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Xue Zhang

Contact

  +49221916520

  XZhang@Dompatent.de

   Linkedin

M.Eng. Osaka University (Electrical, Electronic and Information Engineering) | M.Sc. Aston University (Photonic Network Engineering) | B.Eng. University of Birmingham (Electronic and Electrical Engineering) | B.Eng. HUST (Electronic Engineering)
Trainee Patent Attorney | With dompatent since 2021

Languages

Chinese, English, German

Technical Expertise

Machine Learning and Artificial Intelligence, Photonic Networks Engineering, Electrical, Electronic and Computer Engineering, Mechanic System Dynamics, Telecommunication, big data

Professional Background

Bachelor degree in Electronics from Huazhong University of Science and Technology, China; Bachelor degree in Electronic and Electrical Engineering from University of Birmingham, UK; Masters degree in Photonic Networks Engineering from Aston University, UK; Masters degree in Electrical, Electronic and Information Engineering from Osaka University, Japan; Software testing engineer of mobile broadband devices and smartphone at Huawei Technologies Germany; Early Stage Researcher of Marie Curie Horizon 2020 European Commission Program on Machine Learning and Artificial Intelligence at Austrian Institute of Technology in Vienna; PhD student in Mechanical Engineering at KU Leuven, Belgium; Trainee European Patent Attorney.

Publications

Journal articles:

  • Zhang, X., Kuehnelt, H., De Roeck, W. with Zhang, X. (corresp. author) (2021). Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network. Applied Sciences-Basel, 11, Art.No. 2714. doi: 10.3390/app11062714 (professional oriented)

Book chapters:

  • Zhang, X., Kuehnelt, H., De Roeck, W. (2021). Pass-by Noise Modelling of Road Vehicles employing Machine Learning and Big Data. In: “PBNv2” Next generation Pass-By Noise approaches for new powertrain vehicles, Chapt. 7, (163-200). Leuven, Belgium: KU Leuven. ISBN: 9789082893120.

Conference proceedings:

  • Zhang, X., Kuehnelt, H., De Roeck, W. (2020). Pass-by Noise Modelling Applying Machine Learning. In: e-Forum Acusticum 2020, (2019-2026). Presented at the FORUM ACUSTICUM 2020, Lyon, FR.