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Leak Detection and Quantification

See PI YangQuan Chen's work developing methane quantification strategies utilizing machine learing based big data and Digital Twins. For more information on this task see the Mission page.

Selected Publications

  1. Hollenbeck, D., Zulevic, D., & Chen, Y. (2021). Advanced leak detection and quantification of methane emissions using suas. Drones, 5(4), 117.

  2. Hollenbeck, D., Zulevic, D., & Chen, Y. (2022, June). A modified near-field gaussian plume inversion method using multi-sUAS for emission quantification. In 2022 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1620-1625). IEEE.

  3. Hollenbeck, D., Zulevicl, D., & Chen, Y. (2022, November). Single and Multi-sUAS Based Emission Quantification Performance Assessment Using MOABS/DT: A Simulation Case Study. In 2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) (pp. 1-5). IEEE.

  4. Hollenbeck, D., & Chen, Y. (2020, September). Characterization of ground-to-air emissions with sUAS using a digital twin framework. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1162-1166). IEEE.