Segmentation of coronal features to understand the solar EIV and UV irradiance variability

2020 ◽  
Author(s):  
Joe Zender ◽  
Rens van der Zwaart ◽  
Rangaiah Kariyappa ◽  
Luc Damé ◽  
Gabriel Giono
Keyword(s):  
Linear Model ◽  
Solar Irradiance ◽  
Feature Detection ◽  
Clustering Algorithm ◽  
Coronal Holes ◽  
Principal Component ◽  
Detection Algorithm ◽  
Solar Dynamics Observatory ◽  
The Earth ◽  
Possibilistic Clustering

<p>The study of solar irradiance variability is of great importance in heliophysics, the Earth’s climate, and space weather applications. These studies require careful identifying, tracking and monitoring of features in the solar magnetosphere, chromosphere, and corona.  We studied the variability of solar irradiance for a period of 10 years (May 2010–January 2020) using the Large Yield Radiometer (LYRA), the Sun Watcher using APS and image Processing (SWAP) on board PROBA2, the Atmospheric Imaging Assembly (AIA), and the Helioseismic and Magnetic Imager (HMI) of on board the Solar Dynamics Observatory (SDO), and applied a linear model between the identified features and the measured solar irradiance by LYRA.</p><p>We used the spatial possibilistic clustering algorithm (SPoCA) to identify coronal holes, and a morphological feature detection algorithm to identify active regions (AR), coronal bright points (BPS), and the quite sun (QS) and segment coronal features from the EUV observations of AIA. The AIA segmentation maps were then applied on SWAP images, images of all AIA wavelengths, HMI line-of-sight (LOS) magnetograms, and parameters such as the intensity, fractional area, and contribution of ARs/CHs/BPs/QS features were computed and compared with LYRA irradiance measurements as a proxy for ultraviolet irradiation incident to the Earth atmosphere.</p><p>We modelled the relation between the solar disk features (ARs, CHs, BPs, and QS) applied to magnetrogram and EUV images against the solar irradiance as measured by LYRA and the F10.7 radio flux. To avoid correlation between different the segmented features, a principal component analysis (PCM) was done. Using the independent component, a straightforward linear model was used and corresponding coefficients computed using the Bayesian framework. The model selected is stable and coefficients converge well.</p><p>The application of the model to data from 2010 to 2020 indicates that both at solar cycle timeframes as well as shorter timeframes, the active region influence the EUV irradiance as measured at Earth. Our model replicates the LYRA measured irradiance well.</p>

scholarly journals Segmentation of Coronal Features to Understand the Solar EUV and UV Irradiance Variability III. Inclusion and Analysis of Bright Points

Solar Physics ◽  
2021 ◽  
Vol 296 (9) ◽  
Author(s):  
Rens van der Zwaard ◽  
Matthias Bergmann ◽  
Joe Zender ◽  
Rangaiah Kariyappa ◽  
Gabriel Giono ◽  
...  
Keyword(s):  
Linear Model ◽  
Solar Irradiance ◽  
Active Regions ◽  
Solar Photosphere ◽  
Earth Atmosphere ◽  
Earth’S Atmosphere ◽  
The Earth ◽  
Coronal Bright Points ◽  
Possibilistic Clustering ◽  
Earth's Atmosphere

AbstractThe study of solar irradiance variability is of great importance in heliophysics, Earth’s climate, and space weather applications. These studies require careful identifying, tracking and monitoring of features in the solar photosphere, chromosphere, and corona. Do coronal bright points contribute to the solar irradiance or its variability as input to the Earth atmosphere? We studied the variability of solar irradiance for a period of 10 years (May 2010 – June 2020) using the Large Yield Radiometer (LYRA), the Sun Watcher using APS and image Processing (SWAP) on board PROBA2, and the Atmospheric Imaging Assembly (AIA), and applied a linear model between the segmented features identified in the EUV images and the solar irradiance measured by LYRA. Based on EUV images from AIA, a spatial possibilistic clustering algorithm (SPoCA) is applied to identify coronal holes (CHs), and a morphological feature detection algorithm is applied to identify active regions (ARs), coronal bright points (BPs), and the quiet Sun (QS). The resulting segmentation maps were then applied on SWAP images, images of all AIA wavelengths, and parameters such as the intensity, fractional area, and contribution of ARs/CHs/BPs/QS features were computed and compared with LYRA irradiance measurements as a proxy for ultraviolet irradiation incident to the Earth atmosphere. We modeled the relation between the solar disk features (ARs, CHs, BPs, and QS) applied to EUV images against the solar irradiance as measured by LYRA and the F10.7 radio flux. A straightforward linear model was used and corresponding coefficients computed using a Bayesian method, indicating a strong influence of active regions to the EUV irradiance as measured at Earth’s atmosphere. It is concluded that the long- and short-term fluctuations of the active regions drive the EUV signal as measured at Earth’s atmosphere. A significant contribution from the bright points to the LYRA irradiance could not be found.

scholarly journals An Analysis of Spikes in Atmospheric Imaging Assembly (AIA) Data

