Tim Voigt
10 Publikationen
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 379–390). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_37
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| DOI
2022 | Buchbeitrag | FH-PUB-ID: 2291 | 
Hanitz, M., Schöne, M., Voigt, T., & Kohlhase, M. (2022). Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 (pp. 121–135). Leipzig: ibai-publishing.
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2021). Space-Filling Designs for Experiments with Assembled Products. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 192–199). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460854
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2021 | Artikel | FH-PUB-ID: 3717 |
Voigt, T., Kohlhase, M., & Nelles, O. (2021). Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics, 9(19). https://doi.org/10.3390/math9192479
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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
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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
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| DOI
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
Voigt, T., Kohlhase, M., & Nelles, O. (2020). Incremental Latin Hypercube Additive Design for LOLIMOT. In Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1602–1609). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212173
HSBI-PUB
| DOI
2020 | Artikel | FH-PUB-ID: 1368
Voigt, T., Kohlhase, M., & Peter, A. (2020). Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin, 62(04), 62–69.
HSBI-PUB
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 |
Voigt, T., Kohlhase, M., & Nelles, O. (2019). Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In Proceedings - 29. Workshop Computational Intelligence (pp. 267–288). Dortmund: KIT Scientific Publishing, Karlsruhe. https://doi.org/10.5445/KSP/1000098736
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2018 | Konferenzbeitrag | FH-PUB-ID: 1369 |
Voigt, T., & Kohlhase, M. (2018). Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In Proceedings - 28. Workshop Computational Intelligence (pp. 93–111). KIT Scientific Publishing, Karlsruhe.
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10 Publikationen
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 379–390). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_37
HSBI-PUB
| DOI
2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, M., Schöne, M., Voigt, T., & Kohlhase, M. (2022). Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 (pp. 121–135). Leipzig: ibai-publishing.
HSBI-PUB
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2021). Space-Filling Designs for Experiments with Assembled Products. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 192–199). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460854
HSBI-PUB
| DOI
| Download (ext.)
2021 | Artikel | FH-PUB-ID: 3717 |
Voigt, T., Kohlhase, M., & Nelles, O. (2021). Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics, 9(19). https://doi.org/10.3390/math9192479
HSBI-PUB
| DOI
| Download (ext.)
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
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
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
Voigt, T., Kohlhase, M., & Nelles, O. (2020). Incremental Latin Hypercube Additive Design for LOLIMOT. In Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1602–1609). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212173
HSBI-PUB
| DOI
2020 | Artikel | FH-PUB-ID: 1368
Voigt, T., Kohlhase, M., & Peter, A. (2020). Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin, 62(04), 62–69.
HSBI-PUB
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 |
Voigt, T., Kohlhase, M., & Nelles, O. (2019). Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In Proceedings - 29. Workshop Computational Intelligence (pp. 267–288). Dortmund: KIT Scientific Publishing, Karlsruhe. https://doi.org/10.5445/KSP/1000098736
HSBI-PUB
| DOI
| Download (ext.)
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 |
Voigt, T., & Kohlhase, M. (2018). Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In Proceedings - 28. Workshop Computational Intelligence (pp. 93–111). KIT Scientific Publishing, Karlsruhe.
HSBI-PUB
| Download (ext.)