PUBLIKATIONSSERVER

Wolfram Schenck

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

63 Publikationen

Alle markieren

[63]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6080
F. Jalil, J. Leuering, Q. A. Ahmed, W. Schenck, and T. Jungeblut, “NNXC: Neural Network Meets Approximate Computing,” presented at the KI und ihre Anwendungen – Aktuelle Forschungsarbeiten des wissenschaftlichen Nachwuchses, Bielefeld, 2025.
HSBI-PUB
 
[62]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6081
F. Jalil, J. Leuering, Q. A. Ahmed, W. Schenck, and T. Jungeblut, “AutoDSE: Towards HW/AI Co-design of Ultra-low Latency Hardware Accelerators for Industrial Applications,” in Workshop on AI and its Applications, Bielefeld, 2025.
HSBI-PUB | DOI
 
[61]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6077
J. Leuering, F. Jalil, Q. A. Ahmed, W. Schenck, and T. Jungeblut, “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.
HSBI-PUB
 
[60]
2025 | Artikel | FH-PUB-ID: 6244 | OA
M. Niederhaus, N. Migenda, J. Weller, M. Kohlhase, and W. Schenck, “Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems,” Big Data and Cognitive Computing, vol. 9, no. 10, 2025.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[59]
2025 | Artikel | FH-PUB-ID: 6133 | OA
T. C. Herzig et al., “Softwaregestützte Analyse geriatrischer Entlassbriefe,” Zeitschrift für Gerontologie und Geriatrie, 2025.
HSBI-PUB | DOI | Download (ext.)
 
[58]
2024 | Artikel | FH-PUB-ID: 5568
A. Tharwat and W. Schenck, “Active Learning for Handling Missing Data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 3273–3287, 2024.
HSBI-PUB | DOI
 
[57]
2024 | Artikel | FH-PUB-ID: 5566
A. Tharwat and W. Schenck, “Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 8, pp. 4317–4330, 2024.
HSBI-PUB | DOI
 
[56]
2024 | Artikel | FH-PUB-ID: 5495
A. Tharwat and W. Schenck, “Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 8, pp. 4317–4330, 2024.
HSBI-PUB | DOI
 
[55]
2024 | Konferenzbeitrag | FH-PUB-ID: 5494
J. M. Akay and W. Schenck, “Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition,” in Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII, Lugano, Switzerland, 2024, pp. 427–444.
HSBI-PUB | DOI
 
[54]
2024 | Artikel | FH-PUB-ID: 5497
J. Weller, N. Migenda, S. von Enzberg, M. Kohlhase, W. Schenck, and R. Dumitrescu, “Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories,” Procedia CIRP, vol. 128, pp. 424–429, 2024.
HSBI-PUB | DOI
 
[53]
2024 | Diskussionspapier | FH-PUB-ID: 5498
B. Hammer et al., Sustainable Life-Cycle of Intelligent Socio-Technical Systems. Bielefeld University, 2024.
HSBI-PUB | DOI
 
[52]
2024 | Artikel | FH-PUB-ID: 5500 | OA
Z. H. Shah et al., “Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data,” GigaScience, vol. 13, 2024.
HSBI-PUB | DOI | Download (ext.)
 
[51]
2024 | Artikel | FH-PUB-ID: 5499
Z. H. Shah et al., “Image restoration in frequency space using complex-valued CNNs,” Frontiers in Artificial Intelligence, vol. 7, 2024.
HSBI-PUB | DOI
 
[50]
2024 | Artikel | FH-PUB-ID: 4050
N. Migenda, R. Möller, and W. Schenck, “Adaptive local Principal Component Analysis improves the clustering of high-dimensional data,” Pattern Recognition, vol. 146, 2024.
HSBI-PUB | DOI
 
[49]
2024 | Artikel | FH-PUB-ID: 4698
N. Migenda, R. Möller, and W. Schenck, “NGPCA: Clustering of high-dimensional and non-stationary data streams,” Software Impacts, vol. 20, 2024.
HSBI-PUB | DOI
 
[48]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
M. Niederhaus, N. Migenda, J. Weller, W. Schenck, and M. Kohlhase, “Technical Readiness of Prescriptive Analytics Platforms: A Survey,” in 2024 35th Conference of Open Innovations Association (FRUCT), Tampere, Finland, 2024, pp. 509–519.
HSBI-PUB | DOI
 
[47]
2024 | Buchbeitrag | FH-PUB-ID: 4915
J. Weller et al., “Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories,” in Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023, vol. 18, O. Niggemann, J. Beyerer, M. Krantz, and C. Kühnert, Eds. Cham: Springer Nature Switzerland, 2024, pp. 89–100.
HSBI-PUB | DOI
 
