• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

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.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• Clinical and Experimental Reproductive Medicine
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• v.34 no.3
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• pp.189-196
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• 2007

Objectives: The purpose of this study is to investigate the clinical significance of age and basal serum FSH in predicting the outcomes of in vitro fertilization (IVF) in patients with poor-ovarian response. Methods: From January 2000 to December 2004, 85 second IVF cycles of 85 poor-ovarian response patients under the age of 42 with a back-ground of the first IVF cycles at our infertility center and 5 or less oocytes were retrieved and their basal serum FSH levels of 15$\sim$25 mIU/ml were enrolled in this study. Exclusion criteria were patients with a male factor for the etiology of infertility and undergoing genetic diagnosis of embryo such as PGD. Flare-up protocol was used for ovarian stimulation in all cases. Results: When we stratified the study groups by patient's age, the younger age group (age<35, n=35) showed significantly higher implantation rate (19.0% versus 4.0%, p<0.05) and higher ongoing pregnancy rate (100% versus 14.3%, p<0.05) than the older age group (age$\geq$35, n=50). And then, when we stratified the study populations by basal serum FSH level, the lower FSH group (basal serum FSH<20 mIU/ml, n=58) showed significantly higher number of retrieved oocytes (4.6$\pm$0.7 versus 2.2$\pm$0.5, p<0.05) and lower cancellation rate (19.0% versus 55.6%, p<0.05) than higher FSH group (basal serum FSH$\geq$20 mIU/ml, n=27). Conclusions: In conclusion, it was suggested that the patient's age could predict the IVF outcomes in respect to its potency of pregnancy and ongoing pregnancy. Serum basal FSH levels could predict more accurately the ovarian response of cycle, but not clinical outcomes.