• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3543-3548
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• 2023

Shutdown chemistry evolution is performed in nuclear power plants at each refueling outage (RFO) to establish safe conditions to open system and minimize inventory of corrosion products in the reactor coolant system (RCS). After hydrogen peroxide is added to RCS during shutdown chemistry evolution, corrosion products are released and are removed by filters and ion exchange resins in the chemical volume control system (CVCS). Shutdown chemistry evolution including RCS clean-up time to remove released corrosion products impacts the critical path schedule during RFOs. The estimation of clean-up time prior to RFO can provide more reliable actions for RCS clean-up operations and transients to operators during shutdown chemistry. Electric Power Research Institute (EPRI) shutdown calculator (SDC) enables to provide clean-up time by Co-58 peak activity through operational data from nuclear power plants (NPPs). In this study, we have investigated the results of EPRI SDC by shutdown chemistry data of Co-58 activity using NPP data from previous cycles and modeled the estimated clean-up time by EPRI SDC using average Co-58 activity of the NPP. We selected two RFO data from the NPP to evaluate EPRI SDC results using the purification time to reach to 1.3 mCi/cc of Co-58 after hydrogen peroxide addition. Comparing two RFO data, the similar purification time between actual and computed data by EPRI SDC, 0.92 and 1.74 h respectively, was observed with the deviation of 3.7-7.2%. As the modeling the estimated clean-up time, we calculated average Co-58 peak concentration for normal cycles after cycle 10 and applied two-sigma (2σ, 95.4%) for predicted Co-58 peak concentration as upper and lower values compared to the average data. For the verification of modeling, shutdown chemistry data for RFO 17 was used. Predicted RCS clean-up time with lower and upper values was between 21.05 and 27.58 h, and clean-up time for RFO 17 was 24.75 h, within the predicted time band. Therefore, our calculated modeling band was validated. This approach can be identified that the advantage of the modeling for clean-up time with SDC is that the primary prediction of shutdown chemistry plans can be performed more reliably during shutdown chemistry. This research can contribute to improving the efficiency and safety of shutdown chemistry evolution in nuclear power plants.

• Journal of Embryo Transfer
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• v.16 no.2
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• pp.79-89
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• 2001

Steroid hormones control the expression of many cellular regulators, and a role thor estrogen in mouse oocytes has been well documented. The preovulatory $E_2$increment is generally accepted as the endocrine process regulating induction of in vivo oocyte maturation To address whether the activity of the T-type $Ca^{2+}$ channel is altered by 17 beta-estradiol ( $E_2$), we examined the actions of $E_2$on the calcium channel of mouse oocytes and early embryos. Oocrtes were collected from the oviduct of mice treated with pregnant mare's serum gonadotropin (PMSG) and human choronic gonadotropin (hCG). Whole cell voltage clamp technique and confocal microscopy were used to examine that $E_2$increase intracellular $Ca^{2+}$ concentration ([C $a^{2+}$]$_{i}$ ) via voltage dependent $Ca^{2+}$ channel (VDC) and estrogen receptor (FSR), and $E_2$concentration by the use of radioimmunoassay (RIA) were examined in mouse. The results obtained were as follows: The peak of $Ca^{2+}$ current induced by $E_2$increased 122% to 1.50$\pm$0.03 nA from 1.23$\pm$0.21 nA (n=15) in the presence of 5 mM extracellular $Ca^{2+}$ concentration ([C $a^{2+}$]$_{o}$ ). The increased $Ca^{2+}$ current was temporally associated with $Ca^{2+}$ transients. The intracellular $Ca^{2+}$ level increased 207%~30 s following the addition of 1${\mu}{\textrm}{m}$ $E_2$(relative fluorescence intensity: 836.4$\pm$131.2 for control, n=10, 1736.4$\pm$192.0 in the presence of $E_2$, n=10). $E_2$increased amplitude of $Ca^{2+}$ current and [C $a^{2+}$]$_{i}$ . $E_2$-induced $Ca^{2+}$ current and $E_2$concentration in blood were showed difference on the stage of embryo. These results suggest that $E_2$modulate $Ca^{2+}$ channel to increase $Ca^{2+}$ influx.$Ca^{2+}$ influx.