• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.469-476
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• 2008

Recently, TOD gains popularity as a traffic solution measure of high density urban regeneration projects. The purpose of this study is to investigate traffic impacts of high density TOD projects, and to identify the issues to be resolved. For a case study, it chooses Gangnamgucheong station in Gangnam area served by two subway lines, and designates 400m radius from the station as a site for high-density development. The MOEs chosen for this study is traffic volume, time, distance, speed, and mode share. The SECOM model is adopted for traffic simulation. The analysis results show that high-density TOD is an effective tool for traffic improvement even with only one station area being implemented. It is found that the traffic volume increases near the station in nature where high-density development occurs, but it declines overall in the rest of Gangam area. The total travel time and distance of passenger vehicles decline, meaning that the traffic condition becomes better than before. With regulation on parking supply, the improvement becomes more vivid. In terms of the changes of traffic speed, both alternatives show 4.1% increase in speed, but the difference between alternatives is not quite noticeable because of the induced vehicle demand driven to the streets with improved traffic condition. The mode share changes occur for the benefit of subway ridership, because the study station is equipped with two subway line services. When mixed with parking supply restriction, the impact becomes clearer.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.653-663
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• 2008

The purpose of this study is to establish a database of weather, hydrology, point source pollution management, reservoir release and tillage management for SWAT model evaluation of Anseongcheon watershed ($370.1km^2$, the upstream of Gongdo water level gauging station), and to use them for the following research of future climate and land use change impact on streamflow and stream water quality. It is expected that the database can achieve the practical analysis of current watershed hydrologic and environmental condition. The model calibration and validation were conducted using the constructed database. The model results showed that the tillage management affected the temporal shift of pollutant loads, and changed the flow pattern of pollutant transport through cultivation area. It was identified that the April and May irrigation water supply from the agricultural reservoir also affected the streamflow of downstream. The data application of pollutants treatment facilities and tillage management of cultivation area showed about 10% difference in the simulation results of stream water quality. The data establishment of agricultural reservoir operation, the tillage management of cultivated area within the watershed and the attributes inclusion of pollutants treatment facilities were proved to be important in SWAT model evaluation. The results of model setup in this study are expected for more reliable model application in the following research of future climate and land use change impact on hydrology and stream water quality of the study watershed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.665-673
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• 2008

This study is to assess the future potential climate and land use change impact on streamflow and stream water quality of the study watershed using the established model parameters (I). The CCCma (Canadian Centre for Climate Modelling and Analysis) CGCM2 (Canadian Global Coupled Model) based on IPCC SRES (Special Report Emission Scenarios) A2 and B2 scenarios were adopted for future climate condition, and the data were downscaled by Stochastic Spatio-Temporal Random Cascade Model technique. The future land use condition was predicted by using modified CA-Markov (Cellular Automata-Markov chain) technique with the past time series of Landsat satellite images. The model was applied for the future extreme precipitation cases of around 2030, 2060 and 2090. The predicted results showed that the runoff ratio increased 8% based on the 2005 precipitation (1160.1 mm) and runoff ratio (65%). Accordingly the Sediment, T-N and T-P also increased 120%, 16% and 10% respectively for the case of 50% precipitation increase. This research has the meaning in providing the methodological procedures for the evaluation of future potential climate and land use changes on watershed hydrology and stream water quality. This model result are expected to plan in advance for healthy and sustainable watershed management and countermeasures of climate change.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6A
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• pp.977-988
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• 2006

The goal of this study is to develop a realistic methodology for determination of the Life-Cycle Cost (LCC)-effective optimal seismic retrofit and maintenance strategy of deteriorating bridges. The proposed methodology is based on the concept of minimum LCC which is expressed as the sum of present value of seismic retrofit costs, expected maintenance costs, and expected economic losses with the constraints such as design requirements and acceptable risk of death. The proposed methodology is applied to the LCC-effective optimal seismic retrofit and maintenance strategy of a steel bridge considered as a example bridge in the accompanying study, and various conditions such as corrosion environments and Average Daily Traffic Volumes (ADTVs) are considered to investigate the effects on total expected LCC. In addition, to verify the validity of the developed methodology, the results are compared with the existing methodology. From the numerical investigation, it may be positively expected that the proposed methodology can be effectively utilized as a practical tool for the decision-making of LCC-effective optimal seismic retrofit and maintenance strategy of deteriorating bridges.

• Convergence Security Journal
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• v.23 no.2
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• pp.27-36
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• 2023

In recent years, ransomware attacks have become more organized and specialized, with the sophistication of attacks targeting specific individuals or organizations using tactics such as social engineering, spear phishing, and even machine learning, some operating as business models. In order to effectively respond to this, various researches and solutions are being developed and operated to detect and prevent attacks before they cause serious damage. In particular, honeypots can be used to minimize the risk of attack on IT systems and networks, as well as act as an early warning and advanced security monitoring tool, but in cases where ransomware does not have priority access to the decoy file, or bypasses it completely. has a disadvantage that effective ransomware response is limited. In this paper, this honeypot is optimized for the user environment to create a reliable real-time dynamic honeypot file, minimizing the possibility of an attacker bypassing the honeypot, and increasing the detection rate by preventing the attacker from recognizing that it is a honeypot file. To this end, four models, including a basic data collection model for dynamic honeypot generation, were designed (basic data collection model / user-defined model / sample statistical model / experience accumulation model), and their validity was verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.273-275
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• 2022

