• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.724-729
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• 2008

In the case of 'MAEMI', the Typhoon which formed in September, 2003, the largest-scale damage of tidal wave was caused by the co-occurrence of Typhoon surge and full tide. Until now Korea has been focusing on the calculating the amount of damage and its restoration to cope with these sea and harbor disasters. It is essential to establish some systematic counterplans to diminish such damages of large-scale tidal invasion on coastal lowlands considering the recent weather conditions of growing scale of typhoons. Therefore, the purpose of this research is to make the counterplans for prevention against disasters fulfilled effectively based on the data conducted by comparing and analyzing the accuracy between observation values and the results of estimating the greatest overflow area according to abnormal tidal levels centered on Masan area where there was the severest damage from tidal wave at that time. It's necessary utilize data like high-resolution satellite image and LiDAR(etc.) for correct analysis data considering geographical characteristics of dangerous area from the storm surge. And we must make a solution to minimize the damage by making data of dangerous section of flood into GIS Database using those data (as stated above) and drawing correcter damage function.

• Korean Journal of Environmental Biology
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• v.37 no.1
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• pp.72-81
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• 2019

Bisphenol A (BPA), a representative endocrine disrupting chemicals, has adverse effects on growth, development and reproduction in aquatic organisms. The object of this study was to investigate the modulation of antioxidant enzyme-coding genes using quantitative real time RT-PCR (qRT-PCR), enzyme activity and total protein content, to understand oxidative stress responses after exposure to BPA for 48 h in brackish water flea Diaphanosoma celebensis. The BPA ($3mg\;L^{-1}$) significantly upregulated the expression of Cu/Zn-SOD, Mn-SOD, and catalase (CAT) mRNA. Three GST isoforms (GST-kappa, GST-mu, and GST-theta) mRNA levels significantly increased at the rate of $0.12mg\;L^{-1}$ of BPA. In particular, GST-mu showed the highest expression level, indicating its key role in antioxidant response to BPA. SOD activity was induced with a concentration-dependent manner, and total protein contents was reduced. These findings indicate that BPA can induce oxidative stress in this species, and these antioxidants may be involved in cellular protection against BPA exposure. This study will provide a better understanding of molecular mode of action of BPA toxicity in aquatic organisms.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.747-757
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• 2021

Quantification of extreme rainfall is very important in establishing a flood protection plan, and a general measure of extreme rainfall is expressed as an T-year return level. In this study, a method was proposed for quantifying spatial distribution and uncertainty of daily rainfall depths with various return periods using a hierarchical Bayesian model combined with climate and geographical information, and was applied to the Seoul-Incheon-Gyeonggi region. The annual maximum daily rainfall depth of six automated synoptic observing system weather stations of the Korea Meteorological Administration in the study area was fitted to the generalized extreme value distribution. The applicability and reliability of the proposed method were investigated by comparing daily rainfall quantiles for various return levels derived from the at-site frequency analysis and the regional frequency analysis based on the index flood method. The uncertainty of the regional frequency analysis based on the index flood method was found to be the greatest at all stations and all return levels, and it was confirmed that the reliability of the regional frequency analysis based on the hierarchical Bayesian model was the highest. The proposed method can be used to generate the rainfall quantile maps for various return levels in the Seoul-Incheon-Gyeonggi region and other regions with similar spatial sizes.

• Journal of Life Science
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• v.31 no.1
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• pp.83-89
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• 2021

