Coronal Holes (CHs) are regions of open magneticfield lines, resulting in highspeed solar wind. Accurate detection of CHs is vital for spaceweather prediction. This paper presents an intramethod ensemble for coronalhole detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all ondisk regions of a solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region’s proximity to, and homogeneity with, the core of identified CH regions. By relying on region homogeneity, and not intensity (which can vary due to various factors, including lineofsight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions maintaining a median IOU
There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen selforganizing map (SOM) is an unsupervised artificial neural network for clustering, visualizing, and reducing the dimensionality of complex data. Like all clustering methods, it requires a measure of similarity between input data (in this work time series). Dynamic time warping (DTW) is one such measure, and a top performer that accommodates distortions when aligning time series. Despite its popularity in clustering, DTW is limited in practice because the runtime complexity is quadratic with the length of the time series. To address this, we present a new a selforganizing map for clustering TIME Series, called SOMTimeS, which uses DTW as the distance measure. The method has similar accuracy compared with other DTWbased clustering algorithms, yet scales better and runs faster. The computational performance stems from the pruning of unnecessary DTW computations during the SOM’s training phase. For comparison, we implement a similar pruning strategy for Kmeans, and call the latter KTimeS. SOMTimeS and KTimeS pruned 43% and 50% of the total DTW computations, respectively. Pruning effectiveness, accuracy, execution time and scalability are evaluated using 112 benchmark time series datasets from the UC Riverside classification archive, and show that for similar accuracy, a 1.8
 NSFPAR ID:
 10470043
 Publisher / Repository:
 Springer Science + Business Media
 Date Published:
 Journal Name:
 Data Mining and Knowledge Discovery
 ISSN:
 13845810
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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