Campuses:

Energy minimization

Tuesday, March 24, 2009 - 10:45am - 11:30am
Leah Bar (University of Minnesota, Twin Cities)
Many problems in image processing are addressed via the minimization
of a cost functional. The most prominently used optimization
technique is gradient-descent, often used due to its simplicity and
applicability where other techniques, e.g., those coming from
discrete optimization, can not be applied. Yet, gradient-descent
suffers from slow convergence, and often to just local minima which
highly depend on the initialization and the condition number of the
functional Hessian. Newton-type methods, on the other hand, are
Saturday, November 1, 2008 - 10:00am - 10:30am
Eray Aydil (University of Minnesota, Twin Cities)
Efficient solar-to-electric energy conversion with inexpensive solar cells and materials is one of the most important challenges we face in the 21st century. Crystalline silicon solar cells based on the conventional p-n junction dominate the solar cell market and are commercially available in modules with 15-20% efficiencies. However, they are still too expensive to manufacture which limits their potential for replacing energy from burning fossil fuels.
Friday, June 14, 2013 - 11:00am - 12:30pm
Anders Rantzer (Lund University)
Many problems in optimization of transportation networks for heat and power can be stated in terms of matrices with non-negative coefficients. Moreover, dynamical models for such systems often have monotone step responses. This has great advantages in design and verification of controllers for large-scale networks. In particular optimal controllers can be computed using linear programming, with a complexity that scales linearly with the number of states and interconnections.
Wednesday, June 12, 2013 - 2:30pm - 3:20pm
Zheng O'Neill (University of Alabama)
Building energy systems often consume approximately 16% more energy than is necessary due to system deviation from the design intent. Identifying the root causes of energy waste in buildings can be challenging largely because energy flows are generally invisible. To help address this challenge, we present a model-based, real-time whole building energy diagnostics and performance monitoring system.
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