This image shows Simon Kamm

Simon Kamm

M.Sc.

Academic staff
Institute of Industrial Automation and Software Engineering

Contact

+49 711 685 67293
+49 711 685 67302

Pfaffenwaldring 47
70550 Stuttgart
Germany
Room: 1.113

Journals and conferences:
  1. 2024

    1. C. Homs-Pons, R. Lautenschlager, L. Schmid, J. Ernst, D. Göddeke, O. Röhrle, and M. Schulte, “Coupled Simulation and Parameter Inversion for Neural System  and Electrophysiological Muscle Models,” GAMM-Mitteilungen, Mar. 2024.
    2. C. Homs-Pons, R. Lautenschlager, L. Schmid, J. Ernst, D. Göddeke, O. Röhrle, and M. Schulte, “Coupled simulations and parameter inversion for neural system and electrophysiological muscle models,” GAMM-Mitteilungen, p. e202370009, 2024.
    3. Y. Villota-Narvaez, C. Bleiler, and O. Röhrle, “Data sharing in modeling and simulation of biomechanical systems in interdisciplinary environments,” GAMM-Mitteilungen, p. e202370006, 2024.
    4. F. Ates and O. Röhrle, “Experiments meet simulations: Understanding skeletal muscle mechanics to address clinical problems,” GAMM-Mitteilungen, p. e202370012, 2024.
    5. C. Homs Pons and R. Lautenschlager, “Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models.” 2024.
    6. R. Thierer, B. Oesterle, E. Ramm, and M. Bischoff, “Transverse shear parametrization in hierarchic large rotation shell formulations,” International Journal for Numerical Methods in Engineering, vol. 125, no. 9, 2024.
  2. 2023

