• 한국기후변화학회지
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• 제8권3호
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• pp.239-245
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• 2017

This study aimed to suggest priority for developing emission factor (EF) and to develop the methodology of quantitative assessment of EF in the forestry sector. Based on the stock-difference method, 17 kinds of EFs (27 EFs based on forest types) were required to calculate the carbon emission in the forestry sector. Priority for developing EFs followed the standards, which is a development plan by the government agency, importance of carbon stock for greenhouse gas, and EFs by the species. Currently, the most urgent development of EFs was carbon fraction in biomass and carbon stock in dead wood. Meanwhile, the quantitative assessment of EF consisted of 7 categories (5 categories of compulsory and 2 categories of quality evaluation) and 12 verification factors. Category in compulsory verification consisted of administrative document, determination methodology of emission factors, emission characteristic, accuracy of measurement and analysis, and data representative. Category in quality evaluation consisted of data management and uncertainty estimates. Based on the importance of factors in the verification process, each factor was scored separately, however, the score needs to be coordinated by the government agency. These results would help build a reliable and accurate greenhouse gas inventory report of Korea.

• Asian Pacific Journal of Cancer Prevention
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• 제16권18호
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• pp.8553-8557
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• 2016

Background: Accurate dose assessment and correct identification of irradiated from non-irradiated people are goals of biological dosimetry in radiation accidents. Objectives: Changes in the FDXR and the RAD51 gene expression (GE) levels were here analyzed in response to total body exposure (TBE) to a 6 MV x-ray beam in rats. We determined the accuracy for absolute quantification of GE to predict the dose at 24 hours. Materials and Methods: For this in vivo experimental study, using simple randomized sampling, peripheral blood samples were collected from a total of 20 Wistar rats at 24 hours following exposure of total body to 6 MV X-ray beam energy with doses (0.2, 0.5, 2 and 4 Gy) for TBE in Linac Varian 2100C/D (Varian, USA) in Golestan Hospital, in Ahvaz, Iran. Also, 9 rats was irradiated with a 6MV X-ray beam at doses of 1, 2, 3 Gy in 6MV energy as a validation group. A sham group was also included. After RNA extraction and DNA synthesis, GE changes were measured by the QRT-PCR technique and an absolute quantification strategy by taqman methodology in peripheral blood from rats. ROC analysis was used to distinguish irradiated from non-irradiated samples (qualitative dose assessment) at a dose of 2 Gy. Results: The best fits for mean of responses were polynomial equations with a R2 of 0.98 and 0.90 (for FDXR and RAD51 dose response curves, respectively). Dose response of the FDXR gene produced a better mean dose estimation of irradiated "validation" samples compared to the RAD51 gene at doses of 1, 2 and 3 Gy. FDXR gene expression separated the irradiated rats from controls with a sensitivity, specificity and accuracy of 87.5%, 83.5% and 81.3%, respectively, 24 hours after dose of 2 Gy. These values were significantly (p<0.05) higher than the 75%, 75% and 75%, respectively, obtained using gene expression of RAD51 analysis at a dose of 2 Gy. Conclusions: Collectively, these data suggest that absolute quantification by gel purified quantitative RT-PCR can be used to measure the mRNA copies for GE biodosimetry studies at comparable accuracy to similar methods. In the case of TBE with 6MV energy, FDXR gene expression analysis is more precise than that with RAD51 for quantitative and qualitative dose assessment.

• Nuclear Engineering and Technology
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• 제49권4호
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• pp.721-733
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• 2017

When temporary spent fuel storage pools at nuclear power plants reach their capacity limit, the spent fuel must be moved to an alternative storage facility. However, radioactive materials must be handled and stored carefully to avoid severe consequences to the environment. In this study, the risks of three potential accident scenarios (i.e., maritime transportation, an aircraft crashing into an interim storage facility, and on-site transportation) associated with the spent fuel transportation process were analyzed using a probabilistic approach. For each scenario, the probabilities and the consequences were calculated separately to assess the risks: the probabilities were calculated using existing data and statistical models, and the consequences were calculated using computation models. Risk assessment software was developed to conveniently integrate the three scenarios. The risks were analyzed using the developed software according to the shipment route, building characteristics, and spent fuel handling environment. As a result of the risk analysis with varying accident conditions, transportation and storage strategies with relatively low risk were developed for regulators and licensees. The focus of this study was the risk assessment methodology; however, the applied model and input data have some uncertainties. Further research to reduce these uncertainties will improve the accuracy of this model.

• 한국농공학회논문집
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• 제66권2호
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• pp.35-52
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• 2024

This study conducted an assessment of potential impacts on the drought in agricultural reservoirs using the recently proposed SSP (Shared Socioeconomic Pathways) scenarios by IPCC (Intergovernmental Panel on Climate Change). This study assesses the potential impact of climate change on agricultural water resources and infrastructure vulnerability within Gyeongsangnam-do, focusing on 15 agricultural reservoirs. The assessment was based on the KRC (Korea Rural Community Corporation) 1^st vulnerability assessment methodology using RCP scenarios for 2021. However, there are limitations due to the necessity for climate impact assessments based on the latest climate information and the uncertainties associated with using a single scenario from national standard scenarios. Therefore, we applied the 13 GCM (General Circulation Model) outputs based on the newly introduced SSP scenarios. Furthermore, due to difficulties in data acquisiton, we reassessed potential impacts by redistributing weights for proxy variables. As a main result, with lower future potential impacts observed in areas with higher precipitation along the southern coast. Overall, the potential impacts increased for all reservoirs as we moved into the future, maintaining their relative rankings, yet showing no significant variability in the far future. Although the overall pattern of potential impacts aligns with previous evaluations, reevaluation under similar conditions with different spatial resolutions emphasizes the critical role of meteorological data spatial resolution in assessments. The results of this study are expected to improve the credibility and accuracy formulation of vulnerability employing more scientific predictions.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.272-282
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• 2022

Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.