[46]
2024 | Artikel | FH-PUB-ID: 4913
J. Weller et al., “Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories,” Mathematics, vol. 12, no. 17, 2024.
HSBI-PUB | DOI
 
[45]
2024 | Konferenzbeitrag | FH-PUB-ID: 4644
L. Klein, C. Ostrau, M. Thies, W. Schenck, and U. Rückert, “Exploratory Analysis of Machine Learning Methods for the Prognosis of Falls in Elderly Care Based on Accelerometer Data,” in Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings, Malmö, Schweden, 2024, pp. 423–437.
HSBI-PUB | DOI
 
[44]
2023 | Artikel | FH-PUB-ID: 2774 | OA
A. Tharwat and W. Schenck, “A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions,” Mathematics, vol. 11, no. 4, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[43]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
J. Weller, N. Migenda, A. Wegel, M. Kohlhase, W. Schenck, and R. Dumitrescu, “Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories,” in 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), Marrakesh, Morocco, 2023, pp. 1–7.
HSBI-PUB | DOI
 
[42]
2023 | Konferenzbeitrag | FH-PUB-ID: 4293
C. Schwan and W. Schenck, “Object View Prediction with Aleatoric Uncertainty for Robotic Grasping,” in 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 2023, pp. 1–8.
HSBI-PUB | DOI
 
[41]
2023 | Artikel | FH-PUB-ID: 3453 | OA
N. Grimmelsmann, M. Mechtenberg, W. Schenck, H. G. Meyer, and A. Schneider, “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, vol. 18, no. 8, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[40]
2022 | Artikel | FH-PUB-ID: 2775 | OA
A. Tharwat and W. Schenck, “A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data,” Mathematics, vol. 10, no. 7, 2022.
HSBI-PUB | DOI | Download (ext.)
 
[39]
2022 | Artikel | FH-PUB-ID: 1799 | OA
K. Vandevoorde, L. Vollenkemper, C. Schwan, M. Kohlhase, and W. Schenck, “Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks,” Sensors, vol. 22, no. 7, 2022.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2022 | Konferenzbeitrag | FH-PUB-ID: 2945
Z. H. Shah, M. Muller, B. Hammer, T. Huser, and W. Schenck, “Impact of different loss functions on denoising of microscopic images,” in 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1–10.
HSBI-PUB | DOI
 
[37]
2022 | Artikel | FH-PUB-ID: 2944 | OA
W. Zai El Amri, F. Reinhart, and W. Schenck, “Open set task augmentation facilitates generalization of deep neural networks trained on small data sets,” Neural Computing and Applications, vol. 34, no. 8, pp. 6067–6083, 2022.
HSBI-PUB | DOI | Download (ext.)
 
[36]
2022 | Konferenzbeitrag | FH-PUB-ID: 2776 | OA
C. Schwan and W. Schenck, “Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking,” in Kommunikation und Bildverarbeitung in der Automation. Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020, 2022, pp. 291–303.
HSBI-PUB | DOI | Download (ext.)
 
[35]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
C. Hoppe, N. Migenda, D. Pelkmann, D. A. Hötte, and W. Schenck, “Collaborative System for Question Answering in German Case Law Documents,” in Collaborative Networks in Digitalization and Society 5.0, Lisbon, Portugal, 2022, pp. 303–312.
HSBI-PUB | DOI
 
[34]
2021 | Artikel | FH-PUB-ID: 1202
A. Tharwat and W. Schenck, “A conceptual and practical comparison of PSO-style optimization algorithms,” Expert Systems with Applications, vol. 167, 2021.
HSBI-PUB | DOI
 
[33]
2021 | Artikel | FH-PUB-ID: 2777
A. Tharwat and W. Schenck, “Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques,” Swarm and Evolutionary Computation, vol. 67, 2021.
HSBI-PUB | DOI
 
[32]
2021 | Artikel | FH-PUB-ID: 1201 | OA
Z. H. Shah et al., “Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images,” Photonics Research, vol. 9, no. 5, 2021.
HSBI-PUB | DOI | Download (ext.)
 