    1. A. Kalu-Uka, C. Ozoegwu, and P. Eberhard, “3D FEM Simulation of Titanium Alloy (Ti6Al4V) Machining with Harmonic Endmill Tools,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, vol. 22, 1, p. e202200111.
    2. S. R. Eugster and J. Harsch, “A family of total Lagrangian Petrov–Galerkin Cosserat rod finite element formulations,” GAMM-Mitteilungen, vol. 46, no. 2, p. e202300008, 2023.
    3. W. Ehlers, “A historical review on porous-media research,” in 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Dresden, 2023, no. 23, 3, p. e202300271.
    4. M. Nazish, C. M. Legendre, Y. Ding, B. Schluschaß, B. Schwederski, R. Herbst-Irmer, P. Parvathy, P. Parameswaran, D. Stalke, W. Kaim, and H. W. Roesky, “A Neutral Borylene and its Conversion to a Radical by Selective Hydrogen Transfer,” Inorganic chemistry, vol. 62, no. 24, pp. 9343–9349, 2023.
    5. S. Kamm, P. Suthandhira, N. Jazdi, and M. Weyrich, “A Novel Architecture for Robust and Adaptive Machine Learning Using Heterogeneous Data in Condition Monitoring of Automation Systems,” 2023, pp. 1–8.
    6. A. Schwarz, J. Keim, S. Chiocchetti, and A. Beck, “A Reinforcement Learning Based Slope Limiter for Second-Order Finite Volume Schemes,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, no. 23, 1, p. e202200207.
    7. S. Kamm, S. S. Veekati, T. Müller, N. Jazdi, and M. Weyrich, “A survey on machine learning based analysis of heterogeneous data in industrial automation,” Computers in Industry, vol. 149, Aug. 2023.
    8. S. Kamm, S. S. Veekati, T. Müller, N. Jazdi-Motlagh, and M. Weyrich, “A survey on machine learning based analysis of heterogeneous data in industrial automation,” Computers in industry, vol. 149, no. C, p. 103930, 2023.
    9. J.-S. Völter, T. Ricken, and O. Röhrle, “About the applicability of the theory of porous media for the modelling of non-isothermal material injection into porous structures,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, no. 23, 1, p. e202200070.
    10. J.-S. L. Völter, T. Ricken, and O. Röhrle, “About the applicability of the theory of porous media for the modelling of non-isothermal material injection into porous structures,” PAMM, vol. 23, no. 1, p. e202200070, 2023.
    11. S. Kamm, P. Kumar, N. Jazdi, and M. Weyrich, “An Architecture for Adaptive Machine Learning Models using Adversarial and Transfer Learning,” Procedia CIRP, vol. 120, pp. 1451–1456, Jan. 2023.
    12. D. Pfeifer, J. Scheid, J. Kneifl, and J. C. Fehr, “An improved development process of production plants using digital twins with extended dynamic behaviour in virtual commissioning and control : Simulation@Operations,” in 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Dresden, 2023, no. 23, 3, p. e202300225.
    13. Z. M. Trivedi, D. Gehweiler, J. K. Wychowaniec, T. Ricken, B. Gueorguiev-Rüegg, A. Wagner, and O. Röhrle, “Analysing the bone cement flow in the injection apparatus during vertebroplasty,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, no. 23, 1, p. e202200295.
    14. Z. Trivedi, D. Gehweiler, J. K. Wychowaniec, T. Ricken, B. Gueorguiev, A. Wagner, and O. Röhrle, “Analysing the bone cement flow in the injection apparatus during vertebroplasty,” Proceedings in Applied Mathematics and Mechanics, vol. 23, no. 1, May 2023.
