• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.444-447
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• 1995

The satellite in a circular orbit about a planet with disturbances and a three-degree-of-freedom (3DOF) structure under seismic excitations are modeled by the linear stochastic differential equations. Then the risk-sensitive optimal control method is applied to those equations. The mean and the variance of the cost function varies with respect to the risk-sensitivity parameter, .gamma.$_{RS}$ . For a particular risk-sensitivity parameter value, risk-sensitive control reduces to LQG control. Furthermore, the derivation of the mean square value of the state and control action are given for a finite-horizon full-state-feedback risk-sensitive control system. The risk-sensitive controller outperforms a classical LQG controller in the mean square sense of the state and the control action.

• Korean Journal of Construction Engineering and Management
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• v.9 no.1
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• pp.143-154
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• 2008

Since the concerns for safety of highway traffic safety facilities inherent in various environmental risk is increased, systematic performance, cost, and effect analysis process is needed for this. In case of median barrier among various traffic safety facilities, quantitative risk assessment is inevitable because it has lots of direct/indirect risk factors. Thus, this study suggests an advanced VE(Value Engineering) approach incorporating quantitative risk analysis. For the applicability, suggested VE approach considering alternative 1(140cm) and 2(127cm) is applied to median barrier in fields. Also, major improvement objects are extracted from governing factors of cost and performance based on functional analysis. It is concluded that the proposed risk assessment methodology will provide rational and practical solutions for best value and the approach could effectively applied for various traffic safety facilities by slight modification of suggest process.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.97-119
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• 2016

Motion-sensing interface enhances the sense of reality of user experience in virtual reality context. This study analyzes the innovation resistance and adoption structure for Leap Motion, which provides a motion-sensing function, primarily considering the theory of perceived risk. Previous research regarding innovation resistance and adoption mainly addressed the resultant aspects of perceived risk, or the impact of perceived value on the adoption intention. This study synthetically reviews previous studies from a multi-dimensional view considering both resistance- and adoption-perspective. To do so, we identified important antecedents that affect perceived risk and value, and we analyzed the compound dynamics of perceived risk and value towards innovation resistance. As a result, we found that the antecedents included in the existent acceptance models from adoption-perspective can help reduce the level of perceived risk, and that higher perceived value leads to lower innovation resistance. Additionally, trialability can rather foster the perceived risk.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.597-607
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• 2011

Value at Risk(VaR) is being widely used as a simple tool for measuring financial risk. Although VaR has a few weak points, it is used as a basic risk measure due to its simplicity and easiness of understanding. However, it becomes very difficult to estimate the volatility of the portfolio (essential to compute its VaR) when the number of assets in the portfolio is large. In this case, we can consider the application of a dimension reduction technique; however, the ordinary factor analysis cannot be applied directly to financial data due to autocorrelation. In this paper, we suggest a dimension reduction method that uses the time-series factor analysis and DCC(Dynamic Conditional Correlation) GARCH model. We also compare the method using time-series factor analysis with the existing method using ordinary factor analysis by backtesting the VaR of real data from the Korean stock market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4531-4544
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• 2021

Because the traditional network information security vulnerability risk assessment method does not set the weight, it is easy for security personnel to fail to evaluate the value of information security vulnerability risk according to the calculation value of network centrality, resulting in poor evaluation effect. Therefore, based on the network security data element feature system, this study designed a quantitative assessment method of network information security vulnerability detection risk under single transmission state. In the case of single transmission state, the multi-dimensional analysis of network information security vulnerability is carried out by using the analysis model. On this basis, the weight is set, and the intrinsic attribute value of information security vulnerability is quantified by using the qualitative method. In order to comprehensively evaluate information security vulnerability, the efficacy coefficient method is used to transform information security vulnerability associated risk, and the information security vulnerability risk value is obtained, so as to realize the quantitative evaluation of network information security vulnerability detection under single transmission state. The calculated values of network centrality of the traditional method and the proposed method are tested respectively, and the evaluation of the two methods is evaluated according to the calculated results. The experimental results show that the proposed method can be used to calculate the network centrality value in the complex information security vulnerability space network, and the output evaluation result has a high signal-to-noise ratio, and the evaluation effect is obviously better than the traditional method.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.1-8
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• 2015

The data of fair value hierarchy is expected to contain different degree of measurement error, information asymmetry, and information risk by the level of hierarchy. Thus, this study examines how hierarchy of fair value discriminately influences on companies' cost of capital. Through regression analysis of corporations listed from 2011 to 2014, it turns out that the regression coefficient of level 1 and 2 of fair value variable vary their rank by cost of capital types, while level 3 contains the highest regression coefficient for every cost of capital variable. In addition, further study of how the relevance between cost of capital and the fair value hierarchy gets affected by market risk management level and audit quality finds no consistent results. However, by analyzing the effect of coincident interaction through the convergence of market risk management and audit quality, when audit quality and market risk management level are high, the effect of relieving cost of capital of Level 3 gets the highest. In conclusion, fair value hierarchy data seems to affect discriminately on cost of capital by involved information risk, and the information risk could decrease by the level of market risk management and audit quality.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.483-494
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• 2013

The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.

• Nuclear Engineering and Technology
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• v.29 no.1
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• pp.78-84
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• 1997

The specific purpose of this study is to develop the numerical guide for the cost-benefit analysis of ORE ($/person-Sv reduction) to meet the criterion of ALARA in the design stage of the KNGR. In deriving the guide, the risk factor which is defined by the risk to unit collective radiation exposure dose (deaths/person-Sv) and the monetary value of human life ($/death) are required. The risk factor has been estimated from various clinical data accumulated for a number of years and continuously modified. And the monetary value of human life is usually quantified using the human capital approach. In this study, the risk to radiation exposure perceived by a group of people is investigated through an extensive poll survey conducted among university students in order to modify the existing risk factor for radiation exposure. And in evaluating the monetary value of human life, the QOL factor is introduced in order to incorporate the degree of public welfare or quality of life. As a result of study, a value within the range of 151, 000~172, 000 dollars per person-Sv reduction is recommended as the appropriate interim numerical guide for cost-benefit analysis of ORE to meet the criterion of ALARA in the design stage of the KNGR. A poll survey was also conducted in order to see whether the public acceptance cost of nuclear power should be incorporated in developing the guide, and the result of study shooed that such a cost does not need to be considered.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.689-697
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• 2016

The most useful method for financial market risk management may be Value at Risk (VaR) which estimates the maximum loss amount statistically. The VaR is used as a risk measure for one industry. Many real cases estimate VaRs for many industries or nationwide industries; consequently, it is necessary to estimate the VaR for multivariate distributions when a specific portfolio is established. In this paper, the multivariate quantile vector is proposed to estimate VaR for multivariate distribution, and the Vector at Risk for multivariate space is defined based on the quantile vector. When a weight vector for a specific portfolio is given, one point among Vector at Risk could be found as the best VaR which is called as an alternative VaR. The alternative VaR proposed in this work is compared with the VaR of Morgan with bivariate and trivariate examples; in addition, some properties of the alternative VaR are also explored.