[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
C. Hoppe, D. Pelkmann, N. Migenda, D. A. Hotte, and W. Schenck, “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), Laguna Hills, CA, USA, 2021, pp. 29–32.
HSBI-PUB | DOI
 
[30]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
T. Voigt et al., “Advanced Data Analytics Platform for Manufacturing Companies,” in 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), Vasteras, Sweden, 2021, pp. 01–08.
HSBI-PUB | DOI
 
[29]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
L. Steinmann, N. Migenda, T. Voigt, M. Kohlhase, and W. Schenck, “Variational Autoencoder based Novelty Detection for Real-World Time Series,” in 2021 3rd International Conference on Management Science and Industrial Engineering, Osaka Japan, 2021, pp. 1–7.
HSBI-PUB | DOI
 
[28]
2021 | Artikel | FH-PUB-ID: 1203
N. Migenda, R. Möller, and W. Schenck, “Adaptive dimensionality reduction for neural network-based online principal component analysis,” PLOS ONE, vol. 16, no. 3, 2021.
HSBI-PUB | DOI
 
[27]
2020 | Artikel | FH-PUB-ID: 1204
A. Tharwat and W. Schenck, “Balancing Exploration and Exploitation: A novel active learner for imbalanced data,” Knowledge-Based Systems, vol. 210, 2020.
HSBI-PUB | DOI
 
[26]
2020 | Konferenzbeitrag | FH-PUB-ID: 1206
D. Pelkmann, A. Tharwat, and W. Schenck, “How to Label? Combining Experts’ Knowledge for German Text Classification,” in 2020 7th Swiss Conference on Data Science (SDS), Luzern, Switzerland, 2020, pp. 61–62.
HSBI-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1207
C. Schwan and W. Schenck, “Visual Movement Prediction for Stable Grasp Point Detection,” in Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Proceedings of the EANN 2020, Halkidiki, Greece, 2020, pp. 70–81.
HSBI-PUB | DOI
 
[24]
2020 | Diskussionspapier | FH-PUB-ID: 2778 | OA
Z. H. Shah et al., Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Cold Spring Harbor Laboratory, 2020.
HSBI-PUB | DOI | Download (ext.)
 
[23]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
N. Migenda and W. Schenck, “Adaptive Dimensionality Reduction for Local Principal Component Analysis,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 2020, pp. 1579–1586.
HSBI-PUB | DOI
 
[22]
2019 | Buchbeitrag | FH-PUB-ID: 1208
N. Migenda, R. Möller, and W. Schenck, “Adaptive Dimensionality Adjustment for Online ‘Principal Component Analysis,’” in Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I, H. Yin, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, R. Menezes, and R. Allmendinger, Eds. Cham: Springer International Publishing, 2019, pp. 76–84.
HSBI-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209 | OA
K. Grünberg and W. Schenck, “A Case Study on Benchmarking IoT Cloud Services,” in Cloud Computing – CLOUD 2018, M. Luo and L.-J. Zhang, Eds. Cham: Springer International Publishing, 2018, pp. 398–406.
HSBI-PUB | DOI | Download (ext.)
 
[20]
2017 | Artikel | FH-PUB-ID: 1214
W. Schenck, M. Horst, T. Tiedemann, S. Gaulik, and R. Möller, “Comparing parallel hardware architectures for visually guided robot navigation,” Concurrency and Computation: Practice and Experience, vol. 29, no. 4, 2017.
HSBI-PUB | DOI
 
[19]
2017 | Artikel | FH-PUB-ID: 1210 | OA
S. Kunkel and W. Schenck, “The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code,” Frontiers in Neuroinformatics, vol. 11, 2017.
HSBI-PUB | DOI | Download (ext.)
 
[18]
2017 | Artikel | FH-PUB-ID: 1211
W. Schenck, S. El Sayed, M. Foszczynski, W. Homberg, and D. Pleiter, “Evaluation and Performance Modeling of a Burst Buffer Solution,” ACM SIGOPS Operating Systems Review, vol. 50, no. 2, pp. 12–26, 2017.
HSBI-PUB | DOI
 
[17]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212 | OA
M. Butz, W. Schenck, and A. van Ooyen, Eds., Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA, 2017.
HSBI-PUB | DOI | Download (ext.)
 
[16]
2016 | Buchbeitrag | FH-PUB-ID: 1215
W. Schenck, S. El Sayed, M. Foszczynski, W. Homberg, and D. Pleiter, “Early Evaluation of the ‘Infinite Memory Engine’ Burst Buffer Solution,” in High Performance Computing, vol. vol 9945, M. Taufer, B. Mohr, and J. M. Kunkel, Eds. Cham: Springer International Publishing, 2016, pp. 604–615.
HSBI-PUB | DOI | Download (ext.)
 
[15]
2016 | Artikel | FH-PUB-ID: 1213 | OA
M. Butz, W. Schenck, and A. van Ooyen, “Editorial: Anatomy and Plasticity in Large-Scale Brain Models,” Frontiers in Neuroanatomy, vol. 10, 2016.
HSBI-PUB | DOI | Download (ext.)
 
