hildmann, H., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design. , Dagstuhl, Germany (2016).
     
  • 6.
    Reitmaier, T., Calma, A., Sick, B.: Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data. arXiv e-prints. arXiv:1610.03995 (2016).
     
  • 7.
    Kantert, J., Scharrer, R., Tomforde, S., Edenhofer, S., Müller-Schloer, C.: Runtime Clustering of Similarity Behaving Agents in Open Organic Computing Systems. International Conference on Architecture of Computing Systems (ARCS). bll. 321–333. Springer, Nuremberg, Germany (2016).
     
  • 8.
    Calma, A., Reitmaier, T., Sick, B.: Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data. International Joint Conference on Neural Networks (IJCNN). bll. 4040–4047. IEEE, Vancouver, BC (2016).
     
  • 9.
    Gruhl, C.: Probabilistic Obsoleteness Detection for Gaussian Mixture Models. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2016. bll. 45–56. kassel university press, Kassel, Germany (2016).
     
  • 10.
    Kottke, D., Krempl, G., Stecklina, M., Styp von Rekowski, C., Sabsch, T., Pham Minh, T., Deliano, M., Spiliopoulou, M., Sick, B.: Probabilistic Active Learning for Active Class Selection. Workshop on the Future of Interactive Learning Machines, NIPS. bll. 1–9. , Barcelona, Spain (2016).
     
  • 11.
    Calma, A.: Pals: Interactive Pool-based Active Learning System with Uncertain Oracles. In: Sick, B. en Tomforde, S. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2016. bll. 35–44. kassel university press, Kassel, Germany (2016).
     
  • 12.
    Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Nuremberg, Germany (2016).
     
  • 13.
    Rudolph, S., Kantert, J., Jänen, U., Tomforde, S., Hähner, J., Müller-Schloer, C.: Measuring Self-Organisation Processes in Smart Camera Networks. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–6. VDE, Nuremberg, Germany (2016).
     
  • 14.
    Bahle, G., Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiss, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning. Workshop on Self-Improving System Integration (SISSY), ICAC. bll. 1–10. IEEE, Würzburg, Germany (2016).
     
  • 15.
    Lang, D., Kottke, D., Krempl, G., Spiliopoulou, M.: Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow. bll. 25–34. , Graz, Austria (2016).
     
  • 16.
    Stein, A., Eymüller, C., Rauh, D., Tomforde, S., Hähner, J.: Interpolation-based Classifier Generation in XCSF. IEEE Congress on Evolutionary Computation (CEC). bll. 3990–3998. IEEE, Vancouver, BC (2016).
     
  • 17.
    Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., Tomforde, S.: Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies. IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll. 100–109. IEEE, Augsburg, Germany (2016).
     
  • 18.
    Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities. International Work-Conference on Time Series (ITISE): Selected Contributions. bll. 75–88. Springer, Cham, Switzerland (2016).
     
  • 19.
    Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: From Active Learning to Dedicated Collaborative Interactive Learning. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Nuremberg, Germany (2016).
     
  • 20.
    Gensler, A., Sick, B.: Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation. International Joint Conference on Neural Networks (IJCNN). bll. 4976–4984. IEEE, Vancouver, BC (2016).
     
  • 21.
    Calma, A.: Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop. In: Sick, B. en Tomforde, S. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2016. kassel university press, Kassel, Germany (2016).
     
  • 22.
    Tomforde, S., Meier, D., Stein, A., von Mammen, S.: Distributed Resource Allocation as Co-Evolution Problem. IEEE Congress on Evolutionary Computation (CEC). bll. 1815–1822. IEEE, Vancouver, BC, Canada (2016).
     
  • 23.
    Schlegel, B., Sick, B.: Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 1–9. IEEE, Athens, Greece (2016).
     
  • 24.
    Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks. IEEE International Conference on Systems, Man and Cybernetics (SMC). bll. 2858–2865. IEEE, Budapest, Hungary (2016).
     
  • 25.
    Edenhofer, S., Tomforde, S., Fischer, D., Hähner, J., Menzel, F., von Mammen, S.: Decentralised Trust-Management Inspired by Ant Pheromones. International Journal of Mobile Network Design and Innovation. 7, 46–55 (2016).
     
