no code implementations • 27 Mar 2024 • Chunhui Xu, Jason T. L. Wang, Haimin Wang, Haodi Jiang, Qin Li, Yasser Abduallah, Yan Xu
Image super-resolution has been an important subject in image processing and recognition.
no code implementations • 27 Feb 2024 • Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing
For example, SYMHnet achieves a forecast skill score (FSS) of 0. 343 compared to the FSS of 0. 074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data.
no code implementations • 4 Dec 2023 • Khalid A. Alobaid, Yasser Abduallah, Jason T. L. Wang, Haimin Wang, Shen Fan, Jialiang Li, Huseyin Cavus, Vasyl Yurchyshyn
In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy.
no code implementations • 29 Apr 2023 • Khalid A. Alobaid, Jason T. L. Wang
In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth.
no code implementations • 4 Nov 2022 • Haodi Jiang, Qin Li, Zhihang Hu, Nian Liu, Yasser Abduallah, Ju Jing, Genwei Zhang, Yan Xu, Wynne Hsu, Jason T. L. Wang, Haimin Wang
We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data.
no code implementations • 8 Oct 2022 • Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics.
no code implementations • 28 Sep 2022 • Hewei Zhang, Qin Li, Yanxing Yang, Ju Jing, Jason T. L. Wang, Haimin Wang, Zuofeng Shang
In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.
no code implementations • 5 May 2022 • Yasser Abduallah, Jason T. L. Wang, Prianka Bose, Genwei Zhang, Firas Gerges, Haimin Wang
To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.
no code implementations • 27 Mar 2022 • Yasser Abduallah, Vania K. Jordanova, Hao liu, Qin Li, Jason T. L. Wang, Haimin Wang
Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general.
no code implementations • 23 Jul 2021 • Yasser Abduallah, Jason T. L. Wang, Yucong Shen, Khalid A. Alobaid, Serena Criscuoli, Haimin Wang
In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models' data availability.
no code implementations • 16 Jul 2021 • Haodi Jiang, Ju Jing, Jiasheng Wang, Chang Liu, Qin Li, Yan Xu, Jason T. L. Wang, Haimin Wang
Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.
1 code implementation • 4 Sep 2020 • Yasser Abduallah, Jason T. L. Wang, Yang Nie, Chang Liu, Haimin Wang
Solar flare prediction plays an important role in understanding and forecasting space weather.
4 code implementations • 27 Aug 2020 • Haodi Jiang, Jiasheng Wang, Chang Liu, Ju Jing, Hao liu, Jason T. L. Wang, Haimin Wang
Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments.
no code implementations • 8 May 2020 • Hao Liu, Yan Xu, Jiasheng Wang, Ju Jing, Chang Liu, Jason T. L. Wang, Haimin Wang
By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS.
Solar and Stellar Astrophysics
3 code implementations • 22 Feb 2020 • Hao Liu, Chang Liu, Jason T. L. Wang, Haimin Wang
We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or X-class flare will also produce a coronal mass ejection (CME).
2 code implementations • 17 May 2019 • Hao Liu, Chang Liu, Jason T. L. Wang, Haimin Wang
The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples.
no code implementations • 6 Oct 2016 • Ling Zhong, Jason T. L. Wang
These feature sets are then used to train support vector machines to obtain classification models, based on which classifier ensemble is constructed.
no code implementations • 11 Aug 2016 • Nihir Patel, Jason T. L. Wang
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task.