• Progress in Medical Physics
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• v.21 no.2
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• pp.192-200
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• 2010

Cyberknife with small field size is more difficult and complex for dosimetry compared with conventional radiotherapy due to electronic disequilibrium, steep dose gradients and spectrum change of photons and electrons. The purpose of this study demonstrate the usefulness of Geant4 as verification tool of measurement dose for delivering accurate dose by comparing measurement data using the diode detector with results by Geant4 simulation. The development of Monte Carlo Model for Cyberknife was done through the two-step process. In the first step, the treatment head was simulated and Bremsstrahlung spectrum was calculated. Secondly, percent depth dose (PDD) was calculated for six cones with different size, i.e., 5 mm, 10 mm, 20 mm, 30 mm, 50 mm and 60 mm in the model of water phantom. The relative output factor was calculated about 12 fields from 5 mm to 60 mm and then it compared with measurement data by the diode detector. The beam profiles and depth profiles were calculated about different six cones and about each depth of 1.5 cm, 10 cm and 20 cm, respectively. The results about PDD were shown the error the less than 2% which means acceptable in clinical setting. For comparison of relative output factors, the difference was less than 3% in the cones lager than 7.5 mm. However, there was the difference of 6.91% in the 5 mm cone. Although beam profiles were shown the difference less than 2% in the cones larger than 20 mm, there was the error less than 3.5% in the cones smaller than 20 mm. From results, we could demonstrate the usefulness of Geant4 as dose verification tool.

• Journal of the Korea Institute of Building Construction
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• v.19 no.1
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• pp.67-76
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• 2019

The domestic construction industry is changing according to its preference for demand and supply along with urbanization and economic development. Accordingly, initial risk assessments is more important than before. In particular, demand for lease-based investment products such as commercial and office buildings has surged as a substitute for financial products due to low interest rates of banks. Therefore, the objective is to suggest a basic study on financial analysis model development by applying optimization method in residential officetel. To achieve the objective, first, the previous studies are investigated. Second, the causal loop diagram is structured based on the collected data. Third, the system dynamics method is used to develop cost-income simulation and optimization model sequentially. Finally, the developed model was verifed through analyzing a case project. In the future, the proposed model can be helpful whether or not conduct execution of an officetel development project to the decision makers.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.213-227
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• 2019

In this paper, we propose an end-to-end deep learning model combining Bayesian neural network with Korean speech recognition. In the past, Korean speech recognition was a complicated task due to the excessive parameters of many intermediate steps and needs for Korean expertise knowledge. Fortunately, Korean speech recognition becomes manageable with the aid of recent breakthroughs in "End-to-end" model. The end-to-end model decodes mel-frequency cepstral coefficients directly as text without any intermediate processes. Especially, Connectionist Temporal Classification loss and Attention based model are a kind of the end-to-end. In addition, we combine Bayesian neural network to implement the end-to-end model and obtain Monte Carlo estimates. Finally, we carry out our experiments on the "WorimalSam" online dictionary dataset. We obtain 4.58% Word Error Rate showing improved results compared to Google and Naver API.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.247-259
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• 2007

In this study, we examined the flux of carbon and water using an eco-hydrological model, Regional Hydro-Ecologic Simulation System (RHESSys). Our purposes were to develop a set of parameters optimized for a well-designed experimental watershed (Gwangneung Research Watershed, GN) and then, to test suitability of the parameters for predicting carbon and water fluxes of other watershed with different regimes of climate, topography, and vegetation structure (i.e Gangseonry Watershed in Mt. Jumbong, GS). Field datasets of stream flow, soil water content (SWC), and wood biomass product (WBP) were utilized for model parameterization and validation. After laborious parameterization processes, RHESSys was validated with the field observations from the GN watershed. The parameter set identified at the GN watershed was then applied to the GS watershed in Mt. Jumbong, which resulted in good agreement for SWC but poor predictability for WBP. Our study showed that RHESSys simulated reliable SWC at the GS by adjusting site-specific porosity only. In contrast, vegetation productivity would require more rigorous site-specific parameterization and hence, further study is necessary to identify primary field ecophysiological variables for enhancing model parameterization and application to multiple watersheds.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Korean Journal of Construction Engineering and Management
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• v.7 no.6
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• pp.175-184
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• 2006

This study focused on developing Life Cycle Cost(LCC) analysis model for selecting sludge collectors in wastewater treatment system and applying the model to a case study. Cost items are examined through literature review and historical data of a facility. Analysis period, discount rate, energy cost escalation ratio are assumed to reasonable level. Monetary evaluation is performed using historical data and estimations from vendors. Sensitive analysis is executed using Monte Carlo Simulation for assumed factors. Interviews with operators, vendors, constructors, managers are conducted to define factors which indicates ease of maintenance, ease of delivery, technical performance, efficiency, environmental friendship. Factors are representing technical and social factors. Results from LCC analysis and qualitative analysis are evaluate together with Weighted Matrix Evaluation Methods for optimum alternative of sludge collectors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.6 no.4
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• pp.137-152
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• 2011

