• Geomechanics and Engineering
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• v.24 no.3
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• pp.281-293
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• 2021

Uncertainties in geomechanical input parameters which mainly related to inappropriate data acquisition and estimation due to lack of sufficient calibration information, have led wellbore instability not yet to be fully understood or addressed. This paper demonstrates a workflow of employing Quantitative Risk Assessment technique, considering these uncertainties in terms of rock properties, pore pressure and in-situ stresses to makes it possible to survey not just the likelihood of accomplishing a desired level of wellbore stability at a specific mud pressure, but also the influence of the uncertainty in each input parameter on the wellbore stability. This probabilistic methodology in conjunction with Monte Carlo numerical modeling techniques was applied to a case study of a well. The response surfaces analysis provides a measure of the effects of uncertainties in each input parameter on the predicted mud pressure from three widely used failure criteria, thereby provides a key measurement for data acquisition in the future wells to reduce the uncertainty. The results pointed out that the mud pressure is tremendously sensitive to UCS and SHmax which emphasize the significance of reliable determinations of these two parameters for safe drilling. On the other hand, the predicted safe mud window from Mogi-Coulomb is the widest while the Hoek-Brown is the narrowest and comparing the anticipated collapse failures from the failure criteria and breakouts observations from caliper data, indicates that Hoek-Brown overestimate the minimum mud weight to avoid breakouts while Mogi-Coulomb criterion give better forecast according to real observations.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2609-2626
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• 2023

The hotel industry is an example of experiential services. As consumers cannot fully evaluate the online review content and quality of their services before booking, they must rely on several online reviews to reduce their perceived risks. However, individuals face information overload owing to the explosion of online reviews. Therefore, consumer cognitive fluency is an individual's subjective experience of the difficulty in processing information. Information complexity influences the receiver's attitude, behavior, and purchase decisions. Individuals who cannot process complex information rely on the peripheral route, whereas those who can process more information prefer the central route. This study further discusses the influence of the complexity of review information on hotel ratings using online attraction review data retrieved from TripAdvisor.com. This study conducts a two-level empirical analysis to explore the factors that affect review value. First, in the Peripheral Route model, we introduce a negative binomial regression model to examine the impact of intuitive and straightforward information on hotel ratings. In the Central Route model, we use a Tobit regression model with expert reviews as moderator variables to analyze the impact of complex information on hotel ratings. According to the analysis, five-star and budget hotels have different effects on hotel ratings. These findings have immediate implications for hotel managers in terms of better identifying potentially valuable reviews.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.5
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• pp.107-113
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• 2007

This study presents fragility curves for the wind fragility analysis of a six-span extradosed bridge. The loads and corresponding load combinations are calculated using domestic design codes. Random variables are utilized to considering the uncertainties of the input variables for wind loads. The fragility curve is represented as a log-normal distribution function, in which two parameters are estimated by the maximum likelihood method. The results show that the extradosed bridge is safe to suffer static wind forces.

• Korean Journal of Social Welfare Studies
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• v.40 no.2
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• pp.5-32
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• 2009

The purpose of this study is analyze the pattern and factors affecting the shelter exit and the patterns of homeless women in Korea. For this study, survey data were collected from 139 sheltered homeless women in Seoul in May of 2007. And respondent's exit time and exit pattern from the shelter were investigated through administration data of shelter in December of 2008. Life table analysis, Cox-proportional hazard analysis and competing risk survival analysis were employed in order to analyze data. The major findings were as follows. First, life table analysis shows that the exit ratio of homeless women started to fall sharply in 24 months from entry into shelter. Second, subjective health status, ratio of the homeless in social network and shelter entry with children affected the likelihood of shelter exit of homeless women. Third, age, subjective health status, depression and shelter entry with children affected the likelihood of positive exit. And ratio of the homeless in social network affected the likelihood of negative exit. Based on these findings, this study implied the introduction of case management service concerning individual shelter exit plan and policy for residential stability of homeless women.