[14]
2015 | Buchbeitrag | FH-PUB-ID: 1216
A. V. Adinetz et al., “Performance Evaluation of Scientific Applications on POWER8,” in High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, vol. 8966, S. A. Jarvis, S. A. Wright, and S. D. Hammond, Eds. Cham: Springer International Publishing, 2015, pp. 24–45.
HSBI-PUB | DOI
 
[13]
2013 | Artikel | FH-PUB-ID: 1217
W. Schenck, “Robot studies on saccade-triggered visual prediction,” New Ideas in Psychology, vol. 31, no. 3, pp. 221–238, 2013.
HSBI-PUB | DOI | Download (ext.)
 
[12]
2013 | Artikel | FH-PUB-ID: 1218
A. Kaiser, W. Schenck, and R. Möller, “Solving the correspondence problem in stereo vision by internal simulation,” Adaptive Behavior, vol. 21, no. 4, pp. 239–250, 2013.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2012 | Artikel | FH-PUB-ID: 1221
A. Kaiser, W. Schenck, and R. Möller, “COUPLED SINGULAR VALUE DECOMPOSITION OF A CROSS-COVARIANCE MATRIX,” International Journal of Neural Systems, vol. 20, no. 04, pp. 293–318, 2012.
HSBI-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1219 | OA
W. Schenck, H. Hoffmann, and R. Möller, “Grasping of extrafoveal targets: A robotic model,” New Ideas in Psychology, vol. 29, no. 3, pp. 235–259, 2011.
HSBI-PUB | DOI | Download (ext.)
 
[9]
2011 | Artikel | FH-PUB-ID: 1220 | OA
W. Schenck, “Kinematic motor learning,” Connection Science, vol. 23, no. 4, pp. 239–283, 2011.
HSBI-PUB | DOI | Download (ext.)
 
[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
W. Schenck, “Space Perception through Visuokinesthetic Prediction,” in Anticipatory Behavior in Adaptive Learning Systems, vol. 5499, G. Pezzulo, M. V. Butz, O. Sigaud, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer, 2009, pp. 247–266.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2008 | Artikel | FH-PUB-ID: 1223 | OA
R. Möller and W. Schenck, “Bootstrapping Cognition from Behavior-A Computerized Thought Experiment,” Cognitive Science, vol. 32, no. 3, pp. 504–542, 2008.
HSBI-PUB | DOI | Download (ext.)
 
[6]
2007 | Artikel | FH-PUB-ID: 1225
T. Kollmeier, F. Röben, W. Schenck, and R. Möller, “Spectral contrasts for landmark navigation,” Journal of the Optical Society of America A, vol. 24, no. 1, pp. 1–10, 2007.
HSBI-PUB | DOI | Download (ext.)
 
[5]
2007 | Buchbeitrag | FH-PUB-ID: 1229
W. Schenck and R. Möller, “Training and Application of a Visual Forward Model for a Robot Camera Head,” in Anticipatory Behavior in Adaptive Learning Systems, vol. 4520, M. V. Butz, O. Sigaud, G. Pezzulo, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 153–169.
HSBI-PUB | DOI | Download (ext.)
 
[4]
2007 | Artikel | FH-PUB-ID: 1226
M. Kiefer, S. Schuch, W. Schenck, and K. Fiedler, “Mood States Modulate Activity in Semantic Brain Areas during Emotional Word Encoding,” Cerebral Cortex, vol. 17, no. 7, pp. 1516–1530, 2007.
HSBI-PUB | DOI | Download (ext.)
 
[3]
2007 | Artikel | FH-PUB-ID: 1224 | OA
M. Kiefer, S. Schuch, W. Schenck, and K. Fiedler, “Emotion and memory: Event-related potential indices predictive for subsequent successful memory depend on the emotional mood state,” Advances in Cognitive Psychology, vol. 3, no. 3, pp. 363–373, 2007.
HSBI-PUB | DOI | Download (ext.)
 
[2]
2005 | Artikel | FH-PUB-ID: 1227
H. Hoffmann, W. Schenck, and R. Möller, “Learning visuomotor transformations for gaze-control and grasping,” Biological Cybernetics, vol. 93, no. 2, pp. 119–130, 2005.
HSBI-PUB | DOI
 
[1]
2005 | Artikel | FH-PUB-ID: 1228
K. Fiedler, W. Schenck, M. Watling, and J. I. Menges, “Priming Trait Inferences Through Pictures and Moving Pictures: The Impact of Open and Closed Mindsets.,” Journal of Personality and Social Psychology, vol. 88, no. 2, pp. 229–244, 2005.
HSBI-PUB | DOI
 
