• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.125-134
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• 2011

The paper is to estimate willingness-to-pay (WTP) for residential water quality improvement in Busan, using non-parametric approach. There are several significant advantages of non-parametric approach, compared to parametric methods. That is, no probability distribution assumption is necessary so that there are no needs to assume or test goodness of fit, model specification and heteroscedasticity statistically. For the reliability and the validity of contingent valuation method a survey was conducted for 665 respondents, who were sampled by stratified random sampling method, by personal interview method. The result of mean WTP for residential water quality improvement in Busan was estimated to be 3,190 won to 3,331 won per month per household, while median WTP being 1,750 won. Provided that our sample is broadly representative of the Busan's population, an estimate of the annual aggregated benefit of residential water improvement for all Busan households is approximately 50.2 billion won in case of mean WTP or 27.5 billion won in case of median WTP.

• Journal of Korea Technology Innovation Society
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• v.11 no.1
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• pp.72-90
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• 2008

What has been the contribution of industrial innovation to economic growth? Typically, the issue has been approached with growth-accounting methods augmented to include a "stock of knowledge". An independent estimate of the rate of return to R&D is found in order to impute patents granted to the accumulation of knowledge. Griliches(1973) then uses a regression approach to assess the effect of an R&D variable on the computed TFP growth rate. The regression coefficient on the R&D variable would provide an estimate of the social rate of return to R&D. The related studies tend to show high social rates of return to R&D, typically in a range of 20 to 40 % per year. We need to provide multiple equation dynamic system for productivity and innovation in Korean economy in state space form. A wide range of time series models, including the classical linear regression model, can be written and estimated as special cases of a state space specification. State space models have been applied in the econometrics literature to model unobserved variables like productivity. Estimation produces the following results. Considering the goodness of fit, we can see that the evidence is strongly in favor of the range $0.120{\sim}0.135$ for the elasticity of TFP to R&D stock in the period between 1970's and the early 2000's.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.35-47
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• 2023

Various single probability distributions have been used to represent time headway distributions. However, it has often been difficult to explain the time headway distribution as a single probability distribution on site. This study used the EM algorithm, which is one of the maximum likelihood estimations, for the parameters of combined mixture distributions with a certain relationship between two normal distributions for the time headway of vehicles. The time headway distribution of vehicle arrival is difficult to represent well with previously known single probability distributions. But as a result of this analysis, it can be represented by estimating the parameters of the mixture probability distribution using the EM algorithm. The result of a goodness-of-fit test was statistically significant at a significance level of 1%, which proves the reliability of parameter estimation of the mixture probability distribution using the EM algorithm.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.768-777
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• 2006

The purpose of this study lies in formulating the landscape preference model using a conditional logit that involves the effect of visual elements as well as landscape itself on landscape preferences. To measure landscape preferences, a photo-questionnaire composed of paired photographs of the Cairngorms National Park of Scotland and the Jirisan National Park of Korea was distributed to visitors to the Jirisan National Park of Korea. Visual elements of landscape quantitatively measured by photogrammetry were reduced to orthogonal principal components that were subsequently used as explanatory variables in a conditional logit. As a result, the mixed conditional logit including the effect of landscape itself satisfied the Independence of Irrelevant Alternatives (IIA) property and showed reliable goodness of fit (${\rho}^2=0.25$). It was concluded that the mixed conditional logit including the effect of landscape itself was appropriate for landscape preference model rather than usual conditional logit excluding the effect.

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.3
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• pp.127-141
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• 2015

Silver-based systems activated by low intensity direct current continue to be investigated as an alternative antimicrobial for infection prophylaxis and treatment. However there has been limited research on the quantitative characterization of the antimicrobial efficacy of such systems. The objective of this study was to develop a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model providing the quantitative relationship between the critical system parameters and the degree of antimicrobial efficacy. First, time-kill curves were experimentally established for a strain of Staphylococcus aureus in a nutrientrich fluid environment over 48 hours. Based on these curves, a modified PK/PD model was developed with two components: a growing silver-susceptible bacterial population and a depreciating bactericidal process. The test of goodness-of-fit showed that the model was robust and had good predictability ($R^2>0.7$). The model demonstrated that the current intensity was positively correlated to the initial killing rate and the bactericidal fatigue rate of the system while the anode surface area was negatively correlated to the fatigue rate. The model also allowed the determination of the effective range of these two parameters within which the system has significant antimicrobial efficacy. In conclusion, the modified PK/PD model successfully described bacterial growth and killing kinetics when the bacteria were exposed to the electrically activated silver-titanium implant system. This modeling approach as well as the model itself can also potentially contribute to the development of optimal design strategies for other similar antimicrobial systems.

