PUBLIKATIONSSERVER

Wolfram Schenck

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

42 Publikationen

Alle markieren

[42]
2023 | Artikel | FH-PUB-ID: 3453
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
 
[41]
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.)
 
[40]
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
 
[39]
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.)
 
[38]
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.)
 
[37]
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.)
 
[36]
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
 
[35]
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.)
 
[34]
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.)
 
[33]
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
 
[32]
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
 
[31]
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
 
[30]
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
 
[29]
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
 
[28]
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
 
[27]
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.)
 
[26]
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
 
[25]
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
 
[24]
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
 
[23]
2020 | Buchbeitrag | 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, L. Iliadis, P. P. Angelov, C. Jayne, and E. Pimenidis, Eds. Cham: Springer International Publishing, 2020, pp. 70–81.
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
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
 
[20]
2017 | Artikel | FH-PUB-ID: 1210
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
 
[19]
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
 
[18]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212
M. Butz, W. Schenck, and A. van Ooyen, Eds., Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA, 2017.
HSBI-PUB | DOI
 
[17]
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
 
[16]
2016 | Artikel | FH-PUB-ID: 1213
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
 
[15]
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, M. Taufer, B. Mohr, and J. M. Kunkel, Eds. Cham: Springer International Publishing, 2016, pp. 604–615.
HSBI-PUB | DOI
 
[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, 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
 
[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
 
[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: 1220
W. Schenck, “Kinematic motor learning,” Connection Science, vol. 23, no. 4, pp. 239–283, 2011.
HSBI-PUB | DOI
 
[9]
2011 | Artikel | FH-PUB-ID: 1219
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
 
[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
W. Schenck, “Space Perception through Visuokinesthetic Prediction,” in Anticipatory Behavior in Adaptive Learning Systems, G. Pezzulo, M. V. Butz, O. Sigaud, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 247–266.
HSBI-PUB | DOI
 
[7]
2008 | Artikel | FH-PUB-ID: 1223
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
 
[6]
2007 | Artikel | FH-PUB-ID: 1224
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
 
[5]
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, 2007.
HSBI-PUB | DOI
 
[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
 
[3]
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, M. V. Butz, O. Sigaud, G. Pezzulo, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 153–169.
HSBI-PUB | DOI
 
[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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42 Publikationen

Alle markieren

[42]
2023 | Artikel | FH-PUB-ID: 3453
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
 
[41]
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.)
 
[40]
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
 
[39]
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.)
 
[38]
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.)
 
[37]
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.)
 
[36]
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
 
[35]
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.)
 
[34]
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.)
 
[33]
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
 
[32]
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
 
[31]
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
 
[30]
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
 
[29]
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
 
[28]
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
 
[27]
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.)
 
[26]
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
 
[25]
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
 
[24]
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
 
[23]
2020 | Buchbeitrag | 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, L. Iliadis, P. P. Angelov, C. Jayne, and E. Pimenidis, Eds. Cham: Springer International Publishing, 2020, pp. 70–81.
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
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
 
[20]
2017 | Artikel | FH-PUB-ID: 1210
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
 
[19]
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
 
[18]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212
M. Butz, W. Schenck, and A. van Ooyen, Eds., Anatomy and Plasticity in Large-Scale Brain Models. Frontiers Media SA, 2017.
HSBI-PUB | DOI
 
[17]
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
 
[16]
2016 | Artikel | FH-PUB-ID: 1213
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
 
[15]
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, M. Taufer, B. Mohr, and J. M. Kunkel, Eds. Cham: Springer International Publishing, 2016, pp. 604–615.
HSBI-PUB | DOI
 
[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, 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
 
[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
 
[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: 1220
W. Schenck, “Kinematic motor learning,” Connection Science, vol. 23, no. 4, pp. 239–283, 2011.
HSBI-PUB | DOI
 
[9]
2011 | Artikel | FH-PUB-ID: 1219
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
 
[8]
2009 | Buchbeitrag | FH-PUB-ID: 1222
W. Schenck, “Space Perception through Visuokinesthetic Prediction,” in Anticipatory Behavior in Adaptive Learning Systems, G. Pezzulo, M. V. Butz, O. Sigaud, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 247–266.
HSBI-PUB | DOI
 
[7]
2008 | Artikel | FH-PUB-ID: 1223
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
 
[6]
2007 | Artikel | FH-PUB-ID: 1224
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
 
[5]
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, 2007.
HSBI-PUB | DOI
 
[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
 
[3]
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, M. V. Butz, O. Sigaud, G. Pezzulo, and G. Baldassarre, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 153–169.
HSBI-PUB | DOI
 
[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
 
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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.
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