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63 Publikationen

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[63]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6080
F. Jalil, J. Leuering, Q. A. Ahmed, W. Schenck, and T. Jungeblut, “NNXC: Neural Network Meets Approximate Computing,” presented at the KI und ihre Anwendungen – Aktuelle Forschungsarbeiten des wissenschaftlichen Nachwuchses, Bielefeld, 2025.
HSBI-PUB
 
[62]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6081
F. Jalil, J. Leuering, Q. A. Ahmed, W. Schenck, and T. Jungeblut, “AutoDSE: Towards HW/AI Co-design of Ultra-low Latency Hardware Accelerators for Industrial Applications,” in Workshop on AI and its Applications, Bielefeld, 2025.
HSBI-PUB | DOI
 
[61]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6077
J. Leuering, F. Jalil, Q. A. Ahmed, W. Schenck, and T. Jungeblut, “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.
HSBI-PUB
 
[60]
2025 | Artikel | FH-PUB-ID: 6244 | OA
M. Niederhaus, N. Migenda, J. Weller, M. Kohlhase, and W. Schenck, “Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems,” Big Data and Cognitive Computing, vol. 9, no. 10, 2025.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[59]
2025 | Artikel | FH-PUB-ID: 6133 | OA
T. C. Herzig et al., “Softwaregestützte Analyse geriatrischer Entlassbriefe,” Zeitschrift für Gerontologie und Geriatrie, 2025.
HSBI-PUB | DOI | Download (ext.)
 
[58]
2024 | Artikel | FH-PUB-ID: 5568
A. Tharwat and W. Schenck, “Active Learning for Handling Missing Data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 3273–3287, 2024.
HSBI-PUB | DOI
 
[57]
2024 | Artikel | FH-PUB-ID: 5566
A. Tharwat and W. Schenck, “Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 8, pp. 4317–4330, 2024.
HSBI-PUB | DOI
 
[56]
2024 | Artikel | FH-PUB-ID: 5495
A. Tharwat and W. Schenck, “Using Methods From Dimensionality Reduction for Active Learning With Low Query Budget,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 8, pp. 4317–4330, 2024.
HSBI-PUB | DOI
 
[55]
2024 | Konferenzbeitrag | FH-PUB-ID: 5494
J. M. Akay and W. Schenck, “Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition,” in Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII, Lugano, Switzerland, 2024, pp. 427–444.
HSBI-PUB | DOI
 
[54]
2024 | Artikel | FH-PUB-ID: 5497
J. Weller, N. Migenda, S. von Enzberg, M. Kohlhase, W. Schenck, and R. Dumitrescu, “Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories,” Procedia CIRP, vol. 128, pp. 424–429, 2024.
HSBI-PUB | DOI
 
[53]
2024 | Diskussionspapier | FH-PUB-ID: 5498
B. Hammer et al., Sustainable Life-Cycle of Intelligent Socio-Technical Systems. Bielefeld University, 2024.
HSBI-PUB | DOI
 
[52]
2024 | Artikel | FH-PUB-ID: 5500 | OA
Z. H. Shah et al., “Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data,” GigaScience, vol. 13, 2024.
HSBI-PUB | DOI | Download (ext.)
 
[51]
2024 | Artikel | FH-PUB-ID: 5499
Z. H. Shah et al., “Image restoration in frequency space using complex-valued CNNs,” Frontiers in Artificial Intelligence, vol. 7, 2024.
HSBI-PUB | DOI
 
[50]
2024 | Artikel | FH-PUB-ID: 4050
N. Migenda, R. Möller, and W. Schenck, “Adaptive local Principal Component Analysis improves the clustering of high-dimensional data,” Pattern Recognition, vol. 146, 2024.
HSBI-PUB | DOI
 
[49]
2024 | Artikel | FH-PUB-ID: 4698
N. Migenda, R. Möller, and W. Schenck, “NGPCA: Clustering of high-dimensional and non-stationary data streams,” Software Impacts, vol. 20, 2024.
HSBI-PUB | DOI
 
[48]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
M. Niederhaus, N. Migenda, J. Weller, W. Schenck, and M. Kohlhase, “Technical Readiness of Prescriptive Analytics Platforms: A Survey,” in 2024 35th Conference of Open Innovations Association (FRUCT), Tampere, Finland, 2024, pp. 509–519.
HSBI-PUB | DOI
 
[47]
2024 | Buchbeitrag | FH-PUB-ID: 4915
J. Weller et al., “Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories,” in Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023, vol. 18, O. Niggemann, J. Beyerer, M. Krantz, and C. Kühnert, Eds. Cham: Springer Nature Switzerland, 2024, pp. 89–100.
HSBI-PUB | DOI
 
