Abstract. We present a fast approximate nearest neighbor algorithm for semantic segmentation. Our algorithm builds a graph over superpixels from an annotated set of training images. Edges in the graph represent approximate nearest neighbors in feature space. At test time we match superpixels from a novel image to the training images by adding the novel image to the graph. A move-making search algorithm allows us to leverage the graph and image structure for finding matches. We then transfer labels from the training images to the image under test. To promote good matches between superpixels we propose to learn a distance metric that weights the edges in our graph. Our approach is evaluated on four standard semantic segmentation datasets and achieves results comparable with the state-of-the-art.
Frequency stability of power systems becomes more vulnerable with the increase of solar photovoltaic (PV). Energy storage provides an option to mitigate the impact of high PV penetration. Using the U.S. Eastern Interconnection (EI) and Texas Interconnection (ERCOT) power grid models, this paper investigates the capabilities of using energy storage to improve frequency response under high PV penetration. The study result helps to identify the potential and impact factors in utilizing energy storage to improve frequency response in high renewable penetration power grids.
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