• Nuclear Engineering and Technology
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• 제40권4호
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• pp.299-304
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• 2008

A detection system was set up to measure the neutron generation rate of a recently developed D-D neutron generator. The system is composed of a Si detector, He-3 detector, and electronics for pulse height analysis. The neutron generation rate was measured by counting protons using the Si detector, and the data was crosschecked by counting neutrons with the He-3 detector. The efficiencies of the Si and He-3 detectors were calibrated independently by using a standard alpha particle source $^{241}Am$ and a bare isotopic neutron source $^{252}Cf$, respectively. The effect of the cross-sectional difference between the D(d,p)T and $D(d,n)^3He$ reactions was evaluated for the case of a thick target. The neutron generation rate was theoretically corrected for the anisotropic emission of protons and neutrons in the D-D reactions. The attenuations of neutron on the path to the He-3 detector by the target assembly and vacuum flange of the neutron generator were considered by the Monte Carlo method using the MCNP 4C2 code. As a result, the neutron generation rate based on the Si detector measurement was determined with a relative uncertainty of ${\pm}5%$, and the two rates measured by both detectors corroborated within 20%.

• 분석과학
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• 제34권5호
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• pp.202-211
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• 2021

Volatile organic compounds (VOCs) in hazardous air pollutants (HAPs) have been regulated by the Air Pollution Control Act (1978) and their atmospheric concentrations have been monitored in 39 monitor sites in Korea. However, measurement standards of volatile organic compounds (VOCs) in HAPs at ambient levels have not been established in Korea. Primary reference gas mixtures (measurement standards) at ambient levels are required for accurately monitoring atmospheric VOCs in HAPs and managing their emissions. In this study, primary reference gas mixtures (PRMs) at 5 nmol/mol were developed in order to establish primary national standards of VOCs in HAPs at ambient levels. Primary reference gas mixtures (PRMs) were prepared in pressurized aluminum cylinders with special internal surface treatment using gravimetric method. Analytical methods using gas chromatography-flame ionization detector (GC-FID) coupled with a cryogenic preconcentrator were also developed to verify the consistency of gravimetrically prepared HAP VOCs PRMs. Three different columns installed in the GC-FID were evaluated and compared for the retention times and separation of eighteen target components in a chromatogram. Results show that the HAP VOCs PRMs at 5 nmol/mol were consistent within a relative expanded uncertainty (k=2) of less than 3 % except acrylonitrile (less than 6 %) and the 18 VOCs were stable for 1 year within their associated uncertainties.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.561-573
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• 2021

Knowing how to build and maintain consumer trust is crucial for e-commerce. Despite the number of empirical studies that have explored the factors that influence consumer trust, none of them considers the relative importance of different antecedents and how they interact to influence consumer trust. Therefore, based on the integrated Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) approaches, we establish a hierarchical structural model, which not only demonstrates the intensity of the relationships but also identifies the interdependence among the drivers of consumer trust in E-commerce. The findings confirm that propensity to trust is the most important determinant of consumer trust. The brand-related factors and platform-related factors are prominent in the process of building trust as they influence consumer trust indirectly through propensity to trust. Geographic location, demographic variables, and high security are identified as the root causes that affect consumer trust through other trust antecedents. Furthermore, the findings of this study offer valuable insights into an important element of e-commerce and provide a useful platform for future research. More represented samples and factors are encouraged for further research to ensure research fairness and minimize consumer distrust and uncertainty.

• 한국축산식품학회지
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• 제41권5호
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• pp.816-825
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• 2021

This study aims to validate a fast method of simultaneous analysis of 365 LCamenable and 142 GC-amenable pesticides in hen eggs by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS), respectively, operating in multiple reaction monitoring (MRM) acquisition modes. The sample preparation was based on quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction. Key method performance parameters investigated were specificity, linearity, limit of quantification (LOQ), accuracy, precision and measurement uncertainty. The method was validated at two spiking levels (10 and 50 ㎍/kg), and good recoveries (70%-120%) and relative standard deviations (RSDs) (≤20) were achieved for 92.9% of LC-amenable and 86.6% of GC-amenable pesticide residues. The LOQs were ≤10 ㎍/kg for 94.2% of LC-amenable and 92.3% of GC-amenable pesticides. The validated method was further applied to 100 egg samples from caged hens, and none of the pesticides was quantified.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3897-3908
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• 2022

High-fidelity nuclear data libraries and neutronics simulation tools are essential for the development of fast reactors. The IAEA coordinated research project on "Neutronics Benchmark of CEFR Start-Up Tests" offers valuable data for the qualification of nuclear data libraries and neutronics codes. This paper focuses on the verification and validation of the CEFR start-up modelling using OpenMC Monte-Carlo code against the experimental measurements. The OpenMC simulation results agree well with the measurements in criticality, control rod worth, sodium void reactivity, temperature reactivity, subassembly swap reactivity, and reaction distribution. In feedback coefficient evaluations, an additional state method shows high consistency with lower uncertainty. Among 122 relative errors in the benchmark of the distribution of nuclear reaction, 104 errors are less than 10% and 84 errors are less than 5%. The results demonstrate the high reliability of OpenMC for its application in fast reactor simulations. In the companion paper, the influence of cross-section libraries is investigated using neutronics modelling in this paper.

