• Journal of Radiation Protection and Research
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• v.41 no.3
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• pp.237-244
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• 2016

Background: Korea Atomic Energy Research Institute is developing a fission product transport module for predicting the behavior of radioactive materials in the primary cooling system of a nuclear power plant as a separate module, which will be connected to a severe accident analysis code, Core Meltdown Progression Accident Simulation Software (COMPASS). Materials and Methods: This fission product transport (COMPASS-FP) module consists of a fission product release model, an aerosol generation model, and an aerosol transport model. In the fission product release model there are three submodels based on empirical correlations, and they are used to simulate the fission product gases release from the reactor core. In the aerosol generation model, the mass conservation law and Raoult's law are applied to the mixture of vapors and droplets of the fission products in a specified control volume to find the generation of the aerosol droplet. In the aerosol transport model, empirical correlations available from the open literature are used to simulate the aerosol removal processes owing to the gravitational settling, inertia impaction, diffusiophoresis, and thermophoresis. Results and Discussion: The COMPASS-FP module was validated against Aerosol Behavior Code Validation and Evaluation (ABCOVE-5) test performed by Hanford Engineering Development Laboratory for comparing the prediction and test data. The comparison results assuming a non-spherical aerosol shape for the suspended aerosol mass concentration showed a good agreement with an error range of about ${\pm}6%$. Conclusion: It was found that the COMPASS-FP module produced the reasonable results of the fission product gases release, the aerosol generation, and the gravitational settling in the aerosol removal processes for ABCOVE-5. However, more validation for other aerosol removal models needs to be performed.

• Journal of IKEEE
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• v.18 no.2
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• pp.240-247
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• 2014

Intraocular pressure is measured after a cornea air puff by observing biomechanical properties such as thickness or displacement of the cornea. In this paper, we deal with a frequency domain analysis of corneal deformation in the air puff tonometry that is used to diagnose glaucoma or lasik. We distinguish the patient from the normal by measuring the oscillation frequency in the neighborhood of the central cornea section. A binary image was obtained from the video images, and cornea vertical oscillation profile was extracted from the difference between the vertical displacement data and the curve fitting. In terms of Fourier transform, a vibration frequency of 479.2Hz for the patient was obtained as well as more higher 702.8Hz for the normal due to stiffness. Hilbert-Huang transform's empirical mode decomposition generally describes local, nonlinear, and nonstationary data. After the data were decomposed into intrinsic mode functions, a spectrum and power were analysed. Finally, we confirm that the patient has 6 times more higher power ratio for the specific intrinsic mode function between the patient and the normal.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.49-68
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• 2008

The purpose of this paper is to extensively review the condition monitoring (CM) techniques using empirical models in an effort to reduce or eliminate unexpected downtimes in general industry, and to illustrate the feasibility of applying them to the nuclear industry. CM provides on-time warnings of system states to enable the optimal scheduling of maintenance and, ultimately, plant uptime is maximized. Currently, most maintenance processes tend to be either reactive, or part of scheduled, or preventive maintenance. Such maintenance is being increasingly reported as a poor practice for two reasons: first, the component does not necessarily require maintenance, thus the maintenance cost is wasted, and secondly, failure catalysts are introduced into properly working components, which is worse. This paper first summarizes the technical aspects of CM including state estimation and state monitoring. The mathematical background of CM is mature enough even for commercial use in the nuclear industry. Considering the current computational capabilities of CM, its application is not limited by technical difficulties, but by a lack of desire on the part of industry to implement it. For practical applications in the nuclear industry, it may be more important to clarify and quantify the negative impact of unexpected outcomes or failures in CM than it is to investigate its advantages. In other words, while issues regarding accuracy have been targeted to date, the concerns regarding robustness should now be concentrated on. Standardizing the anticipated failures and the possibly harsh operating conditions, and then evaluating the impact of the proposed CM under those conditions may be necessary. In order to make the CM techniques practical for the nuclear industry in the future, it is recommended that a prototype CM system be applied to a secondary system in which most of the components are non-safety grade. Recently, many activities to enhance the safety and efficiency of the secondary system have been encouraged. With the application of CM to nuclear power plants, it is expected to increase profit while addressing safety and economic issues.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.119-132
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• 2021

