PUBLIKATIONSSERVER

Wolfram Schenck

https://orcid.org/0000-0003-3300-2048

64 Publikationen

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[64]
2026 | Artikel | FH-PUB-ID: 6485 | OA
Migenda, N., Möller, R., & Schenck, W. (2026). H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. PLOS One, 21(1). https://doi.org/10.1371/journal.pone.0339171
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[63]
2025 | Artikel | FH-PUB-ID: 6133 | OA
Herzig, T. C., Marschner, C., Ostrau, C., Held, S., Rickermann, J., Schenck, W., … Amelung, R. (2025). Softwaregestützte Analyse geriatrischer Entlassbriefe. Zeitschrift für Gerontologie und Geriatrie. https://doi.org/10.1007/s00391-025-02478-6
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[62]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., & Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing, 9(10). https://doi.org/10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[61]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6080
Jalil, F., Leuering, J., Ahmed, Q. A., Schenck, W., & Jungeblut, T. (2025). NNXC: Neural Network Meets Approximate Computing. Presented at the KI und ihre Anwendungen – Aktuelle Forschungsarbeiten des wissenschaftlichen Nachwuchses, Bielefeld: Institute for Data Science Solutions.
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[60]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6081
Jalil, F., Leuering, J., Ahmed, Q. A., Schenck, W., & Jungeblut, T. (2025). AutoDSE: Towards HW/AI Co-design of Ultra-low Latency Hardware Accelerators for Industrial Applications. In Workshop on AI and its Applications. Bielefeld: Institute for Data Science Solutions. https://doi.org/10.60802/sidas.2025.2
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[59]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6077
Leuering, J., Jalil, F., Ahmed, Q. A., Schenck, W., & Jungeblut, T. (n.d.). Cognitive Edge Computing for Multi-Sensor Applications with Sparse Data and High Latency Requirements. Presented at the KI und ihre Anwendungen – Aktuelle Forschungsarbeiten des wissenschaftlichen Nachwuchses, Bielefeld: Institute for Data Science Solutions.
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[58]
2024 | Diskussionspapier | FH-PUB-ID: 5498
Hammer, B., Alaçam, Ö., Arlinghaus, C. S., Brinkmann, M., Dörksen, H., Hoeken, S., … Zarrieß, S. (2024). Sustainable Life-Cycle of Intelligent Socio-Technical Systems. Bielefeld University. https://doi.org/10.4119/UNIBI/2992602
HSBI-PUB | DOI
 
[57]
2024 | Artikel | FH-PUB-ID: 5568
Tharwat, A., & Schenck, W. (2024). Active Learning for Handling Missing Data. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 3273–3287. https://doi.org/10.1109/TNNLS.2024.3352279
HSBI-PUB | DOI
 
[56]
2024 | Artikel | FH-PUB-ID: 5566
Tharwat, A., & Schenck, W. (2024). Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering, 36(8), 4317–4330. https://doi.org/10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[55]
2024 | Artikel | FH-PUB-ID: 5495
Tharwat, A., & Schenck, W. (2024). Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering, 36(8), 4317–4330. https://doi.org/10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[54]
2024 | Konferenzbeitrag | FH-PUB-ID: 5494
Akay, J. M., & Schenck, W. (2024). Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition. In M. Wand, K. Malinovská, J. Schmidhuber, & I. V. Tetko (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII (pp. 427–444). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72353-7_31
HSBI-PUB | DOI
 
[53]
2024 | Artikel | FH-PUB-ID: 5497
Weller, J., Migenda, N., Enzberg, S. von, Kohlhase, M., Schenck, W., & Dumitrescu, R. (2024). Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP, 128, 424–429. https://doi.org/10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[52]
2024 | Artikel | FH-PUB-ID: 5500 | OA
Shah, Z. H., Müller, M., Hübner, W., Wang, T.-C., Telman, D., Huser, T., & Schenck, W. (2024). Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. GigaScience, 13. https://doi.org/10.1093/gigascience/giad109
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[51]
2024 | Artikel | FH-PUB-ID: 5499
Shah, Z. H., Müller, M., Hübner, W., Ortkrass, H., Hammer, B., Huser, T., & Schenck, W. (2024). Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1353873
HSBI-PUB | DOI
 
