PUBLIKATIONSSERVER

Martin Kohlhase

https://orcid.org/0009-0002-9374-0720

40 Publikationen

Alle markieren

[40]
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, and M. Kohlhase, “Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), Trondheim, Norway, 2025, pp. 1–9.
HSBI-PUB | DOI | Download (ext.)
 
[39]
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.)
 
[38]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6371 | OA
F.-M. Dockhorn and M. Kohlhase, “Discrepancy Modeling for Dynamical Systems,” in Proceedings - 35. Workshop Computational Intelligence, 2025.
HSBI-PUB | DOI | Download (ext.)
 
[37]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, and M. Kohlhase, “Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), Trondheim, Norway, 2025, pp. 1–9.
HSBI-PUB | Dateien verfügbar | DOI
 
[36]
2025 | Artikel | FH-PUB-ID: 6297
V. Katter, C. Huperz, and M. Kohlhase, “Sensorintegration in Orthesen zur Versorgung des Diabetischen Fußsyndroms: eine technische Betrachtung,” Orthopädie Technik, no. 11, pp. 68–73, 2025.
HSBI-PUB | Download (ext.)
 
[35]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6298 | OA
V. Katter and M. Kohlhase, “Efficient Gait Analysis using Knowledge Distillation from Sparse Sensors,” in Proceedings – 35. Workshop Computational Intelligence: Berlin, 20.–21. November 2025, Berlin, 2025, pp. 89–96.
HSBI-PUB | DOI | Download (ext.)
 
[34]
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
B. Jaster and M. Kohlhase, “Trust Issues in Active Learning and Their Impact on Real-World Applications,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), Trondheim, Norway, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[33]
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 | OA
B. Jaster and M. Kohlhase, “Trust Issues in Active Learning and Their Impact on Real-World Applications,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), Trondheim, Norway, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI
 
[32]
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
J. Bültemeier, C.-A. Holst, V. Lohweg, M. Schöne, B. Jaster, and M. Kohlhase, “AI Workflow for Scarce Data: A Modular Approach to Optimise Processes,” in 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025, pp. 1–4.
HSBI-PUB | DOI
 
[31]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
M. Schöne, B. Jaster, J. Bültemeier, and M. Kohlhase, “Informed Active Learning with Decision Trees to Balance Exploration and Exploitation,” in Kongress KI@HSBI2025 Zukunft im Fokus – Posterbeiträge, vol. 2, Institute for Data Science Solutions, Ed. Bielefeld: Hochschule Bielefeld, 2025, pp. 26–27.
HSBI-PUB | DOI | Download (ext.)
 
[30]
2025 | Artikel | FH-PUB-ID: 6268 | OA
M. Schöne, M. Kohlhase, and O. Nelles, “Incorporation of structural properties of the response surface into oblique model trees,” at - Automatisierungstechnik, vol. 73, no. 10, pp. 727–739, 2025.
HSBI-PUB | DOI | Download (ext.)
 
[29]
2024 | Konferenzbeitrag | FH-PUB-ID: 5789 | OA
F.-M. Dockhorn and M. Kohlhase, “An Application-oriented Review of Standard and Integral Sparse Identification of Nonlinear Dynamics,” in Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, Berlin, 2024, pp. 53–76.
HSBI-PUB | DOI | Download (ext.)
 
[28]
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | OA
J. Bültemeier, M. Schöne, M. Kohlhase, C.-A. Holst, V. Lohweg, and O. Nelles, “Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs,” in Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, Berlin, 2024, pp. 217–231.
HSBI-PUB | DOI | Download (ext.)
 
[27]
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
 
[26]
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
 
[25]
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
 
[24]
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
 
[23]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
B. Jaster and M. Kohlhase, “Active Learning for Regression Problems with Ensemble Methods,” in Proceedings - 33. Workshop Computational Intelligence, Berlin, 2023, pp. 9–29.
HSBI-PUB | DOI | Download (ext.)
 
