• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.255-264
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• 2006

The auxiliary reinforcement method is normally applied to prevent a possible collapse of the tunnel face where the ground condition is not favorable or geologic information is not sufficient. Recently, several engineering approaches have been made to choose the effective reinforcement methods using expert system such as neural network and fuzzy theory field, among others. Even if the expert system has offered many decision aid tools to properly select the reinforcement method, the quantitative assessment items are not easy to estimate and this is why the data mining technique, widely used in the field of social science, medical treatment, banking and agriculture, is introduced in this study. Using decision tree together with PDA, the decision aids for reinforcement method based on field construction data are created to derive the field rules and future study will be concentrated on the application of the proposed methods in a variety of underground development cases.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.71-82
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• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.251-267
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• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.127-134
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• 2008

Potential economic impact of a hypothetical severe accident at a nuclear power plant(Uljin units 3/4) was estimated by applying the Delphi method, which is based on the expert judgements and opinions, in the process of quantifying uncertain factors. For the purpose of this study, it is assumed that the radioactive plume directs the inland direction. Since the economic risk can be divided into direct costs and indirect effects and more uncertainties are involved in the latter, the direct costs were estimated first and the indirect effects were then estimated by applying a weighting factor to the direct cost. The Delphi method however subjects to risk of distortion or discrimination of variables because of the human behavior pattern. A mathematical approach based on the Bayesian inferences was employed for data processing to improve the Delphi results. For this task, a model for data processing was developed. One-dimensional Monte Carlo Analysis was applied to get a distribution of values of the weighting factor. The mean and median values of the weighting factor for the indirect effects appeared to be 2.59 and 2.08, respectively. These values are higher than the value suggested by OECD/NEA, 1.25. Some factors such as small territory and public attitude sensitive to radiation could affect the judgement of panel. Then the parameters of the model for estimating the direct costs were classified as U- and V-types, and two-dimensional Monte Carlo analysis was applied to quantify the overall economic risk. The resulting median of the overall economic risk was about 3.9% of the gross domestic products(GDP) of Korea in 2006. When the cost of electricity loss, the highest direct cost, was not taken into account, the overall economic risk was reduced to 2.2% of GDP. This assessment can be used as a reference for justifying the radiological emergency planning and preparedness.

• Journal of Marine Life Science
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• v.8 no.1
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• pp.87-92
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• 2023

Microplastic particles are ubiquitous in the environment and not standardized particles of size, shape, or type. Therefore, it is very limited to establish a risk assessment framework that accurately evaluated and manage the multi-dimension of marine environment including seawater and sediment based on toxic data. In the study, we review the characteristics and effects of marine environmental microplastic and suggest risk assessment framework (draft) based on the distribution and impact of marine environmental microplastics. Although, the characteristics of environmental microplastic are very widely but the most abundant toxic data are concentrated on unique shape and type, and there are also large gaps of test organism between laboratory-exposed organisms and resident species. Great limitations with respect to toxicity data quality also exist for traditional effect assessment methods, which in reliability of the resulting risk characterizations. However, considering the fact that the international community's movement on microplastics management is gradually strengthening and the pollution level of microplastics in marine environment is increasing, further research on environmental relevant risk assessment technique should be proposed based on the characteristics of microplastics in the marine environment.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.345-363
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• 2003

From the General Circulation Models(GCMs), it is known that the increases of concentrations of greenhouse gases will have significant implications for climate change in global and regional scales. The GCM has an uncertainty in analyzing the meteorologic processes at individual sites and so the 'downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what, at present, climate modellers can provide and what impact assessors require. This paper describes a method for assessing local climate change impacts using a robust statistical downscaling technique. The method facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. The construction of climate change scenarios based on spatial regression(transfer function) downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translates the GCM grid-box predictions with coarse resolution of climate change to site-specific values and the values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather values. In this study, the global climate change scenarios are constructed using the YONU GCM control run and transient experiments.