    15. D. Krach and H. Steeb, “Comparing methods for permeability computation of porous materials and their limitations,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, no. 23, 1, p. e202200225.
    16. L. Chen, G. Casas, C. Kasirer, and T. Van Mele, “COMPAS: Softwarelösung für komplexe Bauprojekte,” Bautechnik, vol. 100, no. 8, pp. 476--482, Aug. 2023.
    17. B. Hahn and B. Wirth, “Convex reconstruction of moving particles with inexact motion model,” PAMM, vol. 23, no. 2, Sep. 2023.
    18. R. R. Jones, C. Miksch, H. Kwon, C. Pothoven, K. R. Rusimova, M. Kamp, K. Gong, L. Zhang, T. Batten, B. Smith, A. V. Silhanek, P. Fischer, D. Wolverson, and V. K. Valev, “Dense Arrays of Nanohelices : Raman Scattering from Achiral Molecules Reveals the Near-Field Enhancements at Chiral Metasurfaces,” Advanced materials, vol. 35, no. 34, p. 2209282, 2023.
    19. S. Amann, T. Haist, A. Gatto, M. Kamm, and A. Herkommer, “Design and realization of a miniaturized high resolution computed tomography imaging spectrometer,” Journal of the European Optical Society, vol. 19, no. 2, p. 34, 2023.
    20. A. Vetter and U. Remer, “Dialogischer Bürgerinnen- und Bürgerbeteiligung in Baden-Württemberg : Eine Einleitung,” in Dialogische Bürgerinnen- und Bürgerbeteiligung in Baden-Württemberg, 1st ed., A. Vetter and U. Remer, Eds. Wiesbaden: Springer VS, 2023, pp. 13–33.
    21. J. Lißner and F. Fritzen, “Double U‐Net: Improved multiscale modeling via fully convolutional neural networks,” Proceedings in Applied Mathematics & Mechanics (PAMM), Sep. 2023.
    22. N. Fahse, M. Millard, F. Kempter, S. Maier, M. Roller, and J. C. Fehr, “Dynamic human body models in vehicle safety : an overview,” GAMM-Mitteilungen, vol. 46, no. 2, p. e202300007, 2023.
    23. S. Wagner, “Entwurf und Aufbau einer Demonstrator-Leiterplatte für einen integrierten Arbiträrsignalgenerator,” Forschungsarbeit. 2023.
    24. A. Humbert, D. Gross, R. Sondershaus, R. Müller, H. Steeb, M. Braun, J. Brauchle, K. Stebner, and M. Rückamp, “Fractures in glaciers—Crack tips and their stress fields by observation and modeling,” PAMM, Nov. 2023.
    25. H. R. Madadi Varzaneh and H. Steeb, “High-speed fatigue testing of high-performance concretes and parallel frequency sweep characterization,” in 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Dresden, 2023, no. 23, 3, p. e202300262.
    26. H. Madadi and H. Steeb, “High‐speed fatigue testing of high‐performance concretes and parallel frequency sweep characterization,” PAMM, Nov. 2023.
    27. V. Afanasenko, K. Sharma, S. Kamm, and I. Kallfass, “Hybrid Model of Power MOSFET for Soft Failures Estimation Based on Time Domain Reflectometry and Machine Learning,” in 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023-ECCE Asia), 2023, pp. 1849--1854.
    28. A. Wagner, A. Sonntag, S. Reuschen, W. Nowak, and W. Ehlers, “Hydraulically induced fracturing in heterogeneous porous media using a TPM-phase-field model and geostatistics,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, no. 23, 1, p. e202200118.
    29. A. Wagner, A. Sonntag, S. Reuschen, W. Nowak, and W. Ehlers, “Hydraulically induced fracturing in heterogeneous porous media using a TPM-phase-field model and geostatistics,” Proceedings in Applied Mathematics and Mechanics, vol. 23, p. e202200118, 2023.