[50]
2024 | Artikel | FH-PUB-ID: 4050
Migenda, N., Möller, R., & Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146. https://doi.org/10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[49]
2024 | Artikel | FH-PUB-ID: 4698
Migenda, N., Möller, R., & Schenck, W. (2024). NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts, 20. https://doi.org/10.1016/j.simpa.2024.100635
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[48]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, M., Migenda, N., Weller, J., Schenck, W., & Kohlhase, M. (2024). Technical Readiness of Prescriptive Analytics Platforms: A Survey. In IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 509–519). Tampere, Finland: IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516367
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[47]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, J., Migenda, N., Liu, R., Wegel, A., von Enzberg, S., Kohlhase, M., … Dumitrescu, R. (2024). Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In O. Niggemann, J. Beyerer, M. Krantz, & C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023 (Vol. 18, pp. 89–100). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47062-2_9
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[46]
2024 | Artikel | FH-PUB-ID: 4913
Weller, J., Migenda, N., Naik, Y., Heuwinkel, T., Kühn, A., Kohlhase, M., … Dumitrescu, R. (2024). Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics, 12(17). https://doi.org/10.3390/math12172663
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[45]
2024 | Konferenzbeitrag | FH-PUB-ID: 4644
Klein, L., Ostrau, C., Thies, M., Schenck, W., & Rückert, U. (2024). Exploratory Analysis of Machine Learning Methods for the Prognosis of Falls in Elderly Care Based on Accelerometer Data. In D. Salvi, P. Van Gorp, & S. A. Shah (Eds.), Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings (pp. 423–437). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-59717-6_27
HSBI-PUB | DOI
 
[44]
2023 | Artikel | FH-PUB-ID: 2774 | OA
Tharwat, A., & Schenck, W. (2023). A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions. Mathematics, 11(4). https://doi.org/10.3390/math11040820
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[43]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, J., Migenda, N., Wegel, A., Kohlhase, M., Schenck, W., & Dumitrescu, R. (2023). Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1–7). Marrakesh, Morocco: IEEE. https://doi.org/10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[42]
2023 | Konferenzbeitrag | FH-PUB-ID: 4293
Schwan, C., & Schenck, W. (2023). Object View Prediction with Aleatoric Uncertainty for Robotic Grasping. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Gold Coast, Australia: IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191465
HSBI-PUB | DOI
 
[41]
2023 | Artikel | FH-PUB-ID: 3453 | OA
Grimmelsmann, N., Mechtenberg, M., Schenck, W., Meyer, H. G., & Schneider, A. (2023). sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. PLOS ONE, 18(8). https://doi.org/10.1371/journal.pone.0289549
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[40]
2022 | Artikel | FH-PUB-ID: 2775 | OA
Tharwat, A., & Schenck, W. (2022). A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data. Mathematics, 10(7). https://doi.org/10.3390/math10071068
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[39]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022). Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors, 22(7). https://doi.org/10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2022 | Konferenzbeitrag | FH-PUB-ID: 2945
Shah, Z. H., Muller, M., Hammer, B., Huser, T., & Schenck, W. (2022). Impact of different loss functions on denoising of microscopic images. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1–10). Padua, Italy: IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892936
HSBI-PUB | DOI
 
[37]
2022 | Artikel | FH-PUB-ID: 2944 | OA
Zai El Amri, W., Reinhart, F., & Schenck, W. (2022). Open set task augmentation facilitates generalization of deep neural networks trained on small data sets. Neural Computing and Applications, 34(8), 6067–6083. https://doi.org/10.1007/s00521-021-06753-6
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[36]
2022 | Konferenzbeitrag | FH-PUB-ID: 2776 | OA
Schwan, C., & Schenck, W. (2022). Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking. In J. Jasperneite & V. Lohweg (Eds.), Kommunikation und Bildverarbeitung in der Automation. Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020 (pp. 291–303). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-64283-2_21
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[35]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe, C., Migenda, N., Pelkmann, D., Hötte, D. A., & Schenck, W. (2022). Collaborative System for Question Answering in German Case Law Documents. In L. M. Camarinha-Matos, A. Ortiz, X. Boucher, & A. L. Osório (Eds.), Collaborative Networks in Digitalization and Society 5.0 (pp. 303–312). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-14844-6_24
HSBI-PUB | DOI
 
[34]
2021 | Artikel | FH-PUB-ID: 1202
Tharwat, A., & Schenck, W. (2021). A conceptual and practical comparison of PSO-style optimization algorithms. Expert Systems with Applications, 167. https://doi.org/10.1016/j.eswa.2020.114430
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[33]
2021 | Artikel | FH-PUB-ID: 2777
Tharwat, A., & Schenck, W. (2021). Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques. Swarm and Evolutionary Computation, 67. https://doi.org/10.1016/j.swevo.2021.100952
HSBI-PUB | DOI
 