[46]
2024 | Artikel | FH-PUB-ID: 4913
J. Weller et al., “Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories,” Mathematics, vol. 12, no. 17, 2024.
HSBI-PUB | DOI
 
[45]
2024 | Konferenzbeitrag | FH-PUB-ID: 4644
L. Klein, C. Ostrau, M. Thies, W. Schenck, and U. Rückert, “Exploratory Analysis of Machine Learning Methods for the Prognosis of Falls in Elderly Care Based on Accelerometer Data,” in Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malmö, Sweden, November 27-29, 2023, Proceedings, Malmö, Schweden, 2024, pp. 423–437.
HSBI-PUB | DOI
 
[44]
2023 | Artikel | FH-PUB-ID: 2774 | OA
A. Tharwat and W. Schenck, “A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions,” Mathematics, vol. 11, no. 4, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[43]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
J. Weller, N. Migenda, A. Wegel, M. Kohlhase, W. Schenck, and R. Dumitrescu, “Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories,” in 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), Marrakesh, Morocco, 2023, pp. 1–7.
HSBI-PUB | DOI
 
[42]
2023 | Konferenzbeitrag | FH-PUB-ID: 4293
C. Schwan and W. Schenck, “Object View Prediction with Aleatoric Uncertainty for Robotic Grasping,” in 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 2023, pp. 1–8.
HSBI-PUB | DOI
 
[41]
2023 | Artikel | FH-PUB-ID: 3453 | OA
N. Grimmelsmann, M. Mechtenberg, W. Schenck, H. G. Meyer, and A. Schneider, “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, vol. 18, no. 8, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[40]
2022 | Artikel | FH-PUB-ID: 2775 | OA
A. Tharwat and W. Schenck, “A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data,” Mathematics, vol. 10, no. 7, 2022.
HSBI-PUB | DOI | Download (ext.)
 
[39]
2022 | Artikel | FH-PUB-ID: 1799 | OA
K. Vandevoorde, L. Vollenkemper, C. Schwan, M. Kohlhase, and W. Schenck, “Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks,” Sensors, vol. 22, no. 7, 2022.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[38]
2022 | Konferenzbeitrag | FH-PUB-ID: 2945
Z. H. Shah, M. Muller, B. Hammer, T. Huser, and W. Schenck, “Impact of different loss functions on denoising of microscopic images,” in 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1–10.
HSBI-PUB | DOI
 
[37]
2022 | Artikel | FH-PUB-ID: 2944 | OA
W. Zai El Amri, F. Reinhart, and W. Schenck, “Open set task augmentation facilitates generalization of deep neural networks trained on small data sets,” Neural Computing and Applications, vol. 34, no. 8, pp. 6067–6083, 2022.
HSBI-PUB | DOI | Download (ext.)
 
[36]
2022 | Konferenzbeitrag | FH-PUB-ID: 2776 | OA
C. Schwan and W. Schenck, “Design of Interpretable Machine Learning Tasks for the Application to Industrial Order Picking,” in Kommunikation und Bildverarbeitung in der Automation. Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020, 2022, pp. 291–303.
HSBI-PUB | DOI | Download (ext.)
 
[35]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
C. Hoppe, N. Migenda, D. Pelkmann, D. A. Hötte, and W. Schenck, “Collaborative System for Question Answering in German Case Law Documents,” in Collaborative Networks in Digitalization and Society 5.0, Lisbon, Portugal, 2022, pp. 303–312.
HSBI-PUB | DOI
 
[34]
2021 | Artikel | FH-PUB-ID: 1202
A. Tharwat and W. Schenck, “A conceptual and practical comparison of PSO-style optimization algorithms,” Expert Systems with Applications, vol. 167, 2021.
HSBI-PUB | DOI
 
[33]
2021 | Artikel | FH-PUB-ID: 2777
A. Tharwat and W. Schenck, “Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques,” Swarm and Evolutionary Computation, vol. 67, 2021.
HSBI-PUB | DOI
 
[32]
2021 | Artikel | FH-PUB-ID: 1201 | OA
Z. H. Shah et al., “Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images,” Photonics Research, vol. 9, no. 5, 2021.
HSBI-PUB | DOI | Download (ext.)
 