[22]
2023 | Artikel | FH-PUB-ID: 2849 | OA
L. Vollenkemper, F. Grumbach, M. Kohlhase, and P. Reusch, “Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops,” wt Werkstattstechnik online, vol. 113, no. 04, pp. 158–163, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[21]
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
 
[20]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
J. Kösters, M. Schöne, and M. Kohlhase, Benchmarking of Machine Learning Models for Tabular Scarce Data. .
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[19]
2023 | Artikel | FH-PUB-ID: 2855 | OA
L. Vollenkemper et al., “HUMANZENTRIERTE PRODUKTIONSPLANUNG MIT KI - Entwicklung eines Assistenzsystems,” Arbeitswelt.Plus Working Paper, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[18]
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.)
 
[17]
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes,” in Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Manchester, UK, 2022, pp. 379–390.
HSBI-PUB | DOI
 
[16]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
M. Hanitz, M. Schöne, T. Voigt, and M. Kohlhase, “Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD,” in Machine Learning and Data Mining in Pattern Recognition, MLDM 2022, P. Perner, Ed. Leipzig: ibai-publishing, 2022, pp. 121–135.
HSBI-PUB | Download (ext.)
 
[15]
2022 | Konferenzbeitrag | FH-PUB-ID: 2277 | OA
L. Vollenkemper and M. Kohlhase, “Spatial Temporal Transformer Networks for Sparse Motion Capture Applications,” in PROCEEDINGS 32. WORKSHOP COMPUTATIONAL INTELLIGENCE, Berlin, 2022, vol. 32.
HSBI-PUB | DOI | Download (ext.)
 
[14]
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
M. Schöne and M. Kohlhase, “Curvature-Oriented Splitting for Multivariate Model Trees,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 2021, pp. 01–09.
HSBI-PUB | DOI | Download (ext.)
 
[13]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
J. Ewerszumrode, M. Schöne, S. Godt, and M. Kohlhase, “Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP,” in Proceedings - 31. Workshop Computational Intelligence , Berlin, 2021, pp. 285–305.
HSBI-PUB | DOI | Download (ext.)
 
[12]
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Space-Filling Designs for Experiments with Assembled Products,” in 2021 3rd International Conference on Management Science and Industrial Engineering, Osaka Japan, 2021, pp. 192–199.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2021 | Artikel | FH-PUB-ID: 3717 | OA
T. Voigt, M. Kohlhase, and O. Nelles, “Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge,” Mathematics, vol. 9, no. 19, 2021.
HSBI-PUB | DOI | Download (ext.)
 
[10]
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
 
[9]
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
 
[8]
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
M. Schöne and M. Kohlhase, “Least Squares Approach for Multivariate Split Selection in Regression Trees,” in Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Guimaraes, Portugal, 2020, pp. 41–50.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
M. Schöne and M. Kohlhase, “Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees,” in Proceedings - 30. Workshop Computational Intelligence, H. Schulte, F. Hoffmann, and R. Mikut, Eds. Karlsruhe: KIT Scientific Publishing, 2020.
HSBI-PUB | DOI | Download (ext.)
 
[6]
2020 | Konferenzbeitrag | FH-PUB-ID: 1557
S. Godt and M. Kohlhase, “Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen,” in Proceedings - 30. Workshop Computational Intelligence, Berlin, 2020, pp. 159–180.
HSBI-PUB | DOI
 
[5]
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
T. Voigt, M. Kohlhase, and O. Nelles, “Incremental Latin Hypercube Additive Design for LOLIMOT,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 2020, pp. 1602–1609.
HSBI-PUB | DOI
 
[4]
2020 | Artikel | FH-PUB-ID: 1368
T. Voigt, M. Kohlhase, and A. Peter, “Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels,” atp magazin, vol. 62, no. 04, pp. 62–69, 2020.
HSBI-PUB
 
[3]
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 | OA
T. Voigt, M. Kohlhase, and O. Nelles, “Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 2019, pp. 267–288.
HSBI-PUB | DOI | Download (ext.)
 
[2]
2019 | Konferenzbeitrag | FH-PUB-ID: 1559 | OA
S. Godt and M. Kohlhase, “Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 2019, pp. 51–72.
HSBI-PUB | DOI | Download (ext.)
 
[1]
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 | OA
T. Voigt and M. Kohlhase, “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, 2018, pp. 93–111.
HSBI-PUB | Download (ext.)
 
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40 Publikationen

Alle markieren

[40]
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, and M. Kohlhase, “Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), Trondheim, Norway, 2025, pp. 1–9.
HSBI-PUB | DOI | Download (ext.)
 
