About Byunggun

phone : (+1) 765-701-8802 email : bjoung@purdue.edu

Bio

I am a Ph.D. student at Purdue EEE, advised by Prof. Sutherland. I received my Bachelor’s degree in CSE at Korea University. and I earned my M.S. degree in EE at Korea University advised by Prof. Kim. My research is focused on Smart & Sustainable Manufacturing technologies enabled by data-driven techniques such as Artificial Intelligence (AI).

Education

B.S. Computer Communication Engineering, Korea University, 2016
M.S. Electrical Engineering, Korea University, 2016 - 2017 Advisor: Chulwoo Kim, Head of Department of Semiconductor-System Engineering
Ph.D. Environmental and Ecological Engineering, Purdue University, 2018 - present Advisor: John William Sutherland, the head of Environmental and Ecological Engineering

Publication

[1] ByungGun Joung, Y. Seo and C. Kim, “A Digital Low-Dropout (DLDO) regulator with -14 dB PSR enhancement technique,” IEEE SoC Design Conference (ISOCC), Oct 2016, pp. 353-354
[2] ByungGun Joung, Wo Jae Lee, Aihua Huang, John W. Sutherland, “Development and Application of a Method for Real Time Motor Fault Detection”, Procedia Manufacturing, Volume 49, 2020, pp. 94-98
[3] Dheeraj Peddireddy, Xingyu Fu, Haobo Wang, Byung Gun Joung, Vaneet Aggarwal, John W. Sutherland, Martin Byung-Guk Jun, “Deep Learning Based Approach for Identifying Conventional Machining Processes from CAD Data”, Procedia Manufacturing, Volume 48, 2020, pp. 915-925
[4] Dheeraj Peddireddy, Xingyu Fu, Anirudh Shankar, Haobo Wang, Byung Gun Joung, Vaneet Aggarwal, John W. Sutherland, Martin Byung-Guk Jun, “Identifying manufacturability and machining processes using deep 3D convolutional networks”, Journal of Manufacturing Processes, Volume 64, 2021, pp.1336-1348
[5] Byung Gun Joung, Zhongtian Li, John W. Sutherland, “Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management”, Manufacturing Science & Engineering Conference, 2022
[6] Wo Jae Lee, Byung Gun Joung, John W. Sutherland, “Environmental and Economic Performance of Different Maintenance Strategies for a Product Subject to Efficiency Erosion”, Journal of Cleaner Production, Volume 389, 2023,135340, 0959-6526, https://doi.org/10.1016/j.jclepro.2022.135340.
[7] Matthew J. Triebe; Sidi Deng; Jesús R. Pérez-Cardona; Byung Gun Joung; Haiyue Wu; Neha Shakelly; John P. Pieper; Xiaoyu Zhou; Thomas Maani; Fu Zhao; John W. Sutherland, “Perspectives on future research directions in green manufacturing for discrete products.”, Green Manufacturing Open. 2023; 1(2): 10. http://dx.doi.org/10.20517/gmo.2022.11
[8] Huang, A., Triebe, M., Li, Z., Wu, H., Joung, B.G. and Sutherland, J.W., 2022. A review of research on smart manufacturing in support of environmental sustainability. International Journal of Sustainable Manufacturing, 5(2-4), pp.132-163.
[9] Abdallah, Mustafa, Byung-Gun Joung, Wo Jae Lee, Charilaos Mousoulis, Nithin Raghunathan, Ali Shakouri, John W. Sutherland, and Saurabh Bagchi. “Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets.” Sensors 23, no. 1 (2023): 486.
[10] Byung-Gun Joung, Chandra Nath, Zhongtian Li, and John W. Sutherland. “ Bearing Anomaly Detection in an Air Compressor using an LSTM and RNN-Based Machine Learning Model.”, International Journal of Advanced Manufacturing Technology, (under review)

CV

Here is my CV