[32]
2021 | Artikel | FH-PUB-ID: 1201 | OA
Shah, Z. H., Müller, M., Wang, T.-C., Scheidig, P. M., Schneider, A., Schüttpelz, M., … Schenck, W. (2021). Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research, 9(5). https://doi.org/10.1364/PRJ.416437
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[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe, C., Pelkmann, D., Migenda, N., Hotte, D. A., & Schenck, W. (2021). Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 29–32). Laguna Hills, CA, USA: IEEE. https://doi.org/10.1109/AIKE52691.2021.00011
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[30]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[29]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, L., Migenda, N., Voigt, T., Kohlhase, M., & Schenck, W. (2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 1–7). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460825
HSBI-PUB | DOI
 
[28]
2021 | Artikel | FH-PUB-ID: 1203
Migenda, N., Möller, R., & Schenck, W. (2021). Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE, 16(3). https://doi.org/10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[27]
2020 | Artikel | FH-PUB-ID: 1204
Tharwat, A., & Schenck, W. (2020). Balancing Exploration and Exploitation: A novel active learner for imbalanced data. Knowledge-Based Systems, 210. https://doi.org/10.1016/j.knosys.2020.106500
HSBI-PUB | DOI
 
[26]
2020 | Konferenzbeitrag | FH-PUB-ID: 1206
Pelkmann, D., Tharwat, A., & Schenck, W. (2020). How to Label? Combining Experts’ Knowledge for German Text Classification. In 2020 7th Swiss Conference on Data Science (SDS) (pp. 61–62). Luzern, Switzerland: IEEE. https://doi.org/10.1109/SDS49233.2020.00023
HSBI-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1207
Schwan, C., & Schenck, W. (2020). Visual Movement Prediction for Stable Grasp Point Detection. In L. Iliadis, P. P. Angelov, C. Jayne, & E. Pimenidis (Eds.), Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Proceedings of the EANN 2020 (pp. 70–81). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-48791-1_5
HSBI-PUB | DOI
 
[24]
2020 | Diskussionspapier | FH-PUB-ID: 2778 | OA
Shah, Z. H., Müller, M., Wang, T.-C., Scheidig, P. M., Schneider, A., Schüttpelz, M., … Schenck, W. (2020). Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.10.27.352633
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[23]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda, N., & Schenck, W. (2020). Adaptive Dimensionality Reduction for Local Principal Component Analysis. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1579–1586). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[22]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda, N., Möller, R., & Schenck, W. (2019). Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” In H. Yin, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, R. Menezes, & R. Allmendinger (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I (pp. 76–84). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33607-3_9
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[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209 | OA
Grünberg, K., & Schenck, W. (2018). A Case Study on Benchmarking IoT Cloud Services. In M. Luo & L.-J. Zhang (Eds.), Cloud Computing – CLOUD 2018 (pp. 398–406). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94295-7_28
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[20]
2017 | Artikel | FH-PUB-ID: 1214
Schenck, W., Horst, M., Tiedemann, T., Gaulik, S., & Möller, R. (2017). Comparing parallel hardware architectures for visually guided robot navigation. Concurrency and Computation: Practice and Experience, 29(4). https://doi.org/10.1002/cpe.3833
HSBI-PUB | DOI
 
[19]
2017 | Artikel | FH-PUB-ID: 1210 | OA
Kunkel, S., & Schenck, W. (2017). The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code. Frontiers in Neuroinformatics, 11. https://doi.org/10.3389/fninf.2017.00040
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[18]
2017 | Artikel | FH-PUB-ID: 1211
Schenck, W., El Sayed, S., Foszczynski, M., Homberg, W., & Pleiter, D. (2017). Evaluation and Performance Modeling of a Burst Buffer Solution. ACM SIGOPS Operating Systems Review, 50(2), 12–26. https://doi.org/10.1145/3041710.3041714
HSBI-PUB | DOI
 
[17]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212 | OA
Butz, M., Schenck, W., & van Ooyen, A. (Eds.). (2017). Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA. https://doi.org/10.3389/978-2-88945-065-7
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[16]
2016 | Buchbeitrag | FH-PUB-ID: 1215
Schenck, W., El Sayed, S., Foszczynski, M., Homberg, W., & Pleiter, D. (2016). Early Evaluation of the “Infinite Memory Engine” Burst Buffer Solution. In M. Taufer, B. Mohr, & J. M. Kunkel (Eds.), High Performance Computing (Vol. vol 9945, pp. 604–615). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-46079-6_41
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[15]
2016 | Artikel | FH-PUB-ID: 1213 | OA
Butz, M., Schenck, W., & van Ooyen, A. (2016). Editorial: Anatomy and Plasticity in Large-Scale Brain Models. Frontiers in Neuroanatomy, 10. https://doi.org/10.3389/fnana.2016.00108
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[14]
2015 | Buchbeitrag | FH-PUB-ID: 1216
Adinetz, A. V., Baumeister, P. F., Böttiger, H., Hater, T., Maurer, T., Pleiter, D., … Schifano, S. F. (2015). Performance Evaluation of Scientific Applications on POWER8. In S. A. Jarvis, S. A. Wright, & S. D. Hammond (Eds.), High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation (Vol. 8966, pp. 24–45). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-17248-4_2
HSBI-PUB | DOI
 