• KIEAE Journal
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• v.16 no.3
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• pp.137-143
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• 2016

Purpose: Apartment housing should conduct a cyclic repair to keep and maintain the building performance since they are constructed. Therefore, the repair plan would be provided for long term period which explains the repair time, items and repair cost. Residents of apartment housing are responsible to pay for the repair activities. For repair cost, residents would reserve the money for repair little by little continuously until the required repair time because the repair cost takes a big burden for residents and lots of money a time. But, there is no systematic approach to provide the long term repair cost because it is no proper forecast of the repair cost to the upcoming repair time. In this study, it aimed at providing the monthly accumulation of the long term repair cost with the survey data in Seoul. Method: For these, the surveyed data are classified into 6 categories and number of data are 1,918. In addition, it developed the repair cost model for the 24 repair works and the cumulation function which is reflected with the each cost model. Result: This study are shown as follows : First, among the various estimation for the repair cost, the power function has a goodness of fit in statistics. Second, the monthly accumulation would be 12,840 won/household in size of $100,000m^2$ management area and $81.7won/m^2$ in size of the 1,000 household number during 40 years.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.760-768
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• 2017

The transition temperature shift (TTS) of the reactor pressure vessel materials is an important factor that determines the lifetime of a nuclear power plant. The prediction of the TTS at the end of a plant's lifespan is calculated based on the equation of Regulatory Guide 1.99 revision 2 (RG1.99/2) from the US. The fluence factor in the equation was expressed as a power function, and the exponent value was determined by the early surveillance data in the US. Recently, an advanced approach to estimate the TTS was proposed in various countries for nuclear power plants, and Korea is considering the development of a new TTS model. In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

• Journal of KIISE
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• v.42 no.12
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• pp.1551-1560
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• 2015

Software Reliability Growth Models (SRGMs) are used to quantitatively evaluate software reliability and to determine the software release date or additional testing efforts using software failure data. Because a single SRGM is not universally applicable to all kinds of software, the selection of an optimal SRGM suitable to a specific case has been an important issue. The existing methods for SRGM selection assess the goodness-of-fit of the SRGM in terms of the collected failure data but do not consider the accuracy of future failure predictions. In this paper, we propose a method for selecting SRGMs using the trend of failure data and failure prediction ability. To justify our approach, we identify problems associated with the existing SRGM selection methods through experiments and show that our method for selecting SRGMs is superior to the existing methods with respect to the accuracy of future failure prediction.

• Journal of KIISE:Information Networking
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• v.33 no.3
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• pp.201-217
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• 2006

In this paper, we collected the physical traces from high speed Internet backbone traffic and analyze the various characteristics of the underlying packet traces. Particularly, our work is focused on analyzing the characteristics of an anomalous traffic. It is found that in our data, the anomalous traffic is caused by UDP session traffic and we determined that it was one of the Denial of Service attacks. In this work, we adopted the unsupervised machine learning algorithm to classify the network flows. We apply the k-means clustering algorithm to train the learner. Via the Cramer-Yon-Misses test, we confirmed that the proposed classification method which is able to detect anomalous traffic within 1 second can accurately predict the class of a flow and can be effectively used in determining the anomalous flows.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.9-18
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• 2015

Software Reliability Growth Models (SRGMs) are useful for determining the software release date or additional testing efforts by using software failure data. It is not appropriate for a SRGM to apply to all software. And besides a large number of SRGMs have already been proposed to estimate software reliability measures. Therefore selection of an optimal SRGM for use in a particular case has been an important issue. The existing methods for selecting a SRGM use the entire collected failure data. However, initial failure data may not affect the future failure occurrence and, in some cases, it results in the distorted result when evaluating the future failure. In this paper, we suggest a method for selecting a SRGM based on the evaluation goodness-of-fit using partial data. Our approach uses partial data except for inordinately unstable failure data in the entire failure data. We will find a portion of data used to select a SRGM through the comparison between the entire failure data and the partial failure data excluded the initial failure data with respect to the predictive ability of future failures. To justify our approach this paper shows that the predictive ability of future failures using partial data is more accurate than using the entire failure data with the real collected failure data.