We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2–6.3%.

거친 해상 조건에서 운항하는 선박은 파도와의 상대운동으로 인해 슬래밍 하중에 노출된다. 특히 선수가 자유수면으로 입수하는 과정에서 선체부는 일시적으로 큰 슬래밍 충격하중을 받게된다. 일반적으로 대형 컨테이너선박의 경우, 큰 플레어를 가지는 특징이 있으며, 이로 인해 플레어 슬래밍 충격하중으로 인한 구조적 손상이 발생할 수 있다. 본 연구에서는 슬래밍 수치시뮬레이션을 위해 먼저 신뢰할 만한 실험결과와의 비교검증을 수행하였으며, 선수 및 사파에서 선수플레어 슬래밍 하중을 추정하였다. 그 결과 슬래밍 하중이 발생 되는 위치는 0.975st이며, 최대 충격 하중은 선수파 조건에서 약 475kPa임을 확인하였다.