This study focused on examining the impact of the social context that causes gender differences in entrepreneurship, not the phenomenon itself. Specifically, this study verified the moderating effect of the social context on the relationship between gender and entrepreneurship using data from 20 countries in the Global Entrepreneurship Trend Report (GETR). In order to test hypotheses involving social context implications, Hofstede's cultural dimension factors such as power distance, individualism, masculinity, and uncertainty avoidance variables, and institutional factors such as gender equality and social security are used as specific variables reflecting the social context. Empirical analysis through GLM found that gender did not independently influence entrepreneurship, and gender had a significant effect by interacting with power distance, individualism, uncertainty avoidance, gender equality, and social security variables, respectively. Such empirical results show that the gender difference in entrepreneurship is not due to the unique characteristics inherent in each gender but on the level of the country's social context to which the individual belongs.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.13-23
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• 2022

In this study, from January 2015 to April 2020, we propose a stochastic volatility model to capture the leverage effect on daily freight yields in the dry cargo market and analyze the freight yields. Estimation involving the Bayesian Markov Chain Monte Carlo method for the leverage effect based on the negative correlation that exists between returns and volatility in stochastic volatility analysis yields similar estimates, and the statistcs indicates significant. That is, the results of the empirical analysis show that the degree of correlation between returns and volatility, and the magnitude and sign of fluctuations differ, which suggests that taking into account the leverage effect in the SV model improves the goodness of fit of the estimates. In addition to the statistical significance of the estimated model's leverage effect, the analysis by log predictive power score presents the estimated results with improved predictive power of the model considering the leveraged effect. These astatistically significant empirical results show that the stochastic volatility model considering the leverage effect is important for freight rate risk modeling in the marine industry.

• Smart Structures and Systems
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• v.33 no.2
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Management & Information Systems Review
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• v.31 no.1
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• pp.89-116
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• 2012

This paper is an empirical study for the listed manufacturing companies in the Korea Stock Exchange during the sample period(2001-2010). The research is based on the target adjustment model(Shyam-Sunder and Myers(1999)) and the pecking order model(Frank and Goyal(2003)), and is aimed at reflecting the critical viewpoint of Chirinko and Singha(2000). An analysis in the model of Shyam-Sunder and Myers(1999) shows the value is too low to support the pecking order model in view of the following results. A target adjustment coefficient value is between 0 and 1, and is significant variable and explanatory power is very high, while deficit-in-funds coefficients close to 0. In addition, the result of an empirical test following the methodology used by Frank and Goyal(2003) does not support the pecking order theory.

• Journal for History of Mathematics
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• v.30 no.4
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• pp.247-258
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• 2017

The goal of this essay is to examine metaphors for mathematics and to discuss philosophical problems related to them. Two metaphors for mathematics are well known. One is a tree and the other is a building. The former was proposed by Pasch, and the latter by Hilbert. The difference between these metaphors comes from different philosophies. Pasch's philosophy is a combination of empiricism and deductivism, and Hilbert's is formalism whose final task is to prove the consistency of mathematics. In this essay, I try to combine two metaphors from the standpoint that 'mathematics is a part of the ecosystem of science', because each of them is not good enough to reflect the holistic mathematics. In order to understand mathematics holistically, I suggest the criteria of the desirable philosophy of mathematics. The criteria consists of three categories: philosophy, history, and practice. According to the criteria, I argue that it is necessary to pay attention to Pasch's philosophy of mathematics as having more explanatory power than Hilbert's, though formalism is the dominant paradigm of modern mathematics. The reason why Pasch's philosophy is more explanatory is that it contains empirical nature. Modern philosophy of mathematics also tends to emphasize the empirical nature, and the synthesis of forms with contents agrees with the ecological analogy for mathematics.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.3
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• pp.75-85
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• 2014

This paper is to develop and estimate a closed form inflation model using the estimates for real marginal costs in manufacturing industries during the sample period 1975-2010. The production function in manufacturing industry incorporates labor, capital, domestic material, and foreign material, assuming constant returns to scale technology and AR(1) process of technological coefficient. We derive real marginal costs from firm's cost minimization with quarterly data and provide new evidences on the new Keynesian Phillips curve for Korea. The main empirical result is that the closed form coefficients ${\delta}_1$ and ${\delta}^{-1}_2$ in manufacturing for PPI inflation proved to be 0.5086 and 0.8779 respectively, similar to the estimates in the U.S. case. These results also are consistent with the functional relationship between the coefficients in hybrid model and its closed form. Thus the paper suggests that the empirical studies on inflation dynamics need to focus on the manufacturing industry with market power, treating PPI inflation as the dependent variable.