[13]
2013 | Artikel | FH-PUB-ID: 1217
Schenck, W. (2013). Robot studies on saccade-triggered visual prediction. New Ideas in Psychology, 31(3), 221–238. https://doi.org/10.1016/j.newideapsych.2012.12.003
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[12]
2013 | Artikel | FH-PUB-ID: 1218
Kaiser, A., Schenck, W., & Möller, R. (2013). Solving the correspondence problem in stereo vision by internal simulation. Adaptive Behavior, 21(4), 239–250. https://doi.org/10.1177/1059712313488425
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[11]
2012 | Artikel | FH-PUB-ID: 1221
Kaiser, A., Schenck, W., & Möller, R. (2012). COUPLED SINGULAR VALUE DECOMPOSITION OF A CROSS-COVARIANCE MATRIX. International Journal of Neural Systems, 20(04), 293–318. https://doi.org/10.1142/S0129065710002437
HSBI-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1219 | OA
Schenck, W., Hoffmann, H., & Möller, R. (2011). Grasping of extrafoveal targets: A robotic model. New Ideas in Psychology, 29(3), 235–259. https://doi.org/10.1016/j.newideapsych.2009.07.005
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[9]
2011 | Artikel | FH-PUB-ID: 1220 | OA
Schenck, W. (2011). Kinematic motor learning. Connection Science, 23(4), 239–283. https://doi.org/10.1080/09540091.2011.625077
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[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
Schenck, W. (2009). Space Perception through Visuokinesthetic Prediction. In G. Pezzulo, M. V. Butz, O. Sigaud, & G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems (Vol. 5499, pp. 247–266). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-02565-5_14
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[7]
2008 | Artikel | FH-PUB-ID: 1223 | OA
Möller, R., & Schenck, W. (2008). Bootstrapping Cognition from Behavior-A Computerized Thought Experiment. Cognitive Science, 32(3), 504–542. https://doi.org/10.1080/03640210802035241
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[6]
2007 | Artikel | FH-PUB-ID: 1225
Kollmeier, T., Röben, F., Schenck, W., & Möller, R. (2007). Spectral contrasts for landmark navigation. Journal of the Optical Society of America A, 24(1), 1–10. https://doi.org/10.1364/JOSAA.24.000001
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[5]
2007 | Buchbeitrag | FH-PUB-ID: 1229
Schenck, W., & Möller, R. (2007). Training and Application of a Visual Forward Model for a Robot Camera Head. In M. V. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems (Vol. 4520, pp. 153–169). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_9
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[4]
2007 | Artikel | FH-PUB-ID: 1226
Kiefer, M., Schuch, S., Schenck, W., & Fiedler, K. (2007). Mood States Modulate Activity in Semantic Brain Areas during Emotional Word Encoding. Cerebral Cortex, 17(7), 1516–1530. https://doi.org/10.1093/cercor/bhl062
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[3]
2007 | Artikel | FH-PUB-ID: 1224 | OA
Kiefer, M., Schuch, S., Schenck, W., & Fiedler, K. (2007). Emotion and memory: Event-related potential indices predictive for subsequent successful memory depend on the emotional mood state. Advances in Cognitive Psychology, 3(3), 363–373. https://doi.org/10.2478/v10053-008-0001-8
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[2]
2005 | Artikel | FH-PUB-ID: 1227
Hoffmann, H., Schenck, W., & Möller, R. (2005). Learning visuomotor transformations for gaze-control and grasping. Biological Cybernetics, 93(2), 119–130. https://doi.org/10.1007/s00422-005-0575-x
HSBI-PUB | DOI
 
[1]
2005 | Artikel | FH-PUB-ID: 1228
Fiedler, K., Schenck, W., Watling, M., & Menges, J. I. (2005). Priming Trait Inferences Through Pictures and Moving Pictures: The Impact of Open and Closed Mindsets. Journal of Personality and Social Psychology, 88(2), 229–244. https://doi.org/10.1037/0022-3514.88.2.229
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64 Publikationen

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[64]
2026 | Artikel | FH-PUB-ID: 6485 | OA
Migenda, N., Möller, R., & Schenck, W. (2026). H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. PLOS One, 21(1). https://doi.org/10.1371/journal.pone.0339171
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[63]
2025 | Artikel | FH-PUB-ID: 6133 | OA
Herzig, T. C., Marschner, C., Ostrau, C., Held, S., Rickermann, J., Schenck, W., … Amelung, R. (2025). Softwaregestützte Analyse geriatrischer Entlassbriefe. Zeitschrift für Gerontologie und Geriatrie. https://doi.org/10.1007/s00391-025-02478-6
HSBI-PUB | DOI | Download (ext.)
 