The use of drugs by pregnant women poses a potential risk to the fetus. Therefore, it is essential to classify drugs that pregnant women should prohibit. However, the fetal toxicity of most drugs has not been identified. This takes a lot of time and cost. In silico approaches, such as virtual screening, can identify compounds that may present a high risk to the fetus for a wide range of compounds at the low cost and time. We collected class information of each drug from the hazard classification lists for prescribing drugs in pregnancy by the government of Korea and Australia. Using the structural and chemical features of each drug, various machine learning models were constructed to predict fetal toxicity of drugs. For all models, the quantitative performance evaluation was performed. Based on the attention algorithm, important molecular substructures of compounds were identified in the process of predicting the fetal toxicity of the drug by the proposed model. From the results, we confirmed that drugs with a high risk of fetal toxicity can be predicted for a wide range of compounds by machine learning. This study can be used as a pre-screening tool for fetal toxicity predictions, as it provides key molecular substructures associated with the fetal toxicity of compounds.

• Fisheries and Aquatic Sciences
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• v.26 no.9
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• pp.558-568
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• 2023

Vibrio vulnificus is an aquatic bacterium causing septicemia and wound infection in humans. To understand this pathogen at the genomic level, it was performed whole genome sequencing of a cefoxitin-resistant strain, V. vulnificus 1908-10 possessing virulence-related genes (vvhA, viuB, and vcgC) isolated from Gadeok island coastal seawater in South Korea. The genome of V. vulnificus 1908-10 consisted of two circular contigs and no plasmid. The total genome size was estimated to be 5,018,425 bp with a guanine-cytosine (GC) content of 46.9%. We found 119 tRNA and 34 rRNA genes respectively in the genome, along with 4,352 predicted protein sequences. Virulence factor (VF) analysis further revealed that V. vulnificus 1908-10 possess various virulence genes in classes of adherence, antiphagocytosis, chemotaxis and motility, iron uptake, quorum sensing, secretion system, and toxin. In the comparison of the presence/absence of virulence genes, V. vulnificus 1908-10 had fur, hlyU, luxS, ompU, pilA, pilF, rtxA, rtxC, and vvhA. Of the 30 V. vulnificus comparative strains, 80% of the C-genotype strains have all of these genes, whereas 40% of the E-genotype strains have all of them. In particular, pilA were identified in 80% of the C-type strains and 40% of the E-type strains, showing more difference than other genes. Therefore, V. vulnificus 1908-10 had similar VF characteristics to those of type C strains. Multifunctional-autoprocessing repeats-in-toxin (MARTX) toxin of V. vulnificus 1908-10 contained 8 A-type repeats (GXXGXXXXXG), 25 B.1-type repeats (TXVGXGXX), 18 B2-type repeats (GGXGXDXXX), and 7 C-type repeats (GGXGXDXXX). The National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) showed that the RtxA protein of V. vulnificus 1908-10 had the effector domain in the order of cross-liking domain (ACD)-C58_PaToxP-like domain- α/β hydrolase-C58_PaToxP-like domain.

• Explosives and Blasting
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• v.41 no.4
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• pp.1-8
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• 2023

On August 4, 2020, 2750 tons of ammonium nitrate stored in a storage warehouse at the Port of Beirut exploded. This explosion is said to be the largest ammonium nitrate explosion ever. By applying the TNT equivalency method, TNT equivalent amount corresponding to the explosion energy of 2750 tons of ammonium nitrate was calculated, and it is found to be 856 tons. Overpressure and impulse were calculated in a range up to 3600 m from the blast using the Kingery-Bulmash explosion parameter calculator tool. As the distance from the explosion center increases, the overpressure and impulse decrease exponentially, but the overpressure decreases more significantly, showing that overpressure is more affected by distance than the impact. As a result of applying the damage criteria to evaluate the effects of overpressure and impulse on the structure, the critical distances at which partial collapse, major damage, and minor damage to the structure occur are found to be approximately 500, 800, and 2200 m from the center of the explosion, respectively. The probit function was applied to evaluate the probability of damage to structures and human body. The points where the probability of collapse, major damage, minor damage, and breakage of window-panes to structures are greater than 50% are found to be approximately 500, 810, 2200, and 3200 m, respectively. For people within 200 m from the center of the explosion, the probability of death due to lung damage is more than 99%, and the 50% probability of eardrum rupture is approximately 300 m. The points with a 100% probability of death due to skull rupture and whole body impact due to whole body displacement are evaluated to be 300 and 100 m, respectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.147-153
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• 2023

As it spreads to all industries of artificial intelligence technology, AIaaS equipped with artificial intelligence services is emerging. In particular, non-IT companies are suffering from the absence of software experts, difficulties in training big data models, and difficulties in collecting and analyzing various types of data. AIaaS makes it easier and more economical for users to build a system by providing various IT resources necessary for artificial intelligence software development as well as functions necessary for artificial intelligence software in the form of a service. Therefore, the supply and demand for such cloud-based AIaaS services will increase rapidly. However, the quality of services provided by AIaaS becomes an important factor in what is required as the supply and demand for AIaaS increases. However, research on a comprehensive and practical quality evaluation metric to measure this is currently insufficient. Therefore, in this paper, we develop and propose a usability, replacement, scalability, and publicity metric, which are the four metrics necessary for measuring reusability, based on implementation, convenience, efficiency, and accessibility, which are characteristics of AIaaS, for reusability evaluation among the service quality measurement factors of AIaaS. The proposed metrics can be used as a tool to predict how much services provided by AIaaS can be reused for potential users in the future.