31 Publikationen

Alle markieren

[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 1373
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (n.d.). Advanced Data Analytics Platform for Manufacturing Companies. In 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Västerås, Sweden: IEEE.
FH-PUB | Download (ext.)
 
[30]
2021 | Artikel | FH-PUB-ID: 1201
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
FH-PUB | DOI
 
[29]
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
FH-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
FH-PUB | DOI
 
[27]
2020 | Preprint | FH-PUB-ID: 1308
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. BioRxiv. Cold Spring Harbor Laboratory.
FH-PUB
 
[26]
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
FH-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1205
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
FH-PUB | DOI
 
[24]
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
FH-PUB | DOI
 
[23]
2020 | Buchbeitrag | 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
FH-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
FH-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209
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
FH-PUB | DOI
 
[20]
2017 | Artikel | FH-PUB-ID: 1210
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
FH-PUB | DOI
 
[19]
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
FH-PUB | DOI
 
[18]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212
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
FH-PUB | DOI
 
[17]
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
FH-PUB | DOI
 
[16]
2016 | Artikel | FH-PUB-ID: 1213
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
FH-PUB | DOI
 
[15]
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 (pp. 604–615). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-46079-6_41
FH-PUB | DOI
 
[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 (pp. 24–45). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-17248-4_2
FH-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
FH-PUB | DOI
 
[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
FH-PUB | DOI
 
[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
FH-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1220
Schenck, W. (2011). Kinematic motor learning. Connection Science, 23(4), 239–283. https://doi.org/10.1080/09540091.2011.625077
FH-PUB | DOI
 
[9]
2011 | Artikel | FH-PUB-ID: 1219
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
FH-PUB | DOI
 
[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 (pp. 247–266). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-02565-5_14
FH-PUB | DOI
 
[7]
2008 | Artikel | FH-PUB-ID: 1223
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
FH-PUB | DOI
 
[6]
2007 | Artikel | FH-PUB-ID: 1224
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
FH-PUB | DOI
 
[5]
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). https://doi.org/10.1364/JOSAA.24.000001
FH-PUB | DOI
 
[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
FH-PUB | DOI
 
[3]
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 (pp. 153–169). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_9
FH-PUB | DOI
 
[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
FH-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
FH-PUB | DOI
 

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31 Publikationen

Alle markieren

[31]
2021 | Konferenzbeitrag | FH-PUB-ID: 1373
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (n.d.). Advanced Data Analytics Platform for Manufacturing Companies. In 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Västerås, Sweden: IEEE.
FH-PUB | Download (ext.)
 
[30]
2021 | Artikel | FH-PUB-ID: 1201
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
FH-PUB | DOI
 
[29]
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
FH-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
FH-PUB | DOI
 
[27]
2020 | Preprint | FH-PUB-ID: 1308
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. BioRxiv. Cold Spring Harbor Laboratory.
FH-PUB
 
[26]
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
FH-PUB | DOI
 
[25]
2020 | Konferenzbeitrag | FH-PUB-ID: 1205
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
FH-PUB | DOI
 
[24]
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
FH-PUB | DOI
 
[23]
2020 | Buchbeitrag | 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
FH-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
FH-PUB | DOI
 
[21]
2018 | Buchbeitrag | FH-PUB-ID: 1209
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
FH-PUB | DOI
 
[20]
2017 | Artikel | FH-PUB-ID: 1210
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
FH-PUB | DOI
 
[19]
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
FH-PUB | DOI
 
[18]
2017 | Buch als Herausgeber | FH-PUB-ID: 1212
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
FH-PUB | DOI
 
[17]
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
FH-PUB | DOI
 
[16]
2016 | Artikel | FH-PUB-ID: 1213
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
FH-PUB | DOI
 
[15]
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 (pp. 604–615). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-46079-6_41
FH-PUB | DOI
 
[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 (pp. 24–45). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-17248-4_2
FH-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
FH-PUB | DOI
 
[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
FH-PUB | DOI
 
[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
FH-PUB | DOI
 
[10]
2011 | Artikel | FH-PUB-ID: 1220
Schenck, W. (2011). Kinematic motor learning. Connection Science, 23(4), 239–283. https://doi.org/10.1080/09540091.2011.625077
FH-PUB | DOI
 
[9]
2011 | Artikel | FH-PUB-ID: 1219
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
FH-PUB | DOI
 
[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 (pp. 247–266). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-02565-5_14
FH-PUB | DOI
 
[7]
2008 | Artikel | FH-PUB-ID: 1223
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
FH-PUB | DOI
 
[6]
2007 | Artikel | FH-PUB-ID: 1224
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
FH-PUB | DOI
 
[5]
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). https://doi.org/10.1364/JOSAA.24.000001
FH-PUB | DOI
 
[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
FH-PUB | DOI
 
[3]
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 (pp. 153–169). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_9
FH-PUB | DOI
 
[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
FH-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
FH-PUB | DOI
 

Suche

Publikationen filtern

Darstellung / Sortierung

Zitationsstil: APA

Export / Einbettung