Thanh Tran

Doktorand|Doctoral Student

  • Professional title: Doctoral Student
  • Area of responsibility: Apply deep learning in digital signal and image processing, industrial sound measurements, and machine fault diagnosis.
  • Department: Department of Electronics Design (EKS)
  • Telephone: +46 (0)10-1428251
  • Email: thanh.tran@miun.se
  • Room number: S229
  • 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

LinkedIn

Google scholar

Publons

Publications

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. , 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.  

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)  

The page was updated 7/12/2022