• 대한조선학회논문집
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• 제59권6호
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• pp.338-344
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• 2022

Roll moment usually is ignored when analyzing the maneuverability of surface ships. However, it is well known that the influence of roll moment on maneuverability is significant for ships with small metacentric height such as container ships, passenger ships, etc. In this study, a pure roll test is performed to determine the hydrodynamic derivatives with respect to roll motion as added mass and damping. The target ship is an autonomous surface ship designed to carry containers with a small drift and large freeboard. The commercial code of STAR CCM+ software is applied as a specialized tool in naval hydrodynamic based on RANS equation for simulating the pure roll of the ship. The numerical uncertainty analysis is conducted to verify the numerical accuracy. By distinguishing the in-phase and out-of-phase from hydrodynamic forces and moments due to roll motion, added mass derivatives and damping derivatives relative to roll angular velocity are obtained.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.225-237
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• 2022

The term "artificial intelligence" is considered a component of sophisticated technological developments, and several intelligent tools have been developed to assist organizations and entrepreneurs in making business decisions. Artificial intelligence (AI) is defined as the concept of transforming inanimate objects into intelligent beings that can reason in the same way that humans do. Computer systems can imitate a variety of human intelligence activities, including learning, reasoning, problem-solving, speech recognition, and planning. This study's objective is to provide responses to the questions: Which factors should be taken into account while deciding whether or not to use AI applications? What role do these elements have in AI application adoption? However, this study proposes a framework to explore the significance and relation of success factors to AI adoption based on the technology-organization-environment model. Ten critical factors related to AI adoption are identified. The framework is empirically tested with data collected by mail surveying organizations in Vietnam. Structural Equation Modeling is applied to analyze the data. The results indicate that Technical compatibility, Relative advantage, Technical complexity, Technical capability, Managerial capability, Organizational readiness, Government involvement, Market uncertainty, and Vendor partnership are significantly related to AI applications adoption.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 춘계학술대회
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• pp.204-206
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• 2022

The global economy and the air transport business have been affected since the spread of the COVID-19 pandemic. As countries tighten restrictions on international movements, the growing emphasis on air cargo puts pressure on airports to maintain and upgrade their cargo policies, facilities, and operations. Hence, ensuring the competitiveness of cargo airports becomes pivotal for airports survival under the volatile global demand. The study aims to evaluate the importance of the competitiveness factors for cargo airports and identify areas for further improvement. The study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors: 'Transport Capacity,' 'Airport Operations and Facility Capacity,' 'Economic Growth,' 'Financial Performance,' and 'Airport Brand Value.' The selected airports include Heathrow Airport, Aéroport de Paris-Charles de Gaulle, Hong Kong International Airport, and Incheon International Airport. The results identify 'Transport Capacity' as the most significant competitiveness factor, and Hong Kong International Airport the best performing cargo airport. This research forms a reference framework for evaluating cargo airports' competitive position, which may help identify airports' relative strengths and weaknesses. Moreover, this framework can also serve as a tool facilitating the strategic design of airports that may accommodate both air cargo and passenger demand flexibly under the demand uncertainty.

• 한국지구과학회지
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• 제36권6호
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• pp.567-579
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• 2015

중규모 기상 모델을 이용하여 안개와 같은 미세규모 국지현상을 정확히 재현하는 것은 매우 어려운 실정이다. 특히, 수치모델의 초기 입력 자료의 불확도는 수치모델의 예측 정확도에 결정적인 영향을 미치기 때문에 이를 보완하기 위한 자료동화 과정이 요구되어진다. 본 연구에서는 WRF (Weather Research and Forecasting) 모델을 이용하여 낙동강 지역에서 발생한 여름철 안개사례 재현실험을 대상으로 중규모 기상 모델의 한계를 검증하였다. 중규모 기상 모델에서 초기 및 경계장으로 사용되는 KLAPS (Korea Local Analysis and Prediction System)와 LDAPS (Local Data Assimilation and Prediction System) 분석장 자료를 이용하여 수치모델 모의 정확도 민감도 분석을 수행하였다. 또한 AWS (Automatic Weather System) 자료를 이용한 자료동화(Four-Dimensional Data Assimilation)에 의한 수치모델의 정확도 개선 정도를 평가하였다. 초기 및 경계장 민감도 분석 결과에서 LDAPS 자료를 입력 자료로 사용한 경우가 KLAPS 자료 보다 기온과 이슬점온도, 상대습도에서 높은 정확도를 보였고, 풍속은 더 낮은 수준을 나타내었다. 특히, 상대습도에서 LDAPS의 경우는 RMSE (Root Mean Square Error)가 15.9%, KLAPS는 35.6%의 수준을 보여 그 차이가 매우 크게 나타났다. 또한 자료동화를 통하여 기온, 풍속, 상대습도의 RMSE가 각각 $0.3^{\circ}C$, $0.2ms^{-1}$, 2.2% 수준으로 개선되었다.