Thanh Tran
- Area of responsibility: Apply deep learning in digital signal and image processing, industrial sound measurements, and machine fault diagnosis.
- Email: thanh.tran@miun.se
- Room number: Ej Angivet
- Location: Sundsvall
- Employee in the subject: Computer Science, Sound Production
Background
Thanh Tran received an M.S. degree in the Department of IT Convergence and Application Engineering, Pukyong National University, Busan, South Korea, in 2019. She is currently pursuing a Ph.D. degree in the Department of Electronics Design, STC Research Centre, Mid Sweden University. Her research interests include digital signal and image processing, industrial sound measurements, machine fault diagnosis, and deep learning. She was invited to review papers for journals: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Elsevier Innovation and Research in BioMedical engineering (IRBM), Jordanian Journal of Computers, and Information Technology, Springer Soft Computing, and Wiley International Journal for Numerical Methods in Biomedical Engineering.
Research
10 peer-reviewed publications in journals and conferences. The publications have been cited 163 times, of which the most cited publication accounts for 71 citations. The author has an h-index of 5 (Google Scholar).
Other information
Publications
Articles in journals
Conference papers
Doctoral theses, comprehensive summaries
Licentiate theses, comprehensive summaries
Articles in journals
Tran, T. , Truong Pham, N.
&
Lundgren, J. (2022). A deep learning approach for detecting drill bit failures from a small sound dataset. Scientific Reports, vol. 12
Tran, T.
&
Lundgren, J. (2020). Drill Fault Diagnosis Based on the Scalogram and Mel Spectrogram of Sound Signals Using Artificial Intelligence. IEEE Access, vol. 8, pp. 203655-203666.
Conference papers
Tran, T. , Bader, S.
&
Lundgren, J. (2023). Denoising Induction Motor Sounds Using an Autoencoder.
In
2023 IEEE Sensors Applications Symposium (SAS).. pp. 01--06.
Tran, T. , Bader, S.
&
Lundgren, J. (2022). An artificial neural network-based system for detecting machine failures using a tiny sound dataset : A case study.
In
Proceedings - 2022 IEEE International Symposium on Multimedia, ISM 2022.. pp. 163--168.
Tran, T. , Huy, K. B. , Pham, N. T. , Carratù, M. , Liguori, C.
&
Thim, J. (2021). Separate Sound into STFT Frames to Eliminate Sound Noise Frames in Sound Classification.
Paper presented at the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), Orlando, USA, [DIGITAL], December 5-7, 2021.
Doctoral theses
Tran, T. (2023). Enhancing Machine Failure Detection with Artificial Intelligence and sound Analysis. Dis. (Comprehensive summary) Sundsvall : Mid Sweden University, 2023 (Mid Sweden University doctoral thesis : 395)
Licentiate theses, comprehensive summaries
Tran, T. (2021). Drill Failure Detection based on Sound using Artificial Intelligence. Lic. (Comprehensive summary) Sundsvall, Sweden : Mid Sweden University, 2021 (Mid Sweden University licentiate thesis : 188)