  • 26.
    Stein, A., Tomforde, S., Rauh, D., Hähner, J.: Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap. IEEE International Conference on Autonomic Computing (ICAC). bll. 167–172. IEEE, Würzburg, Germany (2016).
     
  • 27.
    Kantert, J., Tomforde, S., Weber, S., Müller-Schloer, C.: Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems. International Conference on Informatics in Control, Automation and Robotics (ICINCO). bll. 221–226. , Lisbon, Portugal (2016).
     
  • 28.
    Kalkowski, E., Sick, B.: Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology. International Conference on Web Engineering (ICWE). bll. 207–224. Springer (2016).
     
  • 29.
    Kreil, M., Sick, B., Lukowicz, P.: Coping with variability in motion based activity recognition. International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR). bll. 1–8. , Rostock, Germany (2016).
     
  • 30.
    Kantert, J., Tomforde, S., Kauder, M., Scharrer, R., Edenhofer, S., Hähner, J., Müller-Schloer, C.: Controlling Negative Emergent Behavior by Graph Analysis at Runtime. ACM Transactions on Autonomous and Adaptive Systems. 11, 7 (2016).
     
  • 31.
    Rudolph, S., Hihn, R., Tomforde, S., Hähner, J.: Comparision of Dependency Measures for the Detection of Mutual Influences in Organic Computing Systems. International Conference on Architecture of Computing Systems (ARCS). bll. 334–347. Springer (2016).
     
  • 32.
    Kantert, J., Reinhard, F., von Zengen, G., Tomforde, S., Weber, S., Wolf, L., Müller-Schloer, C.: Combining Trust and ETX to Provide Robust Wireless Sensor Networks. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–7. VDE, Nuremberg, Germany (2016).
     
  • 33.
    Breker, S., Sick, B.: Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM. International Joint Conference on Neural Networks (IJCNN). bll. 2164–2173. IEEE, Vancouver, BC (2016).
     
  • 34.
    Hähner, J., Tomforde, S.: Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks. IEEE Symposium on Computers and Communications (ISCC). bll. 469–476. IEEE, Messina, Italy (2016).
     
  • 35.
    Pirkl, G., Hevesi, P., Lukowicz, P., Klein, P., Heisel, C., Gröber, S., Kuhn, J., Sick, B.: Any Problems? A wearable sensor-based platform for representational learning-analytics. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). bll. 353–356. ACM, Heidelberg, Germany (2016).
     
  • 36.
    Tomforde, S., Rudolph, S., Bellman, K., Würtz, R.P.: An Organic Computing Perspective on Self-Improving System Interwearing at Runtime. Workshop on Self-Improving System Integration (SISSY), ICAC. bll. 276–284. IEEE, Würzburg, Germany (2016).
     
  • 37.
    von Mammen, S., Tomforde, S., Hähner, J.: An Organic Computing Approach to Self-organising Robot Ensembles. Frontiers in Robotics and AI. 3, 67 (2016).
     
  • 38.
    Kantert, J., Reinbold, C., Tomforde, S., Müller-Schloer, C.: An Evaluation of Two Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering. IEEE International Conference on Autonomic Computing (ICAC). bll. 137–146. IEEE, Würzburg, Germany (2016).
     
  • 39.
    Gensler, A., Sick, B., Pankraz, V.: An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting. IEEE International Conference on Systems, Man and Cybernetics (SMC). bll. 2850–2857. IEEE (2016).
     
  • 40.
    Krempl, G., Lemaire, V., Lughofer, E., Kottke, D. reds: Active Learning: Applications, Foundations and Emerging Trends, iKNOW. , Graz, Austria (2016).
     
  • 41.
    Gensler, A., Sick, B., Vogt, S.: A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 1–9. IEEE, Athens, Greece (2016).
     
  • 42.
    Hähner, J., von Mammen, S., Timpf, S., Tomforde, S., Sick, B., Geihs, K., Goeble, T., Hornung, G., Stumme, G.: "Know thyselves" -- Computational Self-Reflection in Collective Technical Systems. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Nuremberg, Germany (2016).
     

