• 천문학회지
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• 제56권2호
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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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• 제55권10호
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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.

• 한국수정란이식학회지
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• 제16권2호
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• pp.79-89
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• 2001

배분화과정시 나타나는 $Ca^{2+}$ 변화에 미치는 $E_2$의 영향을 알아보고자 whole cell voltage clamp 기법, 방사선 등위원소 면역측정법, 그리고 공초점 현미경을 통하여 $E_2$처리 후 나타나는 $Ca^{2+}$ 전류 변화 및 세포내 $Ca^{2+}$ 농도 변화를 조사하였다. 생쥐의 미성숙 난자는 난소의 난포를 천자하고, 배란난자는 과배란 처리 후 난관에서 회수하였다. 수정란은 과배란 처리 후 수컷 생쥐와 교미를 유도한 후 각각의 단계에 맞는 수정란을 채란하였다. 혈중 $E_2$의 농도는 심장을 천자하여 혈액을 채취한 후 배발달 단계와 호르몬 처리 시간이 일치하는 혈액만을 사용하였다. 본 실험의 결과를 요약하면 다음과 같다. 1. $E_2$처리시 미성숙난자의 제 1극체 형성률 (성숙의 지표)은 $E_2$를 처리하지 않은 난자(83% : 83/100)보다 $E_2$를 처리한 난자 (94%, 94/100)에서 유의적 (P<0.05)으로 높게 나타났다. 2. $E_2$를 처리하였을 때 $Ca^{2+}$ 내향전류의 변화는 -10 mV에서 -1.23$\pm$0.01 nA (n=15)에서 -1.50$\pm$0.03 nA (n=15)로 122% 상승함으로써 유의한 (P<0.05) 변화를 보였다. 3. $E_2$를 처리하지 않은 난자 및 수정란을 1로 한 후 $E_2$를 처리한 난자 및 수정란의 변화를 상대적인 값으로 표시하였다. $E_2$처리한 난자는 1.22$\pm$0.17 (n=10), $E_2$처리한 전핵배는 1.20$\pm$0.14 (n=10), $E_2$처리한 2세포기배는 1.07$\pm$0.01 (n=10), 4세포기배는 1.05$\pm$0.09 (n=10)를 나타냄으로써 수정란의 단계마다 $E_2$의 반응 결과가 차이가 남을 알 수 있었다. 4. $E_2$농도 곡선에서 PMSG 처리 후 $E_2$의 혈중농도는 계속적인 상승을 보이다가 배란시기에 최고치를 나타내었으며, 배란 후 다시 감소하여 8세포기에서는 급격한 감소현상이 나타났다. 이후 다시 상실기를 거쳐 배반포기 임신기간동안 $E_2$의 농도가 상승하였다. 5. $E_2$처리 후 세포내 $Ca^{2+}$ 농도변화의 결과로, $E_2$를 처리하지 않은 난자들의 세포내 $Ca^{2+}$ 농도는 836.4$\pm$131.2 (n=10), $E_2$를 처리한 난자들은 1736.4$\pm$192.0 (n=10)로써 유의한 (P<0.05) 차이를 보였다. 이상의 결과로부터 $E_2$처리에 의한 세포내 $Ca^{2+}$ 농도 상승은 $E_2$가 $Ca^{2+}$ 통로를 자극함으로써 세포바깥의 $Ca^{2+}$이 세포안으로 이동하여 나타나는 변화로 생각된다.