    30. J. Stümpfle, N. Sahlab, S. Kamm, P. Grimmeisen, N. Jazdi, and M. Weyrich, “InteLiv: An Architecture for Graph-Based Dynamic Context Modeling for Smart Living,” 2023, pp. 1–8.
    31. S. Amann, T. Haist, A. Gatto, M. Kamm, and A. Herkommer, “Intermediate image free computed tomography imaging spectrometer,” in Photonic Instrumentation Engineering X, San Francisco, CA, 2023, no. 12428, p. 124280G.
    32. F. Matter, I. Iroz, and P. Eberhard, “Interpolation-based parametric model order reduction of automotive brake systems for frequency-domain analyses,” GAMM-Mitteilungen, vol. 46, no. 1, p. e202300002, 2023.
    33. M. Hillebrand, J. Müller, J. Ullah, and T. Lutz, “Investigation of a Realistic Flap Modeling Using a Combination of Chimera Method and Grid Deformation on a Wing Fuselage Configuration,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, vol. 22, 1, p. e202200101.
    34. M. Omlor, E. N. Reinheimer, T. Butzmann, and K. Dilger, “Investigations on the formation of pores during laser beam welding of hairpin windings using a high-speed x-ray imaging system,” Journal of laser applications, vol. 35, no. 3, p. 032010, 2023.
    35. F. Matter, I. Iroz, and P. Eberhard, “Methods of Model Order Reduction for Coupled Systems Applied to a Brake Disc-Wheel Composite,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, vol. 22, 1, p. e202200323.
    36. N. Hornischer, “Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations,” GAMM Archive for Students, 2023.
    37. J. Harsch and S. R. Eugster, “Nonunit quaternion parametrization of a Petrov--Galerkin Cosserat rod finite element,” PAMM, p. e202300172, 2023.
    38. H. Ebel, M. Rosenfelder, and P. Eberhard, “Note on the Predictive Control of Non-Holonomic Systems and Underactuated Vehicles in the Presence of Drift,” Proceedings in Applied Mathematics and Mechanics, vol. 23, no. 4, p. e202300022, 2023.
    39. M. Suditsch, T. Ricken, and A. Wagner, “Patient-specific simulation of brain tumour growth and regression,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, no. 23, 1, p. e202200213.
    40. V. Kamm, P. Mesmer, A. Lechler, and A. Verl, “Prozessmodellierung für das Rührreibschweißen/Process modeling of friction stir welding – Semi-analytical process model of robotic friction stir welding,” wt Werkstattstechnik online, vol. 113, no. 05, pp. 183–188, 2023.
    41. J. Adamek, J. Cavalier, D. Tskhakaya, B. Csillag, L. Cinnirella, J. Lips, D. Lopez-Rodriguez, D. Sosa, D. Medina, P. Vondracek, L. Kripner, M. Komm, M. Sos, H. Lindl, and COMPASS Team, “Temporal characteristics of ELMs on the COMPASS divertor,” Nuclear fusion, vol. 63, no. 8, p. 086009, 2023.
    42. H. R. Madadi Varzaneh and H. Steeb, “The high cycle fatigue testing of High-Performance Concretes using high frequency excitation,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, vol. 22, 1, p. e202200221.
    43. E. Hartlieb, P. Ziegler, and P. Eberhard, “Vibration analysis on newly designed painting supports for the Cranach exhibition 2022 at Herzogin Anna Amalia Bibliothek,” in 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Aachen, 2023, vol. 22, 1, p. e202200324.
  3. 2022

