School of Electrical and Electronic Engineering
Faculty of Engineering, Computer and Mathematical Sciences
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
Hong Gunn is a Teaching Fellow with the School of Electrical and Electronic Engineering. He is the electronics practical coordinator and final year projects coordinator in the school. His research interest are in Machine Learning, Autonomous Systems, Cyber Security and Energy Management.
- My Research
- Grants and Funding
- Professional Activities
My research interest are in Machine Learning, Autonomous Systems, Cyber Security and Energy Management.
Date Position Institution name 2020 Lecturer University of Adelaide 2017 - 2019 Associate Lecturer University of Adelaide, Adelaide 2013 - 2017 Teaching Laboratories Manager The University of Adelaide 2012 - 2013 Casual Associate Lecturer (Practical Coordinator) University of Adelaide, Adelaide
Awards and Achievements
Date Type Title Institution Name Country Amount 2016 Teaching Award ECMS Faculty Prize for Excellence in Professional Support of Learning & Teaching University of Adelaide Australia $1000
Language Competency Chinese (Mandarin) Can speak and understand spoken English Can read, write, speak, understand spoken and peer review
Date Institution name Country Title 2013 University of Adelaide, Adelaide Australia Ph.D 1995 University of Tasmania, Hobart Australia B.E., B.Sc.
Year Citation 2019 Millar, K., Cheng, A., Chew, H., & Lim, C. (2019). Using convolutional neural networks for classifying malicious network traffic. In M. Alazab, & M. Tang (Eds.), Deep Learning Applications for Cyber Security (pp. 103-126). Cham, Switzerland: Springer Nature.
2018 Millar, K., Cheng, A., Chew, H., & Lim, C. (2018). Deep Learning for Classifying Malicious Network Traffic. In M. Ganji, L. Rashidi, B. Fung, & C. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining (Vol. 11154, Lecture Notes in Artificial Intelligence ed., pp. 156-161). Springer.
2005 Chew, H., Lim, C., & Bogner, R. (2005). An implementation of training dual-nu support vector machines. In L. Qi, K. Teo, & X. Yang (Eds.), Applied optimization - Optimization and control with applications (Vol. 96, pp. 157-182). New York, USA: Springer.
Year Citation 2020 Millar, K. A., Cheng, A., Chew, H. G., & Lim, C. C. (2020). Characterising Network-Connected Devices Using Affiliation Graphs. In IEEE/IFIP Network Operations and Management Symposium. Budapest, Hungary. 2018 Millar, K., Cheng, A., Chew, H., & Lim, C. (2018). Deep Learning for Classifying Malicious Network Traffic. In Trends and Applications in Knowledge Discovery and Data Mining Vol. 11154 (pp. 156-161). Switzerland: Springer.
2017 Millar, K., Smit, D., Page, C., Cheng, A., Chew, H., & Lim, C. (2017). Looking deeper: Using deep learning to identify internet communications traffic. In Macquarie Matrix: Special edition, ACUR 2017 Vol. 6.1 (pp. 1-21). online: Macquarie University. 2016 Young, B., Ertugrul, N., & Chew, H. (2016). Overview of optimal energy management for nanogrids (end-users with renewables and storage). In Proceedings of the 2016 Australasian Universities Power Engineering Conference, AUPEC 2016 (pp. 6 pages). Brisbane, AUSTRALIA: IEEE.
DOI Scopus2 WoS1
2004 Chew, H., Lim, C., & Bogner, R. (2004). Dual-nu support vector machines and applications in multi-class image recognition. In A. Rubinov, & M. Sniedovich (Eds.), Proceedings of the 6th International Conference on Optimization: Techniques and Applications 2004 (pp. CD-ROM 1-CD-ROM 11). CD-ROM: University of Ballarat. 2001 Chew, H., Lim, C., & Bogner, R. (2001). On initialising nu-Support Vector Machine Training. In D. Li (Ed.), Proceedings of the 5th International Conference on Optimization: Techniques and Applications (pp. 1740-1747). HONG KONG: ICOTA. 2001 Chew, H., Bogner, R., & Lim, C. (2001). Dual v-support vector machine with error rate and training size biasing. In V. John Matthews (Ed.), Proceedings of IEEE Signal Processing Society International Conference on Acoustics, Speech, and Signal Processing 2001 (pp. CDROM 1-CDROM 4). CD-ROM: IEEE SIGNAL PROCESSING SOCIETY.
2001 Chew, H., Bogner, R., & Lim, C. (2001). Dual ν-support vector machine with error rate and training size biasing. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2 (pp. 1269-1272). SALT LAKE CITY, UT: IEEE.
2001 Chew, H., Bogner, R., & Lim, C. (2001). Dual nu-support vector machine with error rate and training size biasing. In 2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS (pp. 4041). SALT LAKE CITY, UT: IEEE. 2000 Chew, H., Crisp, D., Bogner, R., & Lim, C. (2000). Target detection in radar imagery using support vector machines with training size biasing. In J. Wang (Ed.), Proceedings of ICARCV 2000 - Sixth International Conference on Control, Automation, Robotics and Vision (pp. CD). Singapore: School of Electrical & Electronic Engineering, NTU.
Year Citation 2013 Chew, H. G. (2013). Support Vector Machines with Dual Error Extensions for Target Detection and Object Recognition. (PhD Thesis, The University of Adelaide).
- “Eavesdropping and malicious/deceptive jammer detection using machine learning in wireless sensor networks”, South Korean Agency for Defense Development, $191k, August 2014 – August 2017, 0.10 FTE contribution.
- “Swarm Intelligence and Multi-Mission Coordination in Urban Areas”, Defence Science and Technology Group, $99.9k, March 2017 – August 2018, 0.05 FTE contribution.
- “Swarm Intelligence and Genetic Fuzzy Trees for Threat Avoidance and Target Allocation”, Defence Science and Technology Group, $99.9k, March 2017 – August 2018, 0.05 FTE contribution.
Practical coordinator of these courses:
|Signal and Systems||2012-2016|
|Electronic Circuits M||2016-|
|IV/PG||Distributed Generation Tech||2012-|
Course coordinator of Final Year Projects for Undergraduates (Honours) and Postgraduates (Masters)
|PG||Masters Project (SIP)||2013-2016|
|Masters Project (Electronic)||2013-|
|Masters Project (Electrical)||2013-|
|Masters Project (Engineering)||2020-|
Course coordinator and lecturer of these courses
Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2018 Co-Supervisor Classifying Network Connected Devices and Applications using Deep Learning Doctor of Philosophy Doctorate Full Time Mr Kyle Alexander Millar
Other Supervision Activities
Date Role Research Topic Location Program Supervision Type Student Load Student Name 2018 - ongoing Co-Supervisor Classifying Network Connected Devices and Applications using Deep Learning The University of Adelaide — Doctorate Full Time Kyle Millar
Date Role Committee Institution Country 2013 - ongoing Member School Advisory Committee University of Adelaide Australia
Date Role Membership Country 1999 - ongoing Member Institute of Electrical and Electronic Engineers United States
Date Office Name Institution Country 2002 - 2003 Treasurer IEEE - Computer Society SA chapter Australia
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