• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• Journal of Industrial Convergence
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• v.21 no.3
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• pp.65-74
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• 2023

This study aimed to identify the effect of occupational calling, compliance with standard precautions and perceived threat for COVID-19 on the COVID-19 burnout of hospital nurses in the convergence society. The participants were 212 nurses; data were analyzed using t-test, ANOVA, Pearson correlation, multiple regression. This study found that: hospital nurses showed higher score for perceived threat of COVID-19, higher scores for COVID-19 burnout. The main factors influencing COVID-19 burnout were perceived threat of COVID-19 (𝛽=.233), and working unit (𝛽=.154). They explained about 6.7% of the COVID-19 burnout. Therefore, systematic support and nursing education is needed to reduce the perceived threat of COVID-19 among nurses.

• Water Engineering Research
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• v.3 no.2
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• pp.123-134
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• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• Atmosphere
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• v.23 no.2
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• pp.143-160
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• 2013

The effect of 3DVAR (Three Dimension Variational data Assimilation) was examined by comparing observation and the simulations of CNTL (to which data assimilation was not applied) and ALL (to which data assimilation was applied using ground observation data and radar data) for the case of a heavy snowfall event (case A) of 11-12 February 2011 in the Yeongdong region. In case A, heavy snow intensively came in the Yeongdong coastal region rather than Daegwallyeong, in particular, around the Gangneung and Donghae regions with total precipitation in Bukgangneung at approximately 91 mm according to the AWS observation. It can be seen that compared to CNTL, ALL simulated larger precipitation along the Yeongdong coastline extending from Sokcho to Donghae while simulating smaller precipitation for inland areas including Daegwallyeong. On comparison of the total accumulated precipitations from simulations of CNTL and ALL, and the observed total accumulated precipitation, the positive effect of the assimilation of ground observation data and radar data could be identified in Bukgangneung and Donghae, on the other hand, the negative effect of the assimilation could be identified in the Daegwallyeong and Sokcho regions. In order to examine the average accuracy of precipitation prediction by CNTL and ALL for the entire Gangwon region including the major points mentioned earlier, the three hour accumulated precipitation from simulations of CNTL and ALL were divided into 5, 10, 15, 20, 25 and 30 mm/3hr and threat Scores were calculated by forecasting time. ALL showed relatively higher TSs than CNTL for all threshold values although there were some differences. That is, when considered generally based on the Gangwon region, the accuracy of precipitation prediction from ALL was improved somewhat compared to that from CNTL.

• CELLMED
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• v.10 no.3
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• pp.21.1-21.8
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• 2020

Over a span of few decades, the world has seen the emergence of new viruses that have posed serious problems to global health .COVID-19 is a major pathogenic threat to the modern world that primarily shoots the respiratory system of human beings. Wuhan which is the capital city of Hubei, China was the first place in the world where first cases of COVID-19 emerged and the scores of cases significantly increased at an immense rate leading to city isolation and establishment of new specially designed hospitals. SARS-CoV had emerged from bats in china (2002) and MERS-CoV from camels transmitted via bats in Middle East (2012) where the previous versions of COVID-19 took place. Infections with SARS-CoV-2 are now widespread, like Nuclear Chain Reaction (NRC). In this review we will discuss the COVID-19 origin, transmission, incubation, diagnosis and therapies available at the present scenario.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.99-105
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• 2012

Track-before-detect techniques based on dynamic programming have provided solutions for detecting targets from a sequence of images. In its application to airborne threat detection, dynamic programming solutions should take into account the distinguishable properties of objects in a collision course. This paper describes the development of a new track scoring function that accumulates scores for airborne targets in Bayesian framework. Numerical results show that the proposed scoring function has slightly better detection capabilities.

• Journal of Environmental Impact Assessment
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• v.6 no.2
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• pp.153-164
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• 1997

This study was conducted to develop the new Environmental Impact Assessment techniques for the grading of rareness in plant. 17 EISs which were submitted in 1996 were analyzed to know the problems in existent EIA techniques for the conservation of rare plants. Category of rare plant was reevaluated and evaluation technique for the grading of rareness in plant was developed. The results obtained from this study were as follows: 1. The evaluation technique of rareness in plant was not reflected in the current preparation provision for EIS. And t his fact also appeared in most of the EIS which were reexamined in this study. 2. The category of rare species which have been considered as the subject of protection in EIA have to be enlarged to more than legally protected species designated by MOE. 3. The taxonomic characteristics and status of species, characteristics of population, geographical characters, extent of threat and its possibility by man resulted from the habitat loss were investigated. Each item was endowed scores from 1 to 5 and all the scores were summed, and then this value was used to evaluate the order of conservation. 4. Conservation measures for the rare plants have to be applied according to the levels of rareness grade, and they are absolute conservation, in situ conservation, transplanting and ex situ conservation, post observation and none by the levels from A to E. 5. Considering the phenology of rare plants, investigation for the rare plants have to be made a1 least three times. 6. Size of the MVP(Minimum Viable Population), pollination ecology also have to be considered for the conservation of rare plants. And in the case of adopting ex situ conservation measure, the site and methods for the conservation of rare plants have to be proposed in detail.

• Atmosphere
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• v.32 no.4
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• pp.353-365
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• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• Korean Journal of Occupational Health Nursing
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• v.24 no.3
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• pp.153-161
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• 2015

Purpose: The purpose of this study was to identify the effects of violence experience, emotional labor and job stress on clinical nurses' depression and to provide suggestions for improving the quality of patient care. Methods: This research involved 257 clinical nurses who were working at an acute care hospital with at least 200 beds in S city and K province. Data were collected from May 23 to June 7 in 2014 and were analyzed using IBM SPSS version 21.0. Results: The results show that 98.1% of subjects had violence experience in the past year and the violence experience included 44.4% physical threat, 37.5% verbal violence and 18.1% physical violence. The average scores were emotional labor 3.57, job stress 3.54 and depression 21.16. There were positive correlations among violence experience, emotional labor, job stress and depression (p<.01). There were also significant co-relationships between depression and violence experience (r=.21, p=.001), between depression and emotional labor (r=.48, p<.001) and between depression and job stress (r=.31, p<.001). Conclusion: The results suggest that it is necessary to set up guidelines for clinical nurses to manage violence, emotional labor and job stress in order to create better working environment and to improve quality of patient care.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.635-637
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• 2021

Cybersecurity assessment is the process of assessing the risk level of a system through threat and vulnerability analysis to take appropriate security measures. Accurate security evaluation models are needed to prepare for the recent increase in cyberattacks and the ever-developing intelligent security threats. Therefore, we present a risk assessment model through a matrix-based security assessment model analysis that scores by assigning weights across security equipment, intervals, and vulnerabilities. The factors necessary for cybersecurity evaluation can be simplified and evaluated according to the corporate environment. It is expected that the evaluation will be more appropriate for the enterprise environment through evaluation by security equipment, which will help the cyber security evaluation research in the future.