[62]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., & Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing, 9(10). https://doi.org/10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[61]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6080
Jalil, F., Leuering, J., Ahmed, Q. A., Schenck, W., & Jungeblut, T. (2025). NNXC: Neural Network Meets Approximate Computing. Presented at the KI und ihre Anwendungen – Aktuelle Forschungsarbeiten des wissenschaftlichen Nachwuchses, Bielefeld: Institute for Data Science Solutions.
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[60]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6081
Jalil, F., Leuering, J., Ahmed, Q. A., Schenck, W., & Jungeblut, T. (2025). AutoDSE: Towards HW/AI Co-design of Ultra-low Latency Hardware Accelerators for Industrial Applications. In Workshop on AI and its Applications. Bielefeld: Institute for Data Science Solutions. https://doi.org/10.60802/sidas.2025.2
HSBI-PUB | DOI
 
[59]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6077
Leuering, J., Jalil, F., Ahmed, Q. A., Schenck, W., & Jungeblut, T. (n.d.). Cognitive Edge Computing for Multi-Sensor Applications with Sparse Data and High Latency Requirements. Presented at the KI und ihre Anwendungen – Aktuelle Forschungsarbeiten des wissenschaftlichen Nachwuchses, Bielefeld: Institute for Data Science Solutions.
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[58]
2024 | Diskussionspapier | FH-PUB-ID: 5498
Hammer, B., Alaçam, Ö., Arlinghaus, C. S., Brinkmann, M., Dörksen, H., Hoeken, S., … Zarrieß, S. (2024). Sustainable Life-Cycle of Intelligent Socio-Technical Systems. Bielefeld University. https://doi.org/10.4119/UNIBI/2992602
HSBI-PUB | DOI
 
[57]
2024 | Artikel | FH-PUB-ID: 5568
Tharwat, A., & Schenck, W. (2024). Active Learning for Handling Missing Data. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 3273–3287. https://doi.org/10.1109/TNNLS.2024.3352279
HSBI-PUB | DOI
 
[56]
2024 | Artikel | FH-PUB-ID: 5566
Tharwat, A., & Schenck, W. (2024). Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering, 36(8), 4317–4330. https://doi.org/10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[55]
2024 | Artikel | FH-PUB-ID: 5495
Tharwat, A., & Schenck, W. (2024). Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget. IEEE Transactions on Knowledge and Data Engineering, 36(8), 4317–4330. https://doi.org/10.1109/TKDE.2024.3365189
HSBI-PUB | DOI
 
[54]
2024 | Konferenzbeitrag | FH-PUB-ID: 5494
Akay, J. M., & Schenck, W. (2024). Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition. In M. Wand, K. Malinovská, J. Schmidhuber, & I. V. Tetko (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII (pp. 427–444). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72353-7_31
HSBI-PUB | DOI
 
[53]
2024 | Artikel | FH-PUB-ID: 5497
Weller, J., Migenda, N., Enzberg, S. von, Kohlhase, M., Schenck, W., & Dumitrescu, R. (2024). Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP, 128, 424–429. https://doi.org/10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[52]
2024 | Artikel | FH-PUB-ID: 5500 | OA
Shah, Z. H., Müller, M., Hübner, W., Wang, T.-C., Telman, D., Huser, T., & Schenck, W. (2024). Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. GigaScience, 13. https://doi.org/10.1093/gigascience/giad109
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[51]
2024 | Artikel | FH-PUB-ID: 5499
Shah, Z. H., Müller, M., Hübner, W., Ortkrass, H., Hammer, B., Huser, T., & Schenck, W. (2024). Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1353873
HSBI-PUB | DOI
 
[50]
2024 | Artikel | FH-PUB-ID: 4050
Migenda, N., Möller, R., & Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146. https://doi.org/10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[49]
2024 | Artikel | FH-PUB-ID: 4698
Migenda, N., Möller, R., & Schenck, W. (2024). NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts, 20. https://doi.org/10.1016/j.simpa.2024.100635
HSBI-PUB | DOI
 