    1. S. Kamm, N. Sahlab, N. Jazdi, and M. Weyrich, “A Concept for Dynamic and Robust Machine Learning with Contex Modeling for Heterogeneous Manufacturing Data,” 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME ‘22, Italy, 2022.
    2. S. Goudarzi, S. A. Soleymani, M. H. Anisi, M. A. Azgomi, Z. Movahedi, N. Kama, H. M. Rusli, and M. K. Khan, “A privacy-preserving authentication scheme based on Elliptic Curve Cryptography and using Quotient Filter in fog-enabled VANET.,” Ad Hoc Networks, vol. 128, p. 102782, 2022.
    3. D. Guo, K. Nakhleh, I.-H. Hou, S. Kompella, and C. Kam, “A Theory of Second-Order Wireless Network Optimization and Its Application on AoI.,” CoRR, vol. abs/2201.06486, 2022.
    4. E. Eiben, R. Ganian, T. Hamm, L. Jaffke, and O. joung Kwon, “A Unifying Framework for Characterizing and Computing Width Measures.,” in ITCS, 2022, vol. 215, pp. 63:1-63:23.
    5. R. Ganian, T. Hamm, and T. Talvitie, “An efficient algorithm for counting Markov equivalent DAGs.,” Artif. Intell., vol. 304, p. 103648, 2022.
    6. T. Müller, S. Kamm, A. Löcklin, D. White, M. Mellinger, N. Jazdi, and M. Weyrich, “Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems,” International Journal of Computer Integrated Manufacturing, September, 2022, 2022.
    7. T. Müller, S. Kamm, A. Löcklin, D. White, M. Mellinger, N. Jazdi-Motlagh, and M. Weyrich, “Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems,” International journal of computer integrated manufacturing, 2022.
    8. H. Saeidi, J. D. Opfermann, M. Kam, S. Wei, S. Léonard, M. H. Hsieh, J. U. Kang, and A. Krieger, “Autonomous robotic laparoscopic surgery for intestinal anastomosis.,” Sci. Robotics, vol. 7, no. 62, 2022.
    9. C. Grussler, T. Damm, and R. Sepulchre, “Balanced Truncation of $k$-Positive Systems.,” IEEE Trans. Autom. Control., vol. 67, no. 1, pp. 526–531, 2022.
    10. K. Sharma, S. Kamm, K. M. Baron, and I. Kallfass, “Characterization of Online Junction Temperature of the SiC power MOSFET by Combination of Four TSEPs using Neural Network,” in 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), Hannover, Germany, September, 2022, Hannover, Germany, 2022.
    11. S. Thiele, A. von Kamp, P. S. Bekiaris, P. Schneider, and S. Klamt, “CNApy: a CellNetAnalyzer GUI in Python for analyzing and designing metabolic networks.,” Bioinform., vol. 38, no. 5, pp. 1467–1469, 2022.
    12. L. Klingel, V. Kamm, and A. Verl, “Comparison and Application of Multi-Rate Methods for Real-Time Simulations of Production Systems,” in Proceedings of the 63rd International Conference of Scandinavian Simulation Society, 2022.
    13. T. Müller, N. Sahlab, S. Kamm, C. Köhler, D. Braun, N. Jazdi-Motlagh, and M. Weyrich, “Context-enriched modeling using Knowledge Graphs for intelligent Digital  Twins of Production Systems,” in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, 2022.
    14. T. Müller, N. Sahlab, S. Kamm, D. Braun, C. Köhler, N. Jazdi, and M. Weyrich, “Context-enriched modeling using Knowledge Graphs for intelligent Digital Twins of Production Systems,” in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September, 2022, 2022.
    15. V. Filippou, M. Bubrin, A. Grupp, M. R. Ringenberg, and W. Kaim, “Coordinative diversity in mononuclear CuI and CuII complexes of O,N,S-ambidentate camphoriminoquinone ligands,” Inorganica chimica acta, vol. 542, p. 121081, 2022.
    16. S. Amann, T. Haist, A. Gatto, M. Kamm, and A. Herkommer, “Design and realization of a miniaturized high resolution computed tomography imaging spectrometer,” EPJ Web of Conferences, vol. 266, p. 02001, 2022.
  4. 2016