Solar Physics ◽  
2021 ◽  
Vol 296 (12) ◽  
Author(s):  
Peter R. Young ◽  
Nicholeen M. Viall ◽  
Michael S. Kirk ◽  
Emily I. Mason ◽  
Lakshmi Pradeep Chitta
Keyword(s):  
Solar Atmosphere ◽  
Extreme Ultraviolet ◽  
Solar Surface ◽  
Coronal Holes ◽  
Active Regions ◽  
Detection Algorithm ◽  
Solar Dynamics Observatory ◽  
Processing Pipeline ◽  
High Resolution Images ◽  
Solar Dynamics

AbstractThe Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) returns high-resolution images of the solar atmosphere in seven extreme ultraviolet (EUV) wavelength channels. The images are processed on the ground to remove intensity spikes arising from energetic particles hitting the instrument, and the despiked images are provided to the community. In this article, a three-hour series of images from the 171 Å channel obtained on 28 February 2017 was studied to investigate how often the despiking algorithm gave false positives caused by compact brightenings in the solar atmosphere. The latter were identified through spikes appearing in the same detector pixel for three consecutive frames. 1096 examples were found from the 900 image frames. These “three-spikes” were assigned to 126 dynamic solar features, and it is estimated that the three-spike method identifies 19% of the total number of features affected by despiking. For any ten-minute sequence of AIA 171 Å images there are around 37 solar features that have their intensity modified by despiking. The features are found in active regions, quiet Sun, and coronal holes and, in relation to solar surface area, there is a greater proportion within coronal holes. In 96% of the cases, the despiked structure is a compact brightening with a size of two arcsec or less, and the remaining 4% have narrow, elongated structures. By applying an EUV burst detection algorithm, we found that 96% of the events could be classified as EUV bursts. None of the spike events are rendered invisible by the AIA processing pipeline, but the total intensity over an event’s lifetime can be reduced by up to 67%. Users are recommended to always restore the original intensities in AIA data when studying short-lived or rapidly evolving features that exhibit fine-scale structure.

scholarly journals Origin of the Solar Rotation Harmonics Seen in the EUV and UV Irradiance

Solar Physics ◽  
2021 ◽  
Vol 296 (11) ◽  
Author(s):  
G. Giono ◽  
J. J. Zender ◽  
R. Kariyappa ◽  
L. Damé
Keyword(s):  
Time Series ◽  
Transition Region ◽  
Solar Irradiance ◽  
Solar Rotation ◽  
Coronal Holes ◽  
Active Regions ◽  
Higher Harmonics ◽  
Uv Irradiance ◽  
Possibilistic Clustering ◽  
Full Disk

AbstractLong-term periodicities in the solar irradiance are often observed with periods proportional to the solar rotational period of 27 days. These periods are linked either to some internal mechanism in the Sun or said to be higher harmonics of the rotation without further discussion of their origin. In this article, the origin of the peaks in periodicities seen in the solar extreme ultraviolet (EUV) and ultraviolet (UV) irradiance around the 7, 9, and 14 days periods is discussed. Maps of the active regions and coronal holes are produced from six images per day using the Spatial Possibilistic Clustering Algorithm (SPoCA), a segmentation algorithm. Spectral irradiance at coronal, transition-region/chromospheric, and photospheric levels are extracted for each feature as well as for the full disk by applying the maps to full-disk images (at 19.3, 30.4, and 170 nm sampling in the corona/hot flare plasma, the chromosphere/transition region, and the photosphere, respectively) from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) from January 2011 to December 2018. The peaks in periodicities at 7, 9, and 14 days as well as the solar rotation around 27 days can be seen in almost all of the solar irradiance time series. The segmentation also provided time series of the active regions and coronal holes visible area (i.e. in the area observed in the AIA images, not corrected for the line-of-sight effect with respect to the solar surface), which also show similar peaks in periodicities, indicating that the periodicities are due to the change in area of the features on the solar disk rather than to their absolute irradiance. A simple model was created to reproduce the power spectral density of the area covered by active regions also showing the same peaks in periodicities. Segmentation of solar images allows us to determine that the peaks in periodicities seen in solar EUV/UV irradiance from a few days to a month are due to the change in area of the solar features, in particular, active regions, as they are the main contributors to the total full-disk irradiance variability. The higher harmonics of the solar rotation are caused by the clipping of the area signal as the regions rotate behind the solar limb.

Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

2013 ◽  
Vol 710 ◽  
pp. 768-771 ◽  
Author(s):  
Xiao Hong Wu ◽  
Tong Xiang Cai ◽  
Bin Wu ◽  
Jun Sun
Keyword(s):  
Clustering Algorithm ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Simple Computation ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Possibilistic Clustering ◽  
Discriminant Information

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

scholarly journals Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (10) ◽  
pp. 1930
Author(s):  
Gabriel Loureiro ◽  
André Dias ◽  
Alfredo Martins ◽  
José Almeida
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Safety Issues ◽  
Spot Detection ◽  
Cloud Clusters ◽  
Aerial Vehicle ◽  
Incoming Light ◽  
Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

scholarly journals An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226397-226408
Author(s):  
Mingxu Jin ◽  
Aoran Lv ◽  
Yuanpeng Zhu ◽  
Zijiang Wen ◽  
Yubin Zhong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Anomaly Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Robust Principal Component Analysis ◽  
Microservice Architecture
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