[39]
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.)
 
[38]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6371 | OA
F.-M. Dockhorn and M. Kohlhase, “Discrepancy Modeling for Dynamical Systems,” in Proceedings - 35. Workshop Computational Intelligence, 2025.
HSBI-PUB | DOI | Download (ext.)
 
[37]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, and M. Kohlhase, “Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), Trondheim, Norway, 2025, pp. 1–9.
HSBI-PUB | Dateien verfügbar | DOI
 
[36]
2025 | Artikel | FH-PUB-ID: 6297
V. Katter, C. Huperz, and M. Kohlhase, “Sensorintegration in Orthesen zur Versorgung des Diabetischen Fußsyndroms: eine technische Betrachtung,” Orthopädie Technik, no. 11, pp. 68–73, 2025.
HSBI-PUB | Download (ext.)
 
[35]
2025 | Kurzbeitrag Konferenz | FH-PUB-ID: 6298 | OA
V. Katter and M. Kohlhase, “Efficient Gait Analysis using Knowledge Distillation from Sparse Sensors,” in Proceedings – 35. Workshop Computational Intelligence: Berlin, 20.–21. November 2025, Berlin, 2025, pp. 89–96.
HSBI-PUB | DOI | Download (ext.)
 
[34]
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
B. Jaster and M. Kohlhase, “Trust Issues in Active Learning and Their Impact on Real-World Applications,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), Trondheim, Norway, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[33]
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 | OA
B. Jaster and M. Kohlhase, “Trust Issues in Active Learning and Their Impact on Real-World Applications,” in 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), Trondheim, Norway, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI
 
[32]
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
J. Bültemeier, C.-A. Holst, V. Lohweg, M. Schöne, B. Jaster, and M. Kohlhase, “AI Workflow for Scarce Data: A Modular Approach to Optimise Processes,” in 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025, pp. 1–4.
HSBI-PUB | DOI
 
[31]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
M. Schöne, B. Jaster, J. Bültemeier, and M. Kohlhase, “Informed Active Learning with Decision Trees to Balance Exploration and Exploitation,” in Kongress KI@HSBI2025 Zukunft im Fokus – Posterbeiträge, vol. 2, Institute for Data Science Solutions, Ed. Bielefeld: Hochschule Bielefeld, 2025, pp. 26–27.
HSBI-PUB | DOI | Download (ext.)
 
[30]
2025 | Artikel | FH-PUB-ID: 6268 | OA
M. Schöne, M. Kohlhase, and O. Nelles, “Incorporation of structural properties of the response surface into oblique model trees,” at - Automatisierungstechnik, vol. 73, no. 10, pp. 727–739, 2025.
HSBI-PUB | DOI | Download (ext.)
 
[29]
2024 | Konferenzbeitrag | FH-PUB-ID: 5789 | OA
F.-M. Dockhorn and M. Kohlhase, “An Application-oriented Review of Standard and Integral Sparse Identification of Nonlinear Dynamics,” in Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, Berlin, 2024, pp. 53–76.
HSBI-PUB | DOI | Download (ext.)
 
[28]
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | OA
J. Bültemeier, M. Schöne, M. Kohlhase, C.-A. Holst, V. Lohweg, and O. Nelles, “Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs,” in Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, Berlin, 2024, pp. 217–231.
HSBI-PUB | DOI | Download (ext.)
 
[27]
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
 
[26]
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
 
[25]
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
 
[24]
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
 
[23]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
B. Jaster and M. Kohlhase, “Active Learning for Regression Problems with Ensemble Methods,” in Proceedings - 33. Workshop Computational Intelligence, Berlin, 2023, pp. 9–29.
HSBI-PUB | DOI | Download (ext.)
 