    1. I. Amm, M. Kawan, and D. H. Wolf, “Characterization of protein quality control components via dual    reporter-containing misfolded cytosolic model substrates,” ANALYTICAL BIOCHEMISTRY, vol. 515, pp. 14–21.
    2. I. Amm and D. H. Wolf, “Molecular mass as a determinant for nuclear San1-dependent targeting of    misfolded cytosolic proteins to proteasomal degradation,” FEBS LETTERS, vol. 590, no. 12, pp. 1765–1775.
  5. 2022

    1. P. Benner, M. Burger, D. Göddeke, C. Görgen, C. Himpe, J. Heiland, T. Koprucki, M. Ohlberger, S. Rave, M. Reiselbach, J. Saak, A. Schöbel, K. Tabelow, and M. Weber, “Die mathematische Forschungsdateninitiative in der NFDI : MaRDI (Mathematical Research Data Initiative),” GAMM Rundbrief, vol. 2022, no. 1, pp. 40–43, 2022.
    2. S. Dhara, Mohd. A. Ansari, B. Schwederski, V. Filippou, W. Kaim, and G. K. Lahiri, “Diosmium compounds containing bis(imidazole)-p-quinone bridging ligands,” Dalton Transactions, vol. 51, no. 10, pp. 4058–4067, 2022.
    3. M. Nazish, Y. Ding, C. M. Legendre, A. Kumar, N. Graw, B. Schwederski, R. Herbst-Irmer, P. Parvathy, P. Parameswaran, D. Stalke, W. Kaim, and H. W. Roesky, “Excellent yield of a variety of silicon-boron radicals and their reactivity,” Dalton transactions, vol. 51, no. 29, pp. 11040–11047, 2022.
    4. M. Hanzel, “Existenzialistische Kriegsauffassung – Portrait einer vergessenen Konzeption des Krieges,” Die Friedens-Warte. Journal of International Peace and Organization, vol. 95, no. 3–4, pp. 384–401, 2022.
    5. L. S. Ho, T. Zhang, T. C. T. Kwok, K. P. Wat, F. T. T. Lai, and S. Li, “Financing Orphan Drugs Through a Blockchain-Supported Insurance Model.,” Frontiers Blockchain, vol. 5, p. 818807, 2022.
    6. D. Keküllüoglu, W. Magdy, and K. Vaniea, “From an Authentication Question to a Public Social Event: Characterizing Birthday Sharing on Twitter.,” CoRR, vol. abs/2201.10655, 2022.
    7. S. Rangaraj, M. Islam, V. VS, N. Wijethilake, U. Uppal, A. A. Q. See, J. Chan, M. L. James, N. K. K. King, and H. Ren, “Identifying risk factors of intracerebral hemorrhage stability using explainable attention model.,” Medical Biol. Eng. Comput., vol. 60, no. 2, pp. 337–348, 2022.
    8. L.-J. Kao, C.-C. Chiu, Y.-F. Lin, and H. K. Weng, “Inter-Purchase Time Prediction Based on Deep Learning.,” Comput. Syst. Sci. Eng., vol. 42, no. 2, pp. 493–508, 2022.
    9. T. Hamm, “Intrinsic dimension adaptive learning rates for kernel methods,” Dissertation, Universität Stuttgart, Stuttgart, 2022.
    10. T. Hamm and I. Steinwart, “Intrinsic Dimension Adaptive Partitioning for Kernel Methods,” SIAM journal on mathematics of data science, vol. 4, no. 2, pp. 721–749, 2022.
    11. F. Kramm, F. Ullwer, B. Klinnert, M. Zheng, and B. Plietker, “Iron-Catalyzed Cycloisomerization and C-C Bond Activation to Access Non-canonical Tricyclic Cyclobutanes,” Angewandte Chemie. International edition, vol. 61, no. 38, p. e202205169, 2022.
    12. Q. Wang, Z. Quan, S. Bi, and P.-Y. Kam, “Joint ML/MAP Estimation of the Frequency and Phase of a Single Sinusoid With Wiener Carrier Phase Noise.,” IEEE Trans. Signal Process., vol. 70, pp. 337–350, 2022.
    13. R. C. Calleja, A. Celletti, and R. de la Llave, “KAM quasi-periodic solutions for the dissipative standard map.,” Commun. Nonlinear Sci. Numer. Simul., vol. 106, p. 106111, 2022.
    14. R. C. Calleja, A. Celletti, J. Gimeno, and R. de la Llave, “KAM quasi-periodic tori for the dissipative spin-orbit problem.,” Commun. Nonlinear Sci. Numer. Simul., vol. 106, p. 106099, 2022.
    15. U. Haberlandt, S. Krämer, A. Bardossy, A. Bartens, P. Birkholz, M. Eisele, L. Fuchs, O.-C. Herrmann, A. Kuchenbecker, S. Maßmann, R. Pidoto, T. Müller, J. Seidel, and K. Sympher, “Kontinuierliche synthetische Niederschläge für stadthydrologische Bemessungen in Deutschland,” Hydrologie und Wasserbewirtschaftung, vol. 66, no. 3, pp. 106–121, 2022.
    16. R. Thoppilan et al., “LaMDA: Language Models for Dialog Applications.,” CoRR, vol. abs/2201.08239, 2022.
    17. S. Kamo and Y. Sheng, “Layerwise Geo-Distributed Computing between Cloud and IoT.,” CoRR, vol. abs/2201.07215, 2022.
    18. H. Sato, T. Ochiai, M. Delcroix, K. Kinoshita, N. Kamo, and T. Moriya, “Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition.,” CoRR, vol. abs/2201.03881, 2022.
