Han Fan
I am PhD student working on SmokeBot project. I have a master degree of Science with major subject Engineering Physics from Chalmers University of Technology in Gothenburg, Sweden. Before I went to the graduate school, I had my undergraduate education in Automation at University of Electronic Science and Technology of China, in Chengdu, P.R. China.
I am interested in a variety of topics in Mobile Robotic Olfaction, including gas sensing, gas discrimination, gas dispersion and distribution modelling, as well as gas source localization. My current work is to address the problem of gas discrimination using e-nose in open environments where unknown interferents are present and environmental conditions are uncontrolled.
Contact
Han Fan
AASS Research Centre
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Phone +46 (0)19 30 30 00
han.fan@oru.se
Publications
Journal Articles
[1] | Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications. Frontiers in Chemistry, 10, 2022 [ BibTeX | DiVA | PDF ] |
[2] | FireNose on Mobile Robot in Harsh Environments. IEEE Sensors Journal, 19(24):12418-12431, 2019 [ BibTeX | DiVA ] |
[3] | Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose. Sensors, 19(3), 2019 [ BibTeX | DiVA | PDF ] |
[4] | A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments. Sensors and actuators. B, Chemical, 259:183-203, 2018 [ BibTeX | DiVA | PDF ] |
[5] | GADEN : A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments. Sensors, 17(7):1479-1494, 2017 [ BibTeX | DiVA | PDF ] |
[6] | Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots. IEEE Robotics and Automation Letters, 2(2):1117-1123, 2017 [ BibTeX | DiVA ] |
[7] | Time-dependent gas distribution modelling. Robotics and Autonomous Systems, 96:157-170, 2017 [ BibTeX | DiVA ] |
Refereed Conference and Workshop Articles
[1] | Identification of Gas Mixtures with Few Labels Using Graph Convolutional Networks. In 2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 2024 [ BibTeX | DiVA ] |
[2] | Learning From the Past : Sequential Deep Learning for Gas Distribution Mapping. In ROBOT2022 : Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2, 590(590):178-188, 2022 [ BibTeX | DiVA ] |
[3] | Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning. In 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : Proceedings 2022 [ BibTeX | DiVA ] |
[4] | Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers. In 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 2019 [ BibTeX | DiVA | PDF ] |
[5] | Improving Gas Dispersal Simulation For Mobile Robot Olfaction : Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop. In 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings 2017 [ BibTeX | DiVA ] |
[6] | Improving Gas Tomography With Mobile Robots : An Evaluation of Sensing Geometries in Complex Environments. In 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings 2017 [ BibTeX | DiVA ] |
[7] | Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments. In IEEE SENSORS 2017 : Conference Proceedings, pages 1691-1693, 2017 [ BibTeX | DiVA | PDF ] |
[8] | Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 131-136, 2016 [ BibTeX | DiVA ] |
[9] | Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks. In 2016 IEEE SENSORS 2016 [ BibTeX | DiVA ] |
Theses
[1] | Robot-aided Gas Sensing for Emergency Responses. Örebro University, School of Science and Technology, Ph.D. Thesis, 2022 [ BibTeX | DiVA ] |
Find the complete BibTeX record here.