[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
C. Hoppe, D. Pelkmann, N. Migenda, D. A. Hotte, and W. Schenck, “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), Laguna Hills, CA, USA, 2021, pp. 29–32.
HSBI-PUB | DOI
 
[30]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
T. Voigt et al., “Advanced Data Analytics Platform for Manufacturing Companies,” in 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), Vasteras, Sweden, 2021, pp. 01–08.
HSBI-PUB | DOI
 
[29]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
L. Steinmann, N. Migenda, T. Voigt, M. Kohlhase, and W. Schenck, “Variational Autoencoder based Novelty Detection for Real-World Time Series,” in 2021 3rd International Conference on Management Science and Industrial Engineering, Osaka Japan, 2021, pp. 1–7.
HSBI-PUB | DOI
 
[28]
2021 | Artikel | FH-PUB-ID: 1203
N. Migenda, R. Möller, and W. Schenck, “Adaptive dimensionality reduction for neural network-based online principal component analysis,” PLOS ONE, vol. 16, no. 3, 2021.
HSBI-PUB | DOI
 
[27]
2020 | Artikel | FH-PUB-ID: 1204
A. Tharwat and W. Schenck, “Balancing Exploration and Exploitation: A novel active learner for imbalanced data,” Knowledge-Based Systems, vol. 210, 2020.
HSBI-PUB | DOI
 
[26]
2020 | Konferenzbeitrag | FH-PUB-ID: 1206
D. Pelkmann, A. Tharwat, and W. Schenck, “How to Label? Combining Experts’ Knowledge for German Text Classification,” in 2020 7th Swiss Conference on Data Science (SDS), Luzern, Switzerland, 2020, pp. 61–62.
HSBI-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1207
C. Schwan and W. Schenck, “Visual Movement Prediction for Stable Grasp Point Detection,” in Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Proceedings of the EANN 2020, Halkidiki, Greece, 2020, pp. 70–81.
HSBI-PUB | DOI
 
[24]
2020 | Diskussionspapier | FH-PUB-ID: 2778 | OA
Z. H. Shah et al., Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Cold Spring Harbor Laboratory, 2020.
HSBI-PUB | DOI | Download (ext.)
 
[23]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
N. Migenda and W. Schenck, “Adaptive Dimensionality Reduction for Local Principal Component Analysis,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 2020, pp. 1579–1586.
HSBI-PUB | DOI
 
[22]
2019 | Buchbeitrag | FH-PUB-ID: 1208
N. Migenda, R. Möller, and W. Schenck, “Adaptive Dimensionality Adjustment for Online ‘Principal Component Analysis,’” in Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I, H. Yin, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, R. Menezes, and R. Allmendinger, Eds. Cham: Springer International Publishing, 2019, pp. 76–84.
HSBI-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209 | OA
K. Grünberg and W. Schenck, “A Case Study on Benchmarking IoT Cloud Services,” in Cloud Computing – CLOUD 2018, M. Luo and L.-J. Zhang, Eds. Cham: Springer International Publishing, 2018, pp. 398–406.
HSBI-PUB | DOI | Download (ext.)
 
[20]
2017 | Artikel | FH-PUB-ID: 1214
W. Schenck, M. Horst, T. Tiedemann, S. Gaulik, and R. Möller, “Comparing parallel hardware architectures for visually guided robot navigation,” Concurrency and Computation: Practice and Experience, vol. 29, no. 4, 2017.
HSBI-PUB | DOI
 
[19]
2017 | Artikel | FH-PUB-ID: 1210 | OA
S. Kunkel and W. Schenck, “The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code,” Frontiers in Neuroinformatics, vol. 11, 2017.
HSBI-PUB | DOI | Download (ext.)
 
[18]
2017 | Artikel | FH-PUB-ID: 1211
W. Schenck, S. El Sayed, M. Foszczynski, W. Homberg, and D. Pleiter, “Evaluation and Performance Modeling of a Burst Buffer Solution,” ACM SIGOPS Operating Systems Review, vol. 50, no. 2, pp. 12–26, 2017.
HSBI-PUB | DOI
 
[17]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212 | OA
M. Butz, W. Schenck, and A. van Ooyen, Eds., Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA, 2017.
HSBI-PUB | DOI | Download (ext.)
 
[16]
2016 | Buchbeitrag | FH-PUB-ID: 1215
W. Schenck, S. El Sayed, M. Foszczynski, W. Homberg, and D. Pleiter, “Early Evaluation of the ‘Infinite Memory Engine’ Burst Buffer Solution,” in High Performance Computing, vol. vol 9945, M. Taufer, B. Mohr, and J. M. Kunkel, Eds. Cham: Springer International Publishing, 2016, pp. 604–615.
HSBI-PUB | DOI | Download (ext.)
 
[15]
2016 | Artikel | FH-PUB-ID: 1213 | OA
M. Butz, W. Schenck, and A. van Ooyen, “Editorial: Anatomy and Plasticity in Large-Scale Brain Models,” Frontiers in Neuroanatomy, vol. 10, 2016.
HSBI-PUB | DOI | Download (ext.)
 
