• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.419-430
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• 2005

We have carried out a statistical analysis on solar wind dynamic pressure pulses during geomagnetic storms. The Dst index was used to identify 111 geomagnetic storms that occurred in the time interval from 1997 through 2001. We have selected only the events having the minimum Dst value less than -50 nT. In order to identify the pressure impact precisely, we have used the horizontal component data of the magnetic field H (northward) at low latitudes as well as the solar wind pressure data themselves. Our analysis leads to the following results: (1) The enhancement of H due to a pressure pulse tends to be proportional to the magnitude of minimum Dst value; (2) The occurrence frequency of pressure pulses also increases with storm intensity. (3) For about $30\%$ of our storms, the occurrence frequency of pressure pulses is greater than $0.4\#/hr$, implying that to. those storms the pressure pulses occur more frequently than do periodic substorms with an average substorm duration of 2.5 hrs. In order to understand the origin of these pressure pulses, we have first examined responsible storm drivers. It turns out that $65\%$ of the studied storms we driven by coronal mass ejections (CMEs) while others are associated with corotating interaction regions $(6.3\%)$ or Type II bursts $(7.2\%)$. Out of the storms that are driven by CMEs, over $70\%$ show that the main phase interval overlaps with the sheath, namely, the region between CME body and the shock, and with the leading region of a CME. This suggests that the origin of the frequent pressure pulses is often due to density fluctuations in the sheath region and the leading edge of the CME body.

• Journal of fish pathology
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• v.20 no.3
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• pp.211-220
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• 2007

Red sea bream iridovirus disease (RSIVD) cause massive economic losses in marine aquaculture industry in Korea. The causative agent of this disease (RSIV) infects a wide range of fish species. The aims of this study were to monitor RSIV in wild marine fishes and to give critical information for controling the disease through prophylactic methods. Prevalence of the viral disease, geological distribution and reservoir of the virus were investigated using wild marine fishes captured in southern coast and east china sea for two years. (Polymerase Chain Reaction) PCR results showed that RSIV were detected in 39 (24.3%) out of 160 fish. MCP gene sequences of viral strains isolated in this study were closely related to that of a reference strain, red seabream-K, belonging to Megalocytivirus subgroup Ⅲ. The results suggest that some of wild marine fishes are RSIV carriers and may spread the pathogen directly to fish farmed in coastal area.

• Journal of Astronomy and Space Sciences
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• v.21 no.4
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• pp.441-452
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• 2004

Nowcast and forecast based on realtime data are quite essential for space weather monitoring. We have developed the web pages (http://sun.kao.re.kr) of the KAO Space Weather Monitoring system by using ION (IDL on the Net). They display latest solar and geomagnetic data, and present their expected effects on satellite, communications and ground power system. In addition, daily NOAA/SEC prediction reports on the probability of solar X-ray flares, proton events and geomagnetic storms are provided. To predict the arrival times of interplanetary shocks and CMEs, two different types of prediction models are also implemented. A work is in progress to develop web-based database of several solar and geomagnetic activities. These data are automatically downloaded to our data server in every minute, or every day using IDL and FTP programs. In this paper, we will introduce more details on the development of the KAO Space Weather Monitoring system.

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.479-486
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• 2009

We studied the solar proton events (SPEs) associated with coronal mass ejections (CMEs) during the solar cycle 23 (1997-2006). Using 63 SPE dataset, we investigated the relationship among SPE, flare, and CME, and found that (1) SPE rise time and duration time depend on CME speed and the earthward direction parameter of the CME, and (2) the SPE peak intensity depends on CME speed and X-ray Flare intensity. While inspecting the relation between SPE peak intensity and the direction parameter, we found there are two groups: first group consists of large six SPEs (> 10,000 pfu at > 10 MeV proton channel of GOES satellite) and shows strong correlation (cc = 0.65) between SPE peak intensity and CME direction parameter. The second group has a weak intensity and shows poor correlation between SPE peak intensity and the direction parameter (cc = 0.01). By investigating characteristics of the first group, we found that all the SPEs are associated with very fast halo CME (> 1400km/s) and also they are mostly located at central region and within ${\pm}20^{\circ}$ latitude and ${\pm}30^{\circ}$ longitude strip.

• Journal of the Korean Chemical Society
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• v.37 no.8
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• pp.723-729
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• 1993

Cu(II) ion-responsive chemically modifed electrodes (CMEs) were constructed by incorporating 1-(2-pyridylazo)-2-naphthol (PAN) into a conventional carbon-paste mixture of graphite powder and Nujol oil. Cu(II) ion was chemically deposited on the surface of the PAN-chemically modified electrode in the absence of an applied potential by immersion of the electrode in a buffer solution (pH 3.2) containing Cu(II) ion, and then reduced at a constant potential in 0.1 M KNO$_3$. And a well-defined voltammetric peak could be obtained by scanning the potential to the positive direction. The electrode surface could be regenerated with exposure to acid solution and reused for the determination of Cu(II) ion. In 5 deposition / measurement / regeneration cycles, the response could be reproduced with 6.1${\%}$ relative standard deviation. In case of using the differential pulse voltammetry, the calibration curve for Cu(II) was linear over the range of 2.0 ${times}$ 10$^{-7}$ ∼ 1.0 ${times}$ 10$^{-6}$ M. And the detection limit was 6.0 ${times}$ 10$^{-8}$ M. Studies of the effect of diverse ions showed that Co, Ni, Zn, Pb, Mg and Ag ions added 10 times more than Cu(II) ion did not influence on the determination of Cu(II) ion, except EDTA and oxalate ions.

• 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.