13,087 research outputs found
Understanding Kernel Size in Blind Deconvolution
Most blind deconvolution methods usually pre-define a large kernel size to
guarantee the support domain. Blur kernel estimation error is likely to be
introduced, yielding severe artifacts in deblurring results. In this paper, we
first theoretically and experimentally analyze the mechanism to estimation
error in oversized kernel, and show that it holds even on blurry images without
noises. Then to suppress this adverse effect, we propose a low rank-based
regularization on blur kernel to exploit the structural information in degraded
kernels, by which larger-kernel effect can be effectively suppressed. And we
propose an efficient optimization algorithm to solve it. Experimental results
on benchmark datasets show that the proposed method is comparable with the
state-of-the-arts by accordingly setting proper kernel size, and performs much
better in handling larger-size kernels quantitatively and qualitatively. The
deblurring results on real-world blurry images further validate the
effectiveness of the proposed method.Comment: Accepted by WACV 201
An Analysis of Empirical Cases of Community Wind in Oregon
Recently, a new type of ownership has emerged in the wind energy sector in the U.S. that is referred to as community wind. With the fact that there is little literature on empirical experience of such smaller community based projects, this paper attempts to present seven community wind cases in Oregon to identify opportunities and barriers and so to provide practical information to those who are interested in developing community wind
Plug-in Hybrid Electric Vehicle in Smart Grid
In this thesis, in order to investigate the impact of charging load from plug-in hybrid electric vehicles (PHEVs), a stochastic model is developed in Matlab. In this model, two main types of PHEVs are defined: public transportation vehicles and private vehicles. Different charging time schedule, charging speed and battery capacity are considered for each type of vehicles. The simulation results reveal that there will be two load peaks (at noon and in evening) when the penetration level of PHEVs increases continuously to 30% in 2030. Therefore, optimization tool is utilized to shift load peaks. This optimization process is based on real time pricing and wind power output data. With the help of smart grid, power allocated to each vehicle could be controlled. As a result, this optimization could fulfill the goal of shifting load peaks to valley areas where real time price is low or wind output is high
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