High-Performance Outlier Detection Algorithm for Finding Blob-Filaments in Plasma
نویسندگان
چکیده
Magnetic fusion could provide an inexhaustible, clean, and safe solution to the global energy needs. The success of magnetically-confined fusion reactors demands steady-state plasma confinement which is challenged by the edge turbulence such as the blob-filaments. Real-time analysis can be used to monitor the progress of fusion experiments and prevent catastrophic events. We present a real-time outlier detection algorithm to efficiently find blobs in fusion experiments and numerical simulations. We have implemented this algorithm with hybrid MPI/OpenMP and demonstrated the accuracy and efficiency with a set of data from the XGC1 fusion simulation code. Our tests show that we can complete blob detection in two or three milliseconds using Edison, a Cray XC30 system at NERSC and achieve linear time speedup. We plan to apply the detection algorithm to experimental measurement data from operating fusion devices. We also plan to develop a blob tracking algorithm based on the proposed method.
منابع مشابه
Towards Real-Time Detection and Tracking of Blob-Filaments in Fusion Plasma Big Data
Magnetic fusion could provide an inexhaustible, clean, and safe solution to the global energy needs. The success of magnetically-confined fusion reactors demands steady-state plasma confinement which is challenged by the blob-filaments driven by the edge turbulence. Real-time analysis can be used to monitor the progress of fusion experiments and prevent catastrophic events. However, terabytes o...
متن کاملIdentification of outliers types in multivariate time series using genetic algorithm
Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...
متن کاملReduced-Reference Image Quality Assessment based on saliency region extraction
In this paper, a novel saliency theory based RR-IQA metric is introduced. As the human visual system is sensitive to the salient region, evaluating the image quality based on the salient region could increase the accuracy of the algorithm. In order to extract the salient regions, we use blob decomposition (BD) tool as a texture component descriptor. A new method for blob decomposition is propos...
متن کاملOutlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator
The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...
متن کاملDetecting High-Dimensional Outliers: the New Task, Algorithms and Performance
Outlier detection is a fundamental step in knowledge discovery in databases. With the increasing number of high-dimensional databases, existing outlier detection algorithms that work only in the context of full space are unable to effectively screen out informative outliers. This is because majority of these outliers exists only in subspaces. In this paper, we identify a new outlier detection t...
متن کامل