Multi-scale statistical analysis of coronal solar activity

Abstract. Multi-filter images from the solar corona are used to obtain temperature maps that are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions, we show that the multi-scale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also to be extracted from the analysis.

Download Full-text

Multiscale statistical analysis of coronal solar activity

10.5194/npg-2015-83 ◽

2016 ◽

Author(s):

Diana Gamborino ◽

Diego del-Castillo-Negrete ◽

Julio J. Martinell

Keyword(s):

Solar Activity ◽

Statistical Analysis ◽

Structural Information ◽

Active Regions ◽

Orthogonal Decomposition ◽

Level Of Activity ◽

Before And After ◽

Temperature Maps ◽

Proper Orthogonal ◽

Region Information

Abstract. Multi-filter images from the solar corona are used to obtain temperature maps which are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions we show that the multiscale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also be extracted from the analysis.

Download Full-text

Multi-scale proper orthogonal decomposition of complex fluid flows

Journal of Fluid Mechanics ◽

10.1017/jfm.2019.212 ◽

2019 ◽

Vol 870 ◽

pp. 988-1036 ◽

Cited By ~ 21

Author(s):

M. A. Mendez ◽

M. Balabane ◽

J.-M. Buchlin

Keyword(s):

Proper Orthogonal Decomposition ◽

Correlation Matrix ◽

Feature Detection ◽

Orthogonal Decomposition ◽

Data Driven ◽

Dynamic Mode ◽

Time Frequency ◽

Multi Scale ◽

Detection Capabilities ◽

Proper Orthogonal

Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for tracking the evolution of coherent patterns in large datasets, and for constructing low-order models of complex phenomena. In this work, we analyse the main limits of two popular decompositions, namely the proper orthogonal decomposition (POD) and the dynamic mode decomposition (DMD), and we propose a novel decomposition which allows for enhanced feature detection capabilities. This novel decomposition is referred to as multi-scale proper orthogonal decomposition (mPOD) and combines multi-resolution analysis (MRA) with a standard POD. Using MRA, the mPOD splits the correlation matrix into the contribution of different scales, retaining non-overlapping portions of the correlation spectra; using the standard POD, the mPOD extracts the optimal basis from each scale. After introducing a matrix factorization framework for data-driven decompositions, the MRA is formulated via one- and two-dimensional filter banks for the dataset and the correlation matrix respectively. The validation of the mPOD, and a comparison with the discrete Fourier transform (DFT), DMD and POD are provided in three test cases. These include a synthetic test case, a numerical simulation of a nonlinear advection–diffusion problem and an experimental dataset obtained by the time-resolved particle image velocimetry (TR-PIV) of an impinging gas jet. For each of these examples, the decompositions are compared in terms of convergence, feature detection capabilities and time–frequency localization.

Download Full-text

Model order reduction using sparse coding exemplified for the lid-driven cavity

Journal of Fluid Mechanics ◽

10.1017/jfm.2016.616 ◽

2016 ◽

Vol 808 ◽

pp. 189-223 ◽

Cited By ~ 8

Author(s):

Rohit Deshmukh ◽

Jack J. McNamara ◽

Zongxian Liang ◽

J. Zico Kolter ◽

Abhijit Gogulapati

Keyword(s):

Unsteady Flow ◽

Proper Orthogonal Decomposition ◽

Sparse Coding ◽

Flow Fields ◽

Orthogonal Decomposition ◽

Driven Cavity ◽

Reduced Order ◽

Multi Scale ◽

Small Set ◽

Proper Orthogonal

Basis identification is a critical step in the construction of accurate reduced-order models using Galerkin projection. This is particularly challenging in unsteady flow fields due to the presence of multi-scale phenomena that cannot be ignored and may not be captured using a small set of modes extracted using the ubiquitous proper orthogonal decomposition. This study focuses on this issue by exploring an approach known as sparse coding for the basis identification problem. Compared with proper orthogonal decomposition, which seeks to truncate the basis spanning an observed data set into a small set of dominant modes, sparse coding is used to identify a compact representation that spans all scales of the observed data. As such, the inherently multi-scale bases may improve reduced-order modelling of unsteady flow fields. The approach is examined for a canonical problem of an incompressible flow inside a two-dimensional lid-driven cavity. The results demonstrate that Galerkin reduction of the governing equations using sparse modes yields a significantly improved predictive model of the fluid dynamics.

Download Full-text

Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/505 ◽

2021 ◽

Author(s):

Wangli Lin ◽

Li Sun ◽

Qiwei Zhong ◽

Can Liu ◽

Jinghua Feng ◽

...

Keyword(s):

Large Scale ◽

Structural Information ◽

Critical Role ◽

Fraud Detection ◽

Web Pages ◽

Intrinsic Structure ◽

Structure Information ◽

Multi Scale ◽

Sequential Information ◽

Scale Behavior

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

Download Full-text