• Structural Engineering and Mechanics
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• 제69권2호
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• pp.155-162
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• 2019

It is very common to find an empirical formulation in an earthquake design code to calculate fundamental vibration period of a structural system. Fundamental vibration period or frequency is a key parameter to provide adequate information pertinent to dynamic characteristics and performance assessment of a structure. This parameter enables to assess seismic demand of a structure. It is possible to find an empirical formulation related to reinforced concrete structures, masonry towers and slender masonry structures. Calculated natural vibration frequencies suggested by empirical formulation in the literatures has not suits in a high accuracy to the case of rest of the historical masonry bridges due to different construction techniques and wide variety of material properties. For the listed reasons, estimation of fundamental frequency gets harder. This paper aims to present an empirical formulation through Mean Square Error study to find ambient vibration frequency of historical masonry bridges by using a non-linear regression model. For this purpose, a series of data collected from literature especially focused on the finite element models of historical masonry bridges modelled in a full scale to get first global natural frequency, unit weight and elasticity modulus of used dominant material based on homogenization approach, length, height and width of the masonry bridge and main span length were considered to predict natural vibration frequency. An empirical formulation is proposed with 81% accuracy. Also, this study draw attention that this accuracy decreases to 35%, if the modulus of elasticity and unit weight are ignored.

• Computers and Concrete
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• 제32권3호
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• 한국압력기기공학회 논문집
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• 제11권2호
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• pp.20-24
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• 2015

During refueling Outage, In-service inspections(ISIs) for the Nuclear Power Plant components are mandatory requirement in accordance with ASME Code Sec. XI. Especially, in current ultrasonic testing is one of the most important NDT techniques that are used for volumetric examination methods for nuclear power plant components, and accurate sizing of flaw indication by UT is essential to assure the integrity of the components. However, ASME code specifies minimum requirement for vessel examination procedure, and so far many different flaw sizing approaches have been tried to apply. Through the Round Robin Test(RRT), the accuracy of ultrasonic flaw sizing using DAC techniques was measured with the mock-ups simulating typical pressure vessel welds. These mock-ups contain artificially introduced flaws of known size and location. This paper shows experimental comparison data on the accuracy of techniques using such as 6dB drop, 50%DAC, 20%DAC and 20%DAC with beam spread correction, and also shows that diverse DAC techniques can be effectively applied to the assessment of the flaw sizing for pressure vessel welds in the stage of welding and fabrication.

• 한국지진공학회논문집
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• 제23권6호
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• pp.329-339
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• 2019

The strategy for the management of earthquakes is shifting from post recovery to prevention; therefore, seismic performance management requires quantitative predictions of damage and the establishment of strategies for initial responses to earthquakes. Currently, seismic performance evaluation for seismic management in Korea consists of two stages: preliminary evaluation and detailed evaluation. Also, the priority of seismic performance management is determined in accordance with the preliminary evaluation. As a deterministic method, preliminary evaluation quantifies the physical condition and socio-economic importance of a facility by various predetermined indices, and the priority is decided by the relative value of the indices; however, with the deterministic method it is difficult to consider any uncertainty related to the return-year, epicenter, and propagation of seismic energy. Also this method cannot support tasks such as quantitative socio-economic damage and the provision of data for initial responses to earthquakes. Moreover, indirect damage is often greater than direct damage; therefore, a method to quantify damage is needed to enhance accuracy. In this paper, a Seismic Risk Assessment is used to quantify the cost of damage of road facilities in Pohang city and to support decision making.

• Structural Monitoring and Maintenance
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• 제7권4호
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• pp.319-344
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• 2020

Inverse analysis of non-linear reinforced concrete bridge pier using recursive Gaussian filtering for in-situ condition assessment is the main theme of this work. For this purpose, minimum variance unbiased estimation using unscented sigma points is adopted here. The uniqueness of this inverse analysis lies in its approach for strain based updating of engineering demand parameters, where appropriate bound and constrained conditions are introduced to ensure numerical stability and convergence. In this analysis, seismic input is also identified, which is an added advantage for the structures having no dedicated sensors for earthquake measurement. First, the proposed strategy is tested with a simulated example whose hysteretic properties are obtained from the slow-cyclic test of a frame to investigate its efficiency and accuracy. Finally, the experimental test data of a full-scale bridge pier is used to study its in-situ condition in terms of Park & Ang damage index. Overall the study shows the ability of the augmented minimum variance unbiased estimation based recursive time-marching algorithm for non-linear system identification with the aim to estimate the engineering damage parameters that are the fundamental information necessary for any future decision making for retrofitting/rehabilitation.