Uu-ilys, an i-type lysozyme from spoon worm (Urechis unicinctus), is an innate immune factor that plays an important role in the defense against pathogens. It also possesses non-enzymatic antibacterial activity. Thus, there is a possibility to develop an antimicrobial model peptide from Uu-ilys. In this study, we report the design, production, and antibacterial activity of an Uu-ilys analog that exhibits antibacterial activity. The Uu-ilys structure was fragmented according to its secondary structures to predict the regions with antimicrobial activity using antimicrobial peptide (AMP) prediction tools from different AMP databases. A peptide containing the C-terminal fragment was predicted to exert antimicrobial activity. The chosen fragment was designated as an Uu-ilys analog containing the C-terminal fragment, Uu-ilys-CF. To examine the possibility of developing an AMP using the sequence of Uu-ilys-CF, recombinant fusion protein (TrxA-Uu-ilys-CF) was produced in an expression system that was heterologous. The produced fusion protein was cleaved after methionine leaving Uu-ilys-CF free from the fusion protein. This was then isolated through high performance liquid chromatography and reverse phase column, CapCell-Pak C18. The antibacterial activity of Uu-ilys-CF against different microbial strains (four gram-positive, six gram-negative, and one fungal strain) were assessed through the ultrasensitive radial diffusion assay (URDA). Among the bacterial strains tested, Salmonella enterica was the most susceptible. While the fungal strain tested was not susceptible to Uu-ilys-CF, broad spectrum antibacterial activity was observed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.1
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• pp.19-26
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• 2003

Recently, the number of internet service companies is increasing and so is the number of malicious attackers. Damage such as distrust about credit and instability of the service by these attacks may influence us fatally as it makes companies image failing down. One of the frequent and fatal attacks is DoS(Denial-of-Service). Because the attacker performs IP spoofing for hiding his location in DoS attack it is hard to get an exact location of the attacker from source IP address only. and even if the system recovers from the attack successfully, if attack origin has not been identified, we have to consider the possibility that there may be another attack again in near future by the same attacker. This study suggests to find the attack origin through MAC address marking of the attack origin. It is based on an IP trace algorithm, called Marking Algorithm. It modifies the Martins Algorithm so that we can convey the MAC address of the intervening routers, and as a result it can trace the exact IP address of the original attacker. To improve the detection time, our algorithm also contains a technique to improve the packet arrival rate. By adjusting marking probability according to the distance from the packet origin we were able to decrease the number of needed packets to traceback the IP address.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.257-266
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• 2021

Urban areas can often suffer flood damage because of the more frequent catastrophic rainfall events from climate change. Flood mitigation measures consist of (1) structural and (2) non-structural measures. In this study, the proposed method focused on operating an urban drainage system among non-structural measures. The combined inland-river operation technique estimates the inflow of pump stations based on the water level obtained from a preselected monitoring point, and the pump station expels the stored rainwater to the riverside based on those estimates. In this study, the proposed method was applied to the Mokgam drainage watershed, where catastrophic rainfall events occurred (i.e., 2010- and 2011-years), and severe flood damage was recorded in Seoul. Using the proposed method, the efficiency of flood reduction from the two rainfall events was reduced by 34.9 % and 54.4 %, respectively, compared to the current operation method. Thus, the proposed method can minimize the flood damage in the Mokgam drainage watershed by reserving the additional storage space of a reservoir. In addition, flooding from catastrophic rainfall can be prevented, and citizens' lives and property in urban areas can be protected.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Journal of Radiation Protection and Research
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• v.27 no.3
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• pp.147-154
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• 2002

Purpose : Measurement of transmission dose is useful for in vivo dosimetry of QA purpose. The objective of this study is to develope an algorithm for estimation of tumor dose using measured transmission dose for open radiation field. Materials and Methods : Transmission dose was measured with various field size (FS), phantom thickness (Tp), and phantom chamber distance (PCD) with a acrylic phantom for 6 MV and 10 MV X-ray. Source to chamber distance (SCD) was set to 150 cm. Measurement was conducted with a 0.6 co Farmer type ion chamber. Using measured data and regression analysis, an algorithm was developed lot estimation of expected reading of transmission dose. Accuracy of the algorithm was tested with flat solid phantom with various settings. Results : The algorithm consisted of quadratic function of log(A/P) (where A/P is area-perimeter ratio) and tertiary function of PCD. The algorithm could estimate dose with very high accuracy for open square field, with errors within ${\pm}0.5%$. For elongated radiation field, the errors were limited to ${\pm}1.0%$. Conclusion : The developed algorithm can accurately estimate the transmission dose in open radiation fields with various treatment settings.