• Korean Journal of Environmental Biology
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• v.22 no.1
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• pp.66-74
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• 2004

In order to clarify intra-and inter-specific variation of Korean Rana species, the partial DNA sequences of mitochondrial 16S rDNA gene were determined from 6 Korean and 1 Japanese Rana species, DNA sequences from Korean and Japanese species were comparison-analysed within, and also with the sequences from three species of Japanese brown frogs. DNA similarities were calculated as 91.3∼97.3％ among brown frog (R. amurensis coreana, R. dybowskii and R. huanrenensis), as 96.11∼97.26％ among pond frogs (R. nigromaculata and R. planeyi chosenica). Genetic distance of pond frog and wrinkle fyog (R. rugosa) were near than that of pond frog and brown frog. Two clusters were formed brown frogs and the other group by neigh-bor-joining and maximum-likelihood analysis, also the populations of R. nigromaculata were well distinguished between Korean peninsula and Korean island. But result from maximum-likelihood analysis slightly differed from neighbor-joining to cluster of R. rugosa. Further analyses for their population will be necessary to study the phylogenetic status.

• Tuberculosis and Respiratory Diseases
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• v.47 no.4
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• pp.498-506
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• 1999

Background : The causes of solitary pulmonary nodule are many, but the main concern is whether the nodule is benign or malignant. Because a solitary pulmonary nodule is the initial manifestation of the majority of lung cancer, accurate clinical and radiologic interpretation is important. Bayes' theorem is a simple method of combining clinical and radiologic findings to estimate the probability that a nodule in an individual patients is malignant. We estimated the probability of malignancy of solitary pulmonary nodules with a specific combination of features by Bayesian approach. Method : One hundred and eighty patients with solitary pulmonary nodules were identified from multi-center analysis. The hospital records of these patients were reviewed and patient age, smoking history, original radiologic findings, and diagnosis of the solitary pulmonary nodules were recorded. The diagnosis of solitary pulmonary nodule was established pathologically in all patients. We used to Bayes' theorem to devise a simple scheme for estimating the likelihood that a solitary pulmonary nodule is malignant based on radiological and clinical characteristics. Results : In patients characteristics, the probability of malignancy increases with advancing age, peaking in patients older than 66 year of age(LR : 3.64), and higher in patients with smoking history more than 46 pack years(LR : 8.38). In radiological features, the likelihood ratios were increased with increasing size of the nodule and nodule with lobulated or spiculated margin. Conclusion : In conclusion, the likelihood ratios of malignancy may improve the accuracy of the probability of malignancy, and can be a guide of management of solitary pulmonary nodule.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.833-845
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• 2011

This paper conducts a statistical analysis of extreme values for both daily log-returns and daily negative log-returns, which are computed using a collection of KOSPI data from January 3, 1998 to August 31, 2011. The Poisson-GPD model is used as a statistical analysis model for extreme values and the maximum likelihood method is applied for the estimation of parameters and extreme quantiles. To the Poisson-GPD model is also added the Bayesian method that assumes the usual noninformative prior distribution for the parameters, where the Markov chain Monte Carlo method is applied for the estimation of parameters and extreme quantiles. According to this analysis, both the maximum likelihood method and the Bayesian method form the same conclusion that the distribution of the log-returns has a shorter right tail than the normal distribution, but that the distribution of the negative log-returns has a heavier right tail than the normal distribution. An advantage of using the Bayesian method in extreme value analysis is that there is nothing to worry about the classical asymptotic properties of the maximum likelihood estimators even when the regularity conditions are not satisfied, and that in prediction it is effective to reflect the uncertainties from both the parameters and a future observation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.549-557
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• 2013

An flood inundation map is able to convey spatial distribution of inundation to a decision maker for flood risk management. A roughness coefficient with unclear values and a discharge obtained from the stage-discharge rating equation are key sources of uncertainty in flood inundation mapping by using a hydraulic model. Also, the uncertainty analysis needs an observation for the flood inundation, and satellite images is useful to obtain spatial distribution of flood. Accordingly, the objective of this study is to quantify uncertainty arising roughness and discharge in flood inundation mapping by using a hydraulic model and a satellite image. To perform this, flood inundations were simulated by HEC-RAS and terrain analysis, and ISODATA (Iterative Self-Organizing Data Analysis) was used to classify waterbody from Landsat 5TM imagery. The classified waterbody was used as an observation to calculate F-statistic (likelihood measure) in GLUE (Generalized Likelihood Uncertainty Estimation). The results from GLUE show that flood inundation areas are 74.59 $km^2$ for lower 5 % uncertainty bound and 151.95 $km^2$ for upper 95% uncertainty bound, respectively. The quantification of uncertainty in flood inundation mapping will play a significant role in realizing the efficient flood risk management.

• Genomics & Informatics
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• v.17 no.4
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• pp.37.1-37.8
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• 2019

Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.