[48]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, M., Migenda, N., Weller, J., Schenck, W., & Kohlhase, M. (2024). Technical Readiness of Prescriptive Analytics Platforms: A Survey. In IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 509–519). Tampere, Finland: IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[47]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, J., Migenda, N., Liu, R., Wegel, A., von Enzberg, S., Kohlhase, M., … Dumitrescu, R. (2024). Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In O. Niggemann, J. Beyerer, M. Krantz, & C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023 (Vol. 18, pp. 89–100). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[46]
2024 | Artikel | FH-PUB-ID: 4913
Weller, J., Migenda, N., Naik, Y., Heuwinkel, T., Kühn, A., Kohlhase, M., … Dumitrescu, R. (2024). Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics, 12(17). https://doi.org/10.3390/math12172663
HSBI-PUB | DOI
 
[45]
2024 | Konferenzbeitrag | FH-PUB-ID: 4644
Klein, L., Ostrau, C., Thies, M., Schenck, W., & Rückert, U. (2024). Exploratory Analysis of Machine Learning Methods for the Prognosis of Falls in Elderly Care Based on Accelerometer Data. In D. Salvi, P. Van Gorp, & S. A. Shah (Eds.), Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings (pp. 423–437). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-59717-6_27
HSBI-PUB | DOI
 
[44]
2023 | Artikel | FH-PUB-ID: 2774 | OA
Tharwat, A., & Schenck, W. (2023). A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions. Mathematics, 11(4). https://doi.org/10.3390/math11040820
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[43]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, J., Migenda, N., Wegel, A., Kohlhase, M., Schenck, W., & Dumitrescu, R. (2023). Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1–7). Marrakesh, Morocco: IEEE. https://doi.org/10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[42]
2023 | Konferenzbeitrag | FH-PUB-ID: 4293
Schwan, C., & Schenck, W. (2023). Object View Prediction with Aleatoric Uncertainty for Robotic Grasping. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Gold Coast, Australia: IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191465
HSBI-PUB | DOI
 
[41]
2023 | Artikel | FH-PUB-ID: 3453 | OA
Grimmelsmann, N., Mechtenberg, M., Schenck, W., Meyer, H. G., & Schneider, A. (2023). sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. PLOS ONE, 18(8). https://doi.org/10.1371/journal.pone.0289549
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[40]
2022 | Artikel | FH-PUB-ID: 2775 | OA
Tharwat, A., & Schenck, W. (2022). A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data. Mathematics, 10(7). https://doi.org/10.3390/math10071068
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[39]
2022 | Artikel | FH-PUB-ID: 1799 | OA
Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022). Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors, 22(7). https://doi.org/10.3390/s22072481
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2022 | Konferenzbeitrag | FH-PUB-ID: 2945
Shah, Z. H., Muller, M., Hammer, B., Huser, T., & Schenck, W. (2022). Impact of different loss functions on denoising of microscopic images. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1–10). Padua, Italy: IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892936
HSBI-PUB | DOI
 
[37]
2022 | Artikel | FH-PUB-ID: 2944 | OA
Zai El Amri, W., Reinhart, F., & Schenck, W. (2022). Open set task augmentation facilitates generalization of deep neural networks trained on small data sets. Neural Computing and Applications, 34(8), 6067–6083. https://doi.org/10.1007/s00521-021-06753-6
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[36]
2022 | Konferenzbeitrag | FH-PUB-ID: 2776 | OA
Schwan, C., & Schenck, W. (2022). Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking. In J. Jasperneite & V. Lohweg (Eds.), Kommunikation und Bildverarbeitung in der Automation. Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020 (pp. 291–303). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-64283-2_21
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[35]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe, C., Migenda, N., Pelkmann, D., Hötte, D. A., & Schenck, W. (2022). Collaborative System for Question Answering in German Case Law Documents. In L. M. Camarinha-Matos, A. Ortiz, X. Boucher, & A. L. Osório (Eds.), Collaborative Networks in Digitalization and Society 5.0 (pp. 303–312). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-14844-6_24
HSBI-PUB | DOI
 
[34]
2021 | Artikel | FH-PUB-ID: 1202
Tharwat, A., & Schenck, W. (2021). A conceptual and practical comparison of PSO-style optimization algorithms. Expert Systems with Applications, 167. https://doi.org/10.1016/j.eswa.2020.114430
HSBI-PUB | DOI
 
[33]
2021 | Artikel | FH-PUB-ID: 2777
Tharwat, A., & Schenck, W. (2021). Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques. Swarm and Evolutionary Computation, 67. https://doi.org/10.1016/j.swevo.2021.100952
HSBI-PUB | DOI
 
