{"year":"2020","_id":"20808","publication_identifier":{"isbn":["9781728189567"]},"language":[{"iso":"eng"}],"status":"public","author":[{"first_name":"Hassan","id":"61186","full_name":"Ghasemzadeh Mohammadi, Hassan","last_name":"Ghasemzadeh Mohammadi"},{"first_name":"Rahil","full_name":"Arshad, Rahil","last_name":"Arshad"},{"last_name":"Rautmare","full_name":"Rautmare, Sneha","first_name":"Sneha"},{"first_name":"Suraj","full_name":"Manjunatha, Suraj","last_name":"Manjunatha"},{"first_name":"Maurice","full_name":"Kuschel, Maurice","last_name":"Kuschel"},{"first_name":"Felix Paul","last_name":"Jentzsch","full_name":"Jentzsch, Felix Paul"},{"first_name":"Marco","id":"398","full_name":"Platzner, Marco","last_name":"Platzner"},{"first_name":"Alexander","full_name":"Boschmann, Alexander","last_name":"Boschmann"},{"first_name":"Dirk","full_name":"Schollbach, Dirk","last_name":"Schollbach"}],"date_updated":"2023-07-09T13:08:07Z","publication_status":"published","date_created":"2020-12-21T10:03:49Z","title":"DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms","publication":"2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","type":"conference","user_id":"398","citation":{"apa":"Ghasemzadeh Mohammadi, H., Arshad, R., Rautmare, S., Manjunatha, S., Kuschel, M., Jentzsch, F. P., Platzner, M., Boschmann, A., & Schollbach, D. (2020). DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). https://doi.org/10.1109/etfa46521.2020.9211880","ieee":"H. Ghasemzadeh Mohammadi et al., “DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms,” 2020, doi: 10.1109/etfa46521.2020.9211880.","mla":"Ghasemzadeh Mohammadi, Hassan, et al. “DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms.” 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020, doi:10.1109/etfa46521.2020.9211880.","bibtex":"@inproceedings{Ghasemzadeh Mohammadi_Arshad_Rautmare_Manjunatha_Kuschel_Jentzsch_Platzner_Boschmann_Schollbach_2020, title={DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms}, DOI={10.1109/etfa46521.2020.9211880}, booktitle={2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, author={Ghasemzadeh Mohammadi, Hassan and Arshad, Rahil and Rautmare, Sneha and Manjunatha, Suraj and Kuschel, Maurice and Jentzsch, Felix Paul and Platzner, Marco and Boschmann, Alexander and Schollbach, Dirk}, year={2020} }","chicago":"Ghasemzadeh Mohammadi, Hassan, Rahil Arshad, Sneha Rautmare, Suraj Manjunatha, Maurice Kuschel, Felix Paul Jentzsch, Marco Platzner, Alexander Boschmann, and Dirk Schollbach. “DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms.” In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020. https://doi.org/10.1109/etfa46521.2020.9211880.","ama":"Ghasemzadeh Mohammadi H, Arshad R, Rautmare S, et al. DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). ; 2020. doi:10.1109/etfa46521.2020.9211880","short":"H. Ghasemzadeh Mohammadi, R. Arshad, S. Rautmare, S. Manjunatha, M. Kuschel, F.P. Jentzsch, M. Platzner, A. Boschmann, D. Schollbach, in: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020."},"project":[{"_id":"83","name":"SFB 901 - T1: SFB 901 -Subproject T1"},{"_id":"82","name":"SFB 901 - T: SFB 901 - Project Area T"},{"_id":"1","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","grant_number":"160364472"}],"doi":"10.1109/etfa46521.2020.9211880"}