Yuxuan Zhang
Doktorand|Doctoral Student
- Professional title: Doctoral Student
- Academic title: Doctoral Student
- Telephone: +46 (0)10-1428004
- Email: yuxuan.zhang@miun.se
- Room number: S220
- Location: Sundsvall
- Electronics
- STC Research Centre
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Employed at the subject:
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Research centers:
Background
Yuxuan Zhang completed his Master's Degree in Embedded Systems Engineering from the University of Leeds, Leeds, UK, in 2019. He joined Mid Sweden University in 2021 as a Doctoral Student.
Research
Yuxuan's current research focuses on the performance and optimization of deep learning/machine learning on low-power MCUs, with a particular focus on structural health monitoring applications.
Other information
ORCID: 0000-0002-8617-0435
Publications
Articles in journals
Conference papers
Licentiate theses, comprehensive summaries
Manuscripts
Articles in journals
Zhang, Y. , Adin, V. , Bader, S. & Oelmann, B. (2023). Leveraging Acoustic Emission and Machine Learning for Concrete Materials Damage Classification on Embedded Devices. IEEE Transactions on Instrumentation and Measurement, vol. 72
Conference papers
Adin, V. , Zhang, Y. , Oelmann, B. & Bader, S. (2023). Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals. In 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).
Adin, V. , Zhang, Y. , Ando, B. , Oelmann, B. & Bader, S. (2023). Tiny Machine Learning for Real-Time Postural Stability Analysis. In 2023 IEEE Sensors Applications Symposium (SAS).
Zhang, Y. , Bader, S. & Oelmann, B. (2022). A Lightweight Convolutional Neural Network Model for Concrete Damage Classification using Acoustic Emissions. In 2022 IEEE Sensors Applications Symposium, SAS 2022 - Proceedings.
Licentiate theses, comprehensive summaries
Zhang, Y. (2024). Tiny Machine Learning for Structural Health Monitoring with Acoustic Emissions. Lic. (Comprehensive summary) Sundsvall : Mid Sweden University, 2024 (Mid Sweden University licentiate thesis : 204)
Manuscripts
Muthumala, U. , Zhang, Y. , Martinez Rau, L. & Bader, S. Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis.
Zhang, Y. , Pullin, R. , Oelmann, B. & Bader, S. Data Augmentation of Acoustic Emission Signals for Real-time Fault Diagnosis based on Tiny Machine Learning.