The purpose of this study is to estimate efficiency of agricultural business corporations using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generate efficiency estimates through Monte Carlo simulation re-sampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of the corporations is 0.749 0.790, 0.948 respectively. Among the 692 agricultural business corporations, the number of Increasing Returns to Scale (IRS)-type corporations was analyzed to be 539(77.9%). The number of Constant Returns to Scale (CRS)-type corporations was 108(15.6%), and that of Decreasing Returns to Scale (DRS)-type corporations was 45(6.5%). Since an increase in input is lower than an increase in output in IRS, an increase in input factors such as new investments is required. The Tobit model suggests that the type of corporation, capital level, and period of operation affect the efficiency score more than others. The positive coefficient of capital level and period of operation variable indicates that efficiency score increases as capital level and period of operation increases.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.139-169
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• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

• Journal of Korea Port Economic Association
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• v.28 no.3
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• pp.235-257
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• 2012

Recently the role of ports has been changed to satisfy flexibly needs of demands in global economy. A new concept for ports is not just a place for international trade but an important nodal point in logistics chain. The changing environment like this trend creates a high degree of uncertainty and leaves port managers confused with the question how to respond effectively to dynamic market. The latest studies provide that the port must have a good hinterland to achieve competitive advantages in a logistics chain. Korean Government announced "The Master Development Plan for Port Logistics Parks in Korea" in 2006. This contains the plan of hinterland construction of Busan New Port to achieve the status of logistics hub in Asian market. Previous studies rely solely on traditional DCF(discounted cash flow) analysis for investment of hinterland. However DCF method does not include irreversibility, uncertainty and the choice of timing for investment project. This thesis introduces a ROPM(real options pricing model) which overcomes the limitations of traditional valuation methods. The option valuations in this study utilize the Black-Scholes model, the binomial model and the MonteCarlo simulation to value investment opportunity of a port hinterland. In this thesis, an attempt is made to modify the NPV criterion by incorporating the real options approach, and its application is demonstrated in a hinterland construction investment plan. This research has conducted an empirical analysis by calculating economic value of the investment for a hinterland of Busan New Port.

• Asia Marketing Journal
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• v.15 no.3
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• pp.53-77
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• 2013

As advertising and promotions are categorized as operating expenses, managers tend to reduce marketing budget to improve their short term profitability. Gauging the value and accountability of marketing spending is therefore considered as a major research priority in marketing. To respond this call, recent studies have documented that financial market reacts positively to a firm's marketing activity or marketing related outcomes such as brand equity and customer satisfaction. However, prior studies focus on the relation of marketing variable and financial market variables. This study suggests a channel about how marketing activity increases firm valuation. Specifically, we propose that a firm's marketing activity increases the level of the firm's product market information and thereby the dispersion in financial analysts' earnings forecasts decreases. With less uncertainty about the firm's future prospect, the firm's managers and shareholders have less information asymmetry, which reduces the firm's cost of capital and thereby increases the valuation of the firm. To our knowledge, this is the first paper to examine how informational benefits can mediate the effect of marketing activity on firm value. To test whether marketing activity contributes to increase in firm value by mitigating information asymmetry, this study employs a longitudinal data which contains 12,824 firm-year observations with 2,337 distinct firms from 1981 to 2006. Firm value is measured by Tobin's Q and one-year-ahead buy-and-hold abnormal return (BHAR). Following prior literature, dispersion in analysts' earnings forecasts is used as a proxy for the information gap between management and shareholders. For model specification, to identify mediating effect, the three-step regression approach is adopted. All models are estimated using Markov chain Monte Carlo (MCMC) methods to test the statistical significance of the mediating effect. The analysis shows that marketing intensity has a significant negative relationship with dispersion in analysts' earnings forecasts. After including the mediator variable about analyst dispersion, the effect of marketing intensity on firm value drops from 1.199 (p < .01) to 1.130 (p < .01) in Tobin's Q model and the same effect drops from .192 (p < .01) to .188 (p < .01) in BHAR model. The results suggest that analysts' forecast dispersion partially accounts for the positive effect of marketing on firm valuation. Additionally, the same analysis was conducted with an alternative dependent variable (forecast accuracy) and a marketing metric (advertising intensity). The analysis supports the robustness of the main results. In sum, the results provide empirical evidence that marketing activity can increase shareholder value by mitigating problem of information asymmetry in the capital market. The findings have important implications for managers. First, managers should be cognizant of the role of marketing activity in providing information to the financial market as well as to the consumer market. Thus, managers should take into account investors' reaction when they design marketing communication messages for reducing the cost of capital. Second, this study shows a channel on how marketing creates shareholder value and highlights the accountability of marketing. In addition to the direct impact of marketing on firm value, an indirect channel by reducing information asymmetry should be considered. Potentially, marketing managers can justify their spending from the perspective of increasing long-term shareholder value.