    19. Y. Shibuya, A. Hamm, and T. C. Pargman, “Mapping HCI research methods for studying social media interaction: A systematic literature review.,” Comput. Hum. Behav., vol. 129, p. 107131, 2022.
    20. J. Gade, E. Ramm, K.-E. Kurrer, and M. Bischoff, “Marc Biguenets Beitrag zur Berechnung der Seilnetztragwerke für die Olympischen Spiele 1972,” Stahlbau, vol. 91, no. 9, pp. 612–621, 2022.
    21. S. Kamo and Y. Sheng, “Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet.,” CoRR, vol. abs/2201.03027, 2022.
    22. M. Ruf, K. Taghizadeh Bajgirani, and H. Steeb, “micro-XRCT data sets and in situ measured ultrasonic wave propagation of a pre-stressed monodisperse rubber and glass particle mixture with 30% volume rubber content.” 2022.
    23. G.-B. Wu, K. F. Chan, K. M. Shum, and C. H. Chan, “Millimeter-Wave and Terahertz OAM Discrete-Lens Antennas for 5G and Beyond.,” IEEE Commun. Mag., vol. 60, no. 1, pp. 34–39, 2022.
    24. M. Ruf, K. Taghizadeh, and H. Steeb, “Multi-scale characterization of granular media by~in~situ~laboratory X-ray computed tomography,” GAMM-Mitteilungen, vol. 45, no. 3–4, p. e202200011, 2022.
    25. K. Schroeder, “Polizei behandelt in Ausbildung kaum Rassismus,” Stuttgarter Zeitung, p. 6, Aug. 2022.
    26. R. Kaushik and K. Y. J. Zhang, “ProFitFun: a protein tertiary structure fitness function for quantifying the accuracies of model structures.,” Bioinform., vol. 38, no. 2, pp. 369–376, 2022.
    27. U. Stroth et al., “Progress from ASDEX Upgrade experiments in preparing the physics basis of ITER operation and DEMO scenario development,” Nuclear fusion, vol. 62, no. 4, p. 042006, 2022.
    28. C. Schiller, M. Freitag, A. Leiden, C. Herrmann, A. Gorovoj, P. Hering, and S. Hering, “Reference Model for Product-Service Systems with an Use Case from the Plating Industry,” in Advances in Production Management Systems. Smart Manufacturing and Logistics Systems : Turning Ideas into Action, Gyeongju, South Korea, 2022, vol. 1, no. 663, pp. 335–342.
    29. K. C. Li and B. T.-M. Wong, “Research landscape of smart education: a bibliometric analysis.,” Interact. Technol. Smart Educ., vol. 19, no. 1, pp. 3–19, 2022.
    30. R. Jordan, M. Niazi, S. Schaefer, W. Kaim, and A. Klein, “Rhenium Tricarbonyl Complexes of Azodicarboxylate Ligands,” Molecules, vol. 27, no. 23, p. 8159, 2022.
    31. S. Thombre, Z. Zhao, H. Ramm-Schmidt, J. M. V. Garcia, T. Malkamäki, S. Nikolskiy, T. Hammarberg, H. Nuortie, M. Z. H. Bhuiyan, S. Särkkä, and V. V. Lehtola, “Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review.,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 1, pp. 64–83, 2022.
    32. S. Kamm, S. Bickelhaupt, K. Sharma, N. Jazdi-Motlagh, I. Kallfass, and M. Weyrich, “Simulation-to-Reality based Transfer Learning for the Failure Analysis  of SiC Power Transistors,” in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, 2022.
    33. S. Kamm, S. Bickelhaupt, K. Sharma, N. Jazdi, I. Kallfass, and M. Weyrich, “Simulation-to-Reality based Transfer Learning for the Failure Analysis of SiC Power Transistors,” in 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany, September 2022, 2022.

Research focus: Analyzing heterogeneous data using AI methods for robust decisions

Description: The amount of recordable data continues to increase. As a result, the variety and thus the heterogeneity of the data are also increasing more and more. This brings new challenges for data management and data analysis of this heterogeneous data. Data management should provide the data in a uniform, machine-readable format as best as possible in order to make the data centrally accessible to the user and downstream analysis models. Moreover, classical machine learning models typically use only data of one form (e.g., image data) for data analysis. 

To take advantage of the variety of data, new machine learning models shall be explored and applied to make robust decisions on this database. For this purpose, existing knowledge (e.g. in the form of simulation models) shall be used to improve the learning behavior of the models. Furthermore, methods are to be investigated with which the data for these analysis models can be provided centrally while preserving the complex relations.

Research portal: ResearchGate Simon Kamm 

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