Yuxuan Zhang
- Academic title: Affiliate Researcher
- Other title: Assistant Professor in Embedded Systems and IoT at Beijing University of Agriculture
- Telephone: +46 (0)10-1428004
- Email: yuxuan.zhang@miun.se
- Room number: Ej Angivet
- Location: Sundsvall
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- Employed at the subject:
- Electronics
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- Research centers:
- STC Research Centre
Background
Yuxuan Zhang received his PhD in Electronics from Mid Sweden University, Sundsvall, Sweden in 2025, MSc in Embedded Systems Engineering from University of Leeds, UK, BEng in New Energy Science & Engineering from Beijing Information Science & Technology University, Beijing, China.
He is now an Asst. Prof. in Embedded Systems and IoT at the College of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing, China, and an Affliated Researcher at the Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden.
Area of interest
Yuxuan's research interests include edge artificial intelligence and the IoT, as well as their applications in precision agriculture, structural health monitoring, and industrial maintenance.
Other information
ORCID: 0000-0002-8617-0435
Personal website: Yuxuan Zhang (张宇轩) - GitHub
Publications
Articles in journals
Conference papers
Doctoral theses, comprehensive summaries
Licentiate theses, comprehensive summaries
Manuscripts
Articles in journals
Lu, Y. , Zhang, Y. , Chen, M. , Liu, Q. , Wang, X. , Liu, Z. & Liu, H. (2025). Acoustic emission-based graph learning for internal valve leakage localisation in offshore pipelines. Nondestructive Testing and Evaluation,
Martinez Rau, L. , Zhang, Y. , Oelmann, B. & Bader, S. (2025). On-Device Anomaly Detection in Conveyor Belt Operations. IEEE Open Journal of Instrumentation and Measurement, vol. 4
Zhang, Y. , Pullin, R. , Oelmann, B. & Bader, S. (2025). On-Device Fault Diagnosis with Augmented Acoustic Emission Data : A Case Study on Carbon Fiber Panels. IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-12.
Li, G. , Chen, M. , Lu, Y. & Zhang, Y. (2025). Rolling bearing fault diagnosis in noisy environments using Channel-Time parallel attention networks. Scientific Reports, vol. 15: 1
Xie, Y. , Nie, Y. , Lundgren, J. , Yang, M. , Zhang, Y. & Chen, Z. (2024). Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images. Sensors, vol. 24: 11
Huang, C. , Sun, X. & Zhang, Y. (2024). Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring. Sensors, vol. 24: 13
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
Martinez Rau, L. S. , Zhang, Y. , Nguyen Phuong Vu, Q. , Oelmann, B. & Bader, S. (2025). An On-Device Hybrid Machine Learning Approach for Anomaly Detection in Conveyor Belt Operations. In 2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).
Nguyen Phuong Vu, Q. , Martinez Rau, L. S. , Zhang, Y. , Tran, N. D. , Oelmann, B. , Magno, M. & Bader, S. (2025). Efficient Continual Learning in Keyword Spotting using Binary Neural Networks. In 2025 IEEE Sensors Applications Symposium (SAS).. pp. 1--6.
Li, W. , Wu, X. , Hu, X. , Zhang, Y. , Bader, S. & Huang, Y. (2025). LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines. In 2025 IEEE Sensors Applications Symposium (SAS).. pp. 1--6.
Wang, X. , Li, H. , Liu, Z. , Zhang, J. , Zhang, Y. & Bader, S. (2025). Long short-term memory-optimized time difference mapping for enhanced acoustic emission source localization in composite materials. In 2025 IEEE Sensors Applications Symposium (SAS).. pp. 1--6.
Zhang, Y. , Xu, Y. , Martinez Rau, L. S. , Nguyen Phuong Vu, Q. , Oelmann, B. & Bader, S. (2025). On-Device Crack Segmentation for Edge Structural Health Monitoring. In 2025 IEEE Sensors Applications Symposium (SAS).. pp. 1--6.
Zhang, Y. , Martinez Rau, L. S. , Nguyen Phuong Vu, Q. , Oelmann, B. & Bader, S. (2025). Survey of Quantization Techniques for On-Device Vision-based Crack Detection. In 2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).
Muthumala, U. , Zhang, Y. , Martinez Rau, L. & Bader, S. (2024). Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis. In 2024 IEEE 10th World Forum on Internet of Things (WF-IoT).
Zhang, Y. , Martinez Rau, L. , Oelmann, B. & Bader, S. (2024). Enabling Autonomous Structural Inspections with Tiny Machine Learning on UAVs. In 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings.
Martinez Rau, L. , Zhang, Y. , Oelmann, B. & Bader, S. (2024). TinyML Anomaly Detection for Industrial Machines with Periodic Duty Cycles. In 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings.
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.
Doctoral theses
Zhang, Y. (2025). Efficient On-Device Intelligence for Structural Health Monitoring : A TinyML Perspective. Dis. (Comprehensive summary) Sundsvall : Mid Sweden University, 2025 (Mid Sweden University doctoral thesis : 434)
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
Zhang, Y. , Nürnberg, A. , Martinez Rau, L. , Nguyen Phuong Vu, Q. , Lu, Y. , Oelmann, B. & Bader, S. TinyML Pipeline for Efficient Crack Classification in UAV-based Structural Health Inspections.