Yuxuan Zhang

Doktorand|Doctoral Student

Tjänstetitel: Doktorand
Akademisk titel: Doktorand
Telefon arbete: +46 (0)10-1428004
E-postadress: yuxuan.zhang@miun.se
Rumsnummer: S220
Ort: Sundsvall

Anställd inom ämnet:

Elektronik

Forskningscentra:

Sensible Things that Communicate, STC

Avdelning:

Institutionen för data- och elektroteknik (DET)

Bakgrund

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.

Forskningsområden

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.

Övrigt

ORCID: 0000-0002-8617-0435

Publikationer

Artiklar i tidskrifter
Konferensbidrag
Licentiatavhandlingar, sammanläggningar

Artiklar i tidskrifter

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

Konferensbidrag

Adin, V. , Zhang, Y. , Oelmann, B. & Bader, S. (2023). Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals. I 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. I 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. I 2022 IEEE Sensors Applications Symposium, SAS 2022 - Proceedings.

Licentiatavhandlingar

Zhang, Y. (2024). Tiny Machine Learning for Structural Health Monitoring with Acoustic Emissions. Lic.-avh. (Sammanläggning) Sundsvall : Mid Sweden University, 2024 (Mid Sweden University licentiate thesis : 204)

Manuskript

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.