• Journal of the Korean Society for Railway
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• v.11 no.2
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• pp.158-164
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• 2008

Abstract Infiltration of rainfall that may lead to reduce resistance force due to reduction of matric suction and to increase driving force due to increase of self weight makes the slope fail. There are many specifications to make slope stable based on factor of safety. Although result of slope stability analysis satisfy the specifications, slope failures triggered by rainfall are frequently occurred in reality because slope stability analysis cannot consider uncertainty of each soil properties. This is why conventional analysis has limitation and development of alternative method is needed. So it is suggested to adopt the reliability analysis rather than design based on factor of safety into designing safer structure. Through the evaluation of handicaps for the factor of safety based design, calculation of soil properties by site investigation, and reliability analysis considering distribution of each soil properties, distribution of failure probability in railway slope is obtained. Then, Risk assessment of slopes in Korean railway is executed from the results. Damage loss and incoming loss are considered as the loss. Using these results, it is possible to make proper countermeasure or efficient maintenance.

• Journal of Korea Water Resources Association
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• v.46 no.2
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• pp.99-109
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• 2013

Increasing complexity of the basin environments makes it difficult for single decision maker to consider all relevant aspects of problem, and thus the uncertainty of decision making grows. This study attempts to develop an approach to quantify the spatial flood vulnerability of South Korea. Fuzzy TOPSIS is used to calculate individual preference by each group and then three GDM techniques (Borda count method, Condorcet method, and Copeland method) are used to integrate the individual preference. Finally, rankings from Fuzzy TOPSIS, TOPSIS, and GDM are compared with Spearman rank correlation, Kendall rank correlation, and Emond & Mason rank correlation. As a result, the rankings of some areas are dramatically changed by the use of GDM techniques. Because GDM technique in regional vulnerability assessment may cause a significant change in priorities, the model presented in this study should be considered for objective flood vulnerability assessment.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.4
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• pp.316-322
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• 2019

A probabilistic approach considering uncertainties was employed to investigate the effects on the material characteristics and strength of nuclear bio-logical shield concrete walls, when exposed to long-term radiation during the entire service life. Time-dependent compressive and tensile strengths were estimated by conducting the neutron fluence analysis. For the contaminated concrete, individual compressive and tensile failure probabilities can be possibly evaluated by not only establishing limit-state function withthe predefined critical values but also performing Monte Carlo Simulation. Nuclear power plant types similar to the Kori Unit 1, which was shut off permanently in 2017 after the 40-year operation, were herein selected for an illustrative purpose. Consequently, the probability-based performance assessment and prediction of contaminated concrete walls were well demonstrated.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.636-636
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• 2012

우리나라에서 가장 심각한 자연재해가 홍수재해이므로, 홍수기에 홍수예보를 하는 것은 매우 중요한 일이다. 홍수예보를 위한 예측 과정은 강우예측과 유출해석부분으로 크게 나눌 수가 있는데, 강우를 정확하게 예측하는 일은 주로 정교한 강우모형과 기상학자들의 몫으로 남겨놓는다고 하더라도 정확한 유출해석은 오랜 동안 수문학자들에게 중요한 고민거리였으며, 특히 우리나라와 같이 홍수재해에 취약한 지역에서는 더욱 간절한 문제가 되었다. 우리나라에서는 국가하천을 대상으로 홍수예보모형을 개발하여 하천의 주요지점에 대한 홍수예보를 시행하고 있으며, 매년 보다 정확하고 신속한 예보를 통해 피해를 줄이기 위해 많은 노력을 기울이고 있다. 본 연구에서는 전역최적화기법인 SCE-UA방법을 이용하여 홍수예보모형의 매개변수의 최적화를 수행하였다. 그러나 최적화기법에 의해 제안된 매개변수들이 강우-유출모형이나 유역의 물리적인 특성을 반영하지 못한다는 비판을 피하기 위해 다단계의 최적화를 통해 유역의 물리적인 특성을 반영하면서도 유출수문곡선을 성공적으로 재현하는 매개변수를 제안하고, 각 매개변수가 가지는 의미를 평가하여 실무에서 홍수예보업무의 효율을 높이는데 도움을 주는 것을 목적으로 하였다. 연구를 위해 매개변수의 민감도 분석을 수행하고, 민감도에 따라 최적화 하는 방법을 다르게 적용하였다. 또한 유역의 물리적인 특성을 나타내는 매개변수와 강우의 특성에 따라 변화하는 매개변수를 구분하여, 유역별 다른 매개변수의 범위를 제안하였다. 제안된 매개변수는 검증을 통하여 적용성을 확인하였으며, 유역별 다양한 특성을 성공적으로 나타내었다.