[14]
2015 | Buchbeitrag | FH-PUB-ID: 1216
A. V. Adinetz et al., “Performance Evaluation of Scientific Applications on POWER8,” in High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, vol. 8966, S. A. Jarvis, S. A. Wright, and S. D. Hammond, Eds. Cham: Springer International Publishing, 2015, pp. 24–45.
HSBI-PUB | DOI
 
[13]
2013 | Artikel | FH-PUB-ID: 1217
W. Schenck, “Robot studies on saccade-triggered visual prediction,” New Ideas in Psychology, vol. 31, no. 3, pp. 221–238, 2013.
HSBI-PUB | DOI | Download (ext.)
 
[12]
2013 | Artikel | FH-PUB-ID: 1218
A. Kaiser, W. Schenck, and R. Möller, “Solving the correspondence problem in stereo vision by internal simulation,” Adaptive Behavior, vol. 21, no. 4, pp. 239–250, 2013.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2012 | Artikel | FH-PUB-ID: 1221
A. Kaiser, W. Schenck, and R. Möller, “COUPLED SINGULAR VALUE DECOMPOSITION OF A CROSS-COVARIANCE MATRIX,” International Journal of Neural Systems, vol. 20, no. 04, pp. 293–318, 2012.
HSBI-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1219 | OA
W. Schenck, H. Hoffmann, and R. Möller, “Grasping of extrafoveal targets: A robotic model,” New Ideas in Psychology, vol. 29, no. 3, pp. 235–259, 2011.
HSBI-PUB | DOI | Download (ext.)
 
[9]
2011 | Artikel | FH-PUB-ID: 1220 | OA
W. Schenck, “Kinematic motor learning,” Connection Science, vol. 23, no. 4, pp. 239–283, 2011.
HSBI-PUB | DOI | Download (ext.)
 
[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
W. Schenck, “Space Perception through Visuokinesthetic Prediction,” in Anticipatory Behavior in Adaptive Learning Systems, vol. 5499, G. Pezzulo, M. V. Butz, O. Sigaud, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer, 2009, pp. 247–266.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2008 | Artikel | FH-PUB-ID: 1223 | OA
R. Möller and W. Schenck, “Bootstrapping Cognition from Behavior-A Computerized Thought Experiment,” Cognitive Science, vol. 32, no. 3, pp. 504–542, 2008.
HSBI-PUB | DOI | Download (ext.)
 
[6]
2007 | Artikel | FH-PUB-ID: 1225
T. Kollmeier, F. Röben, W. Schenck, and R. Möller, “Spectral contrasts for landmark navigation,” Journal of the Optical Society of America A, vol. 24, no. 1, pp. 1–10, 2007.
HSBI-PUB | DOI | Download (ext.)
 
[5]
2007 | Buchbeitrag | FH-PUB-ID: 1229
W. Schenck and R. Möller, “Training and Application of a Visual Forward Model for a Robot Camera Head,” in Anticipatory Behavior in Adaptive Learning Systems, vol. 4520, M. V. Butz, O. Sigaud, G. Pezzulo, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 153–169.
HSBI-PUB | DOI | Download (ext.)
 
[4]
2007 | Artikel | FH-PUB-ID: 1226
M. Kiefer, S. Schuch, W. Schenck, and K. Fiedler, “Mood States Modulate Activity in Semantic Brain Areas during Emotional Word Encoding,” Cerebral Cortex, vol. 17, no. 7, pp. 1516–1530, 2007.
HSBI-PUB | DOI | Download (ext.)
 
[3]
2007 | Artikel | FH-PUB-ID: 1224 | OA
M. Kiefer, S. Schuch, W. Schenck, and K. Fiedler, “Emotion and memory: Event-related potential indices predictive for subsequent successful memory depend on the emotional mood state,” Advances in Cognitive Psychology, vol. 3, no. 3, pp. 363–373, 2007.
HSBI-PUB | DOI | Download (ext.)
 
[2]
2005 | Artikel | FH-PUB-ID: 1227
H. Hoffmann, W. Schenck, and R. Möller, “Learning visuomotor transformations for gaze-control and grasping,” Biological Cybernetics, vol. 93, no. 2, pp. 119–130, 2005.
HSBI-PUB | DOI
 
[1]
2005 | Artikel | FH-PUB-ID: 1228
K. Fiedler, W. Schenck, M. Watling, and J. I. Menges, “Priming Trait Inferences Through Pictures and Moving Pictures: The Impact of Open and Closed Mindsets.,” Journal of Personality and Social Psychology, vol. 88, no. 2, pp. 229–244, 2005.
HSBI-PUB | DOI
 
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