[32]
2021 | Artikel | FH-PUB-ID: 1201 | OA
Shah, Z. H., Müller, M., Wang, T.-C., Scheidig, P. M., Schneider, A., Schüttpelz, M., … Schenck, W. (2021). Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research, 9(5). https://doi.org/10.1364/PRJ.416437
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[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe, C., Pelkmann, D., Migenda, N., Hotte, D. A., & Schenck, W. (2021). Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 29–32). Laguna Hills, CA, USA: IEEE. https://doi.org/10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[30]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[29]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, L., Migenda, N., Voigt, T., Kohlhase, M., & Schenck, W. (2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 1–7). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460825
HSBI-PUB | DOI
 
[28]
2021 | Artikel | FH-PUB-ID: 1203
Migenda, N., Möller, R., & Schenck, W. (2021). Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE, 16(3). https://doi.org/10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[27]
2020 | Artikel | FH-PUB-ID: 1204
Tharwat, A., & Schenck, W. (2020). Balancing Exploration and Exploitation: A novel active learner for imbalanced data. Knowledge-Based Systems, 210. https://doi.org/10.1016/j.knosys.2020.106500
HSBI-PUB | DOI
 
[26]
2020 | Konferenzbeitrag | FH-PUB-ID: 1206
Pelkmann, D., Tharwat, A., & Schenck, W. (2020). How to Label? Combining Experts’ Knowledge for German Text Classification. In 2020 7th Swiss Conference on Data Science (SDS) (pp. 61–62). Luzern, Switzerland: IEEE. https://doi.org/10.1109/SDS49233.2020.00023
HSBI-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1207
Schwan, C., & Schenck, W. (2020). Visual Movement Prediction for Stable Grasp Point Detection. In L. Iliadis, P. P. Angelov, C. Jayne, & E. Pimenidis (Eds.), Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Proceedings of the EANN 2020 (pp. 70–81). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-48791-1_5
HSBI-PUB | DOI
 
[24]
2020 | Diskussionspapier | FH-PUB-ID: 2778 | OA
Shah, Z. H., Müller, M., Wang, T.-C., Scheidig, P. M., Schneider, A., Schüttpelz, M., … Schenck, W. (2020). Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.10.27.352633
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[23]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda, N., & Schenck, W. (2020). Adaptive Dimensionality Reduction for Local Principal Component Analysis. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1579–1586). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[22]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda, N., Möller, R., & Schenck, W. (2019). Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” In H. Yin, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, R. Menezes, & R. Allmendinger (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I (pp. 76–84). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209 | OA
Grünberg, K., & Schenck, W. (2018). A Case Study on Benchmarking IoT Cloud Services. In M. Luo & L.-J. Zhang (Eds.), Cloud Computing – CLOUD 2018 (pp. 398–406). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94295-7_28
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[20]
2017 | Artikel | FH-PUB-ID: 1214
Schenck, W., Horst, M., Tiedemann, T., Gaulik, S., & Möller, R. (2017). Comparing parallel hardware architectures for visually guided robot navigation. Concurrency and Computation: Practice and Experience, 29(4). https://doi.org/10.1002/cpe.3833
HSBI-PUB | DOI
 
[19]
2017 | Artikel | FH-PUB-ID: 1210 | OA
Kunkel, S., & Schenck, W. (2017). The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code. Frontiers in Neuroinformatics, 11. https://doi.org/10.3389/fninf.2017.00040
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[18]
2017 | Artikel | FH-PUB-ID: 1211
Schenck, W., El Sayed, S., Foszczynski, M., Homberg, W., & Pleiter, D. (2017). Evaluation and Performance Modeling of a Burst Buffer Solution. ACM SIGOPS Operating Systems Review, 50(2), 12–26. https://doi.org/10.1145/3041710.3041714
HSBI-PUB | DOI
 
[17]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212 | OA
Butz, M., Schenck, W., & van Ooyen, A. (Eds.). (2017). Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA. https://doi.org/10.3389/978-2-88945-065-7
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[16]
2016 | Buchbeitrag | FH-PUB-ID: 1215
Schenck, W., El Sayed, S., Foszczynski, M., Homberg, W., & Pleiter, D. (2016). Early Evaluation of the “Infinite Memory Engine” Burst Buffer Solution. In M. Taufer, B. Mohr, & J. M. Kunkel (Eds.), High Performance Computing (Vol. vol 9945, pp. 604–615). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-46079-6_41
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[15]
2016 | Artikel | FH-PUB-ID: 1213 | OA
Butz, M., Schenck, W., & van Ooyen, A. (2016). Editorial: Anatomy and Plasticity in Large-Scale Brain Models. Frontiers in Neuroanatomy, 10. https://doi.org/10.3389/fnana.2016.00108
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[14]
2015 | Buchbeitrag | FH-PUB-ID: 1216
Adinetz, A. V., Baumeister, P. F., Böttiger, H., Hater, T., Maurer, T., Pleiter, D., … Schifano, S. F. (2015). Performance Evaluation of Scientific Applications on POWER8. In S. A. Jarvis, S. A. Wright, & S. D. Hammond (Eds.), High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation (Vol. 8966, pp. 24–45). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-17248-4_2
HSBI-PUB | DOI
 
