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Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope
Deshpande, P., Pandiyan, V., Meylan, B., & Wasmer, K. (2021). Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope. Wear, 203622 (12 pp.). https://doi.org/10.1016/j.wear.2021.203622
Acoustic emission and machine learning for in situ monitoring of a gold-copper ore weakening by electric pulse
Meylan, B., Shevchik, S. A., Parvaz, D., Mosaddeghi, A., Simov, V., & Wasmer, K. (2021). Acoustic emission and machine learning for in situ monitoring of a gold-copper ore weakening by electric pulse. Journal of Cleaner Production, 280, 124348 (12 pp.). https://doi.org/10.1016/j.jclepro.2020.124348
Applications of reinforcement learning in energy systems
Perera, A. T. D., & Kamalaruban, P. (2021). Applications of reinforcement learning in energy systems. Renewable and Sustainable Energy Reviews, 137, 110618 (22 pp.). https://doi.org/10.1016/j.rser.2020.110618
A data acquisition setup for data driven acoustic design
Rust, R., Xydis, A., Heutschi, K., Perraudin, N., Casas, G., Du, C., … Kohler, M. (2021). A data acquisition setup for data driven acoustic design. Building Acoustics. https://doi.org/10.1177/1351010X20986901
Machine learning monitoring for laser osteotomy
Shevchik, S., Nguendon Kenhagho, H., Le-Quang, T., Faivre, N., Meylan, B., Guzman, R., … Wasmer, K. (2021). Machine learning monitoring for laser osteotomy. Journal of Biophotonics, 14(4), e202000352 (11 pp.). https://doi.org/10.1002/jbio.202000352
A machine learning-based surrogate model to approximate optimal building retrofit solutions
Thrampoulidis, E., Mavromatidis, G., Lucchi, A., & Orehounig, K. (2021). A machine learning-based surrogate model to approximate optimal building retrofit solutions. Applied Energy, 281, 116024 (20 pp.). https://doi.org/10.1016/j.apenergy.2020.116024
Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings
Bünning, F., Huber, B., Heer, P., Aboudonia, A., & Lygeros, J. (2020). Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings. Energy and Buildings, 211, 109792 (8 pp.). https://doi.org/10.1016/j.enbuild.2020.109792
Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review
Pandiyan, V., Shevchik, S., Wasmer, K., Castagne, S., & Tjahjowidodo, T. (2020). Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review. Journal of Manufacturing Processes, 57, 114-135. https://doi.org/10.1016/j.jmapro.2020.06.013
Introducing reinforcement learning to the energy system design process
Perera, A. T. D., Wickramasinghe, P. U., Nik, V. M., & Scartezzini, J. L. (2020). Introducing reinforcement learning to the energy system design process. Applied Energy, 262, 114580 (14 pp.). https://doi.org/10.1016/j.apenergy.2020.114580
Concurrent optimization of organic donor–acceptor pairs through machine learning
Padula, D., & Troisi, A. (2019). Concurrent optimization of organic donor–acceptor pairs through machine learning. Advanced Energy Materials, 9(40), 1902463 (8 pp.). https://doi.org/10.1002/aenm.201902463
Historical penetration patterns of automobile electronic control systems and implications for critical raw materials recycling
Restrepo, E., Løvik, A. N., Widmer, R., Wäger, P., & Müller, D. B. (2019). Historical penetration patterns of automobile electronic control systems and implications for critical raw materials recycling. Resources, 8(2), 58 (20 pp.). https://doi.org/10.3390/resources8020058
Laser welding quality monitoring via graph support vector machine with data adaptive kernel
Shevchik, S. A., Le-Quang, T., Farahani, F. V., Faivre, N., Meylan, B., Zanoli, S., & Wasmer, K. (2019). Laser welding quality monitoring via graph support vector machine with data adaptive kernel. IEEE Access, 7, 93108-93122. https://doi.org/10.1109/ACCESS.2019.2927661
In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach
Wasmer, K., Le-Quang, T., Meylan, B., & Shevchik, S. A. (2019). In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. Journal of Materials Engineering and Performance, 28(2), 666-672. https://doi.org/10.1007/s11665-018-3690-2
Acoustic emission for <i>in situ</i> monitoring of solid materials pre-weakening by electric discharge: a machine learning approach
Shevchik, S. A., Meylan, B., Mosaddeghi, A., & Wasmer, K. (2018). Acoustic emission for in situ monitoring of solid materials pre-weakening by electric discharge: a machine learning approach. IEEE Access, 6, 40313-40324. https://doi.org/10.1109/ACCESS.2018.2853666
High-speed X-ray imaging for correlating acoustic signals with quality monitoring: a machine learning approach
Wasmer, K. (2018). High-speed X-ray imaging for correlating acoustic signals with quality monitoring: a machine learning approach. In Contributed Papers from Materials Science & Technology 2018 (pp. 165-168).
Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach
Wasmer, K., Le-Quang, T., Meylan, B., Vakili-Farahani, F., Olbinado, M. P., Rack, A., & Shevchik, S. A. (2018). Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach. In M. Schmidt, F. Vollertsen, & G. Dearden (Eds.), Procedia CIRP: Vol. 74. 10th CIRP conference on photonic technologies [LANE 2018] (pp. 654-658). https://doi.org/10.1016/j.procir.2018.08.054
In situ quality monitoring in am using acoustic emission: a machine learning approach
Wasmer, K., Kenel, C., Leinenbach, C., & Shevchik, S. A. (2017). In situ quality monitoring in am using acoustic emission: a machine learning approach (pp. 386-388). Presented at the Materials science and technology (MS&T17). https://doi.org/10.7449/2017/MST_2017_386_388
Choice-based experiments in multiple dimensions
Scheller Lichtenauer, M., Zolliker, P., & Sprow, I. (2013). Choice-based experiments in multiple dimensions. Color Research and Application, 38(5), 334-343. https://doi.org/10.1002/col.21723