75 citations · 188 across the 27 of their papers we have counts for
27 papers
Ordinal Encoding as a Regularizer in Binary Loss for Solar Flare Prediction
Chetraj Pandey, Jinsu Hong, Anli Ji +2
The prediction of solar flares is typically formulated as a binary classification task, distinguishing events as either Flare (FL) or No-Flare (NF) according to a specified thresho…
Operational and Exploration Requirements and Research Capabilities for SEP Environment Monitoring and Forecasting
Viacheslav Sadykov, Petrus Martens, Dustin Kempton +15
Mitigating risks posed by solar energetic particles (SEPs) to operations and exploration in space and Earth's atmosphere motivates the development of advanced, synergistic approach…
Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting
Chetraj Pandey, Anli Ji, Jinsu Hong +2
In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cro…
Advancing Solar Flare Prediction using Deep Learning with Active Region Patches
Chetraj Pandey, Temitope Adeyeha, Jinsu Hong +2
In this paper, we introduce a novel methodology for leveraging shape-based characteristics of magnetograms of active region (AR) patches and provide a novel capability for predicti…
Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction
Junzhi Wen, Rafal A. Angryk
Time series data plays a crucial role across various domains, making it valuable for decision-making and predictive modeling. Machine learning (ML) and deep learning (DL) have show…
FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-encoder
Yang Chen, Dustin J. Kempton, Rafal A. Angryk
The success of deep learning-based generative models in producing realistic images, videos, and audios has led to a crucial consideration: how to effectively assess the quality of…