[13]
2013 | Artikel | FH-PUB-ID: 1217
Schenck, W. (2013). Robot studies on saccade-triggered visual prediction. New Ideas in Psychology, 31(3), 221–238. https://doi.org/10.1016/j.newideapsych.2012.12.003
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[12]
2013 | Artikel | FH-PUB-ID: 1218
Kaiser, A., Schenck, W., & Möller, R. (2013). Solving the correspondence problem in stereo vision by internal simulation. Adaptive Behavior, 21(4), 239–250. https://doi.org/10.1177/1059712313488425
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[11]
2012 | Artikel | FH-PUB-ID: 1221
Kaiser, A., Schenck, W., & Möller, R. (2012). COUPLED SINGULAR VALUE DECOMPOSITION OF A CROSS-COVARIANCE MATRIX. International Journal of Neural Systems, 20(04), 293–318. https://doi.org/10.1142/S0129065710002437
HSBI-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1219 | OA
Schenck, W., Hoffmann, H., & Möller, R. (2011). Grasping of extrafoveal targets: A robotic model. New Ideas in Psychology, 29(3), 235–259. https://doi.org/10.1016/j.newideapsych.2009.07.005
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[9]
2011 | Artikel | FH-PUB-ID: 1220 | OA
Schenck, W. (2011). Kinematic motor learning. Connection Science, 23(4), 239–283. https://doi.org/10.1080/09540091.2011.625077
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[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
Schenck, W. (2009). Space Perception through Visuokinesthetic Prediction. In G. Pezzulo, M. V. Butz, O. Sigaud, & G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems (Vol. 5499, pp. 247–266). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-02565-5_14
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[7]
2008 | Artikel | FH-PUB-ID: 1223 | OA
Möller, R., & Schenck, W. (2008). Bootstrapping Cognition from Behavior-A Computerized Thought Experiment. Cognitive Science, 32(3), 504–542. https://doi.org/10.1080/03640210802035241
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[6]
2007 | Artikel | FH-PUB-ID: 1225
Kollmeier, T., Röben, F., Schenck, W., & Möller, R. (2007). Spectral contrasts for landmark navigation. Journal of the Optical Society of America A, 24(1), 1–10. https://doi.org/10.1364/JOSAA.24.000001
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[5]
2007 | Buchbeitrag | FH-PUB-ID: 1229
Schenck, W., & Möller, R. (2007). Training and Application of a Visual Forward Model for a Robot Camera Head. In M. V. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems (Vol. 4520, pp. 153–169). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_9
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[4]
2007 | Artikel | FH-PUB-ID: 1226
Kiefer, M., Schuch, S., Schenck, W., & Fiedler, K. (2007). Mood States Modulate Activity in Semantic Brain Areas during Emotional Word Encoding. Cerebral Cortex, 17(7), 1516–1530. https://doi.org/10.1093/cercor/bhl062
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[3]
2007 | Artikel | FH-PUB-ID: 1224 | OA
Kiefer, M., Schuch, S., Schenck, W., & Fiedler, K. (2007). Emotion and memory: Event-related potential indices predictive for subsequent successful memory depend on the emotional mood state. Advances in Cognitive Psychology, 3(3), 363–373. https://doi.org/10.2478/v10053-008-0001-8
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[2]
2005 | Artikel | FH-PUB-ID: 1227
Hoffmann, H., Schenck, W., & Möller, R. (2005). Learning visuomotor transformations for gaze-control and grasping. Biological Cybernetics, 93(2), 119–130. https://doi.org/10.1007/s00422-005-0575-x
HSBI-PUB | DOI
 
[1]
2005 | Artikel | FH-PUB-ID: 1228
Fiedler, K., Schenck, W., Watling, M., & Menges, J. I. (2005). Priming Trait Inferences Through Pictures and Moving Pictures: The Impact of Open and Closed Mindsets. Journal of Personality and Social Psychology, 88(2), 229–244. https://doi.org/10.1037/0022-3514.88.2.229
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