LDRD Seminar: March 12
Two Argonne researchers will discuss their Laboratory-Directed Research and Development (LDRD) sponsored work at the LDRD Seminar Series presentation Tuesday, March 12, 2019, at 12:30 p.m. in Building 212, Room A157. All are welcome to attend.
Visit the LDRD website to view upcoming seminars.
“Unmanned Aerial Systems for Environmental Characterization and Monitoring,” by Biophysical Scientist Yuki Hamada (EVS)
The Environmental Science, Decision & Infrastructure Sciences and Strategic Security Sciences divisions came together and explored the capability of unmanned aerial systems (UASs) for environmental characterization and monitoring. For this first effort, we aimed to characterize land surface and subsurface, and to detect land cover change using UAS imaging. We collected imagery using UAS-mounted RGB- and thermal-infrared cameras. With the help of domain experts such as archeologists and ecologists, we successfully extracted information from the imagery including the indication of subsurface artifacts, land cover change, vegetation types and road damage. During the project, we faced various challenges and learned valuable lessons with regards to improving the odds of a successful mission. In addition to a summary of our upcoming UAS activities, this talk will conclude with tips for how to develop UAS tasks effectively and efficiently for your future research and projects.
Yuki Hamada is a biophysical remote sensing scientist who uses remote sensing science and technologies to characterize environmental conditions for land and water and monitor their changes over space and time. Her research involves observing and modeling half-hourly carbon exchange in ecosystems using hyperspectral remote sensing and developing remote sensing methodologies for monitoring impacts on land and natural resources associated with energy development by integrating field observations, videography, and satellite and aerial images with statistical modeling and machine learning. She is the principal investigator on the EcoSpec Project and a member of the Spectral Network Board of Directors.
“Machine Learning for Mathematical Optimization: Using ML to Help Solve Large-Scale Security- Constrained Unit Commitment in Power Systems,” by Computational Scientist Feng Qiu (ES)
Complex operational and planning decisions often rely on mathematical optimization to come up with an optimal (or satisfying) solution in a reasonable time. In most of the application settings, those decisions are made routinely and useful information from solving the optimization problems could be accumulated and used for learning better strategies to make better decisions faster. In this LDRD swift project, we developed a machine learning framework to expedite the solution of a fundamental operational decision-making problem in power systems, i.e., security constrained unit commitment (SCUC), and the results show that our machine learning framework can speed up the SCUC by 10 times on average.
Feng Qiu received his Ph.D. from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is a principal computational scientist with the Energy Systems division. His current research interests include optimization in power system operations, electricity markets and power grid resilience.