2015 [ nach oben ]

  • 1.
    Kalkowski, E., Sick, B.: Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data. IEEE International Conference on Systems, Man and Cybernetics (SMC). bll. 1693–1699. IEEE, Hongkong, China (2015).
     
  • 2.
    Reitmaier, T., Calma, A., Sick, B.: Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data. Information Sciences. 293, 275–298 (2015).
     
  • 3.
    Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions. SAI Intelligent Systems Conference (IntelliSys). bll. 259–279. Springer, Cham, Switzerland (2015).
     
  • 4.
    Reitmaier, T., Sick, B.: The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification. Information Sciences. 323, 179–198 (2015).
     
  • 5.
    Jänicke, M.: Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll. 109–120. kassel university press, Kassel, Germany (2015).
     
  • 6.
    Hähner, J., Brinkschulte, U., Lukowicz, P., Mostaghim, S., Sick, B., Tomforde, S.: Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems. International Conference on Architecture of Computing Systems (ARCS). bll. 1–8. VDE, Porto, Portugal (2015).
     
  • 7.
    Tomforde, S., Sick, B. reds: Organic Computing -- Doctoral Dissertation Colloquium 2015. kassel university press (2015).
     
  • 8.
    Heck, H., Edenhofer, S., Gruhl, C., Lund, A., Shuka, R., Hähner, J.: On the Application Possibilities of Organic Computing Principles in Socio-technical Systems. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll. 165–170. kassel university press, Kassel, Germany (2015).
     
  • 9.
    Breker, S.: Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen, (2015).
     
  • 10.
    Calma, A., Jänicke, M., Kantert, J., Kopal, N., Siefert, F., Tomforde, S.: Horizontal Integration of Organic Computing and Control Theory Concepts. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll. 157–164. kassel university press, Kassel, Germany (2015).
     
  • 11.
    Kalkowski, E., Sick, B.: Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. International Work-Conference on Time Series (ITISE). bll. 806–817. , Granada, Spain (2015).
     
  • 12.
    Gensler, A., Gruber, T., Sick, B.: Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions. International Symposium on Temporal Representation and Reasoning (TIME). bll. 29–37. IEEE, Kassel, Germany (2015).
     
  • 13.
    Breker, S., Sick, B.: Effiziente Bewertung des Anschlusspotentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence. Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll. 51–56. , Hamburg, Germany (2015).
     
  • 14.
    Stone, T.C., Haas, S., Breitenstein, S., Wiesner, K., Sick, B.: Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning. International Journal on Advances in Software. 8, 53–64 (2015).
     
  • 15.
    Breker, S., Claudi, A., Sick, B.: Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations. IEEE Transactions on Power Systems. 30, 689–700 (2015).
     
  • 16.
    Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks. SAI Intelligent Systems Conference (IntelliSys). bll. 390–399. Springer, London, UK (2015).
     
  • 17.
    Rudolph, J., Breker, S., Sick, B.: Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence. Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll. 57–63. , Hamburg, Germany (2015).
     
  • 18.
    Gruhl, C.: Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. bll. 95–107. kassel university press, Kassel, Germany (2015).
     
  • 19.
    Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Elektronik automotive. 2, 32–36 (2015).
     
  • 20.
    Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., Hähner, J.: An Online Influence Detection Algorithm for Organic Computing Systems. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Porto, Portugal (2015).
     
  • 21.
    Reitmaier, T.: Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen, (2015).
     
  • 22.
    Jahn, A., Lau, S.L., David, K., Sick, B.: A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets. International Workshop on Mobile and Context Aware Services (MOCS), VTC. bll. 1–5. , Boston, MA (2015).
     
  • 23.
    Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning. arXiv e-prints. arXiv:1504.00284 (2015).
     
  • 24.
    Rudolph, S., Tomforde, S., Sick, B., Hähner, J.: A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement. IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll. 144–149. IEEE, Cambridge, MA (2015).
     
  • 25.
    Embrechts, M., Sick, B.: A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning. International Conference on Neural Networks - Fuzzy Systems (NN-FS). bll. 21–24. , Vienna, Austria (2015).