[22]
2023 | Artikel | FH-PUB-ID: 2849 | OA
L. Vollenkemper, F. Grumbach, M. Kohlhase, and P. Reusch, “Humanzentrierte Ablaufplanung von Montagelinien/Human-centered scheduling in assembly lines - Plug and play: Efficient algorithms minimize stress in flow shops,” wt Werkstattstechnik online, vol. 113, no. 04, pp. 158–163, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[21]
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
 
[20]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
J. Kösters, M. Schöne, and M. Kohlhase, Benchmarking of Machine Learning Models for Tabular Scarce Data. .
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[19]
2023 | Artikel | FH-PUB-ID: 2855 | OA
L. Vollenkemper et al., “HUMANZENTRIERTE PRODUKTIONSPLANUNG MIT KI - Entwicklung eines Assistenzsystems,” Arbeitswelt.Plus Working Paper, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[18]
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.)
 
[17]
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes,” in Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Manchester, UK, 2022, pp. 379–390.
HSBI-PUB | DOI
 
[16]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
M. Hanitz, M. Schöne, T. Voigt, and M. Kohlhase, “Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD,” in Machine Learning and Data Mining in Pattern Recognition, MLDM 2022, P. Perner, Ed. Leipzig: ibai-publishing, 2022, pp. 121–135.
HSBI-PUB | Download (ext.)
 
[15]
2022 | Konferenzbeitrag | FH-PUB-ID: 2277 | OA
L. Vollenkemper and M. Kohlhase, “Spatial Temporal Transformer Networks for Sparse Motion Capture Applications,” in PROCEEDINGS 32. WORKSHOP COMPUTATIONAL INTELLIGENCE, Berlin, 2022, vol. 32.
HSBI-PUB | DOI | Download (ext.)
 
[14]
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
M. Schöne and M. Kohlhase, “Curvature-Oriented Splitting for Multivariate Model Trees,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 2021, pp. 01–09.
HSBI-PUB | DOI | Download (ext.)
 
[13]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
J. Ewerszumrode, M. Schöne, S. Godt, and M. Kohlhase, “Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP,” in Proceedings - 31. Workshop Computational Intelligence , Berlin, 2021, pp. 285–305.
HSBI-PUB | DOI | Download (ext.)
 
[12]
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Space-Filling Designs for Experiments with Assembled Products,” in 2021 3rd International Conference on Management Science and Industrial Engineering, Osaka Japan, 2021, pp. 192–199.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2021 | Artikel | FH-PUB-ID: 3717 | OA
T. Voigt, M. Kohlhase, and O. Nelles, “Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge,” Mathematics, vol. 9, no. 19, 2021.
HSBI-PUB | DOI | Download (ext.)
 
[10]
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
 
[9]
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
 
[8]
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
M. Schöne and M. Kohlhase, “Least Squares Approach for Multivariate Split Selection in Regression Trees,” in Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Guimaraes, Portugal, 2020, pp. 41–50.
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[7]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
M. Schöne and M. Kohlhase, “Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees,” in Proceedings - 30. Workshop Computational Intelligence, H. Schulte, F. Hoffmann, and R. Mikut, Eds. Karlsruhe: KIT Scientific Publishing, 2020.
HSBI-PUB | DOI | Download (ext.)
 
[6]
2020 | Konferenzbeitrag | FH-PUB-ID: 1557
S. Godt and M. Kohlhase, “Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen,” in Proceedings - 30. Workshop Computational Intelligence, Berlin, 2020, pp. 159–180.
HSBI-PUB | DOI
 
[5]
2020 | Konferenzbeitrag | FH-PUB-ID: 1367
T. Voigt, M. Kohlhase, and O. Nelles, “Incremental Latin Hypercube Additive Design for LOLIMOT,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 2020, pp. 1602–1609.
HSBI-PUB | DOI
 
[4]
2020 | Artikel | FH-PUB-ID: 1368
T. Voigt, M. Kohlhase, and A. Peter, “Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels,” atp magazin, vol. 62, no. 04, pp. 62–69, 2020.
HSBI-PUB
 
[3]
2019 | Konferenzbeitrag | FH-PUB-ID: 1371 | OA
T. Voigt, M. Kohlhase, and O. Nelles, “Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 2019, pp. 267–288.
HSBI-PUB | DOI | Download (ext.)
 
[2]
2019 | Konferenzbeitrag | FH-PUB-ID: 1559 | OA
S. Godt and M. Kohlhase, “Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 2019, pp. 51–72.
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[1]
2018 | Konferenzbeitrag | FH-PUB-ID: 1369 | OA
T. Voigt and M. Kohlhase, “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, 2018, pp. 93–111.
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