• Journal of Radiation Protection and Research
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• v.41 no.2
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• pp.149-154
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• 2016

Background: Any real application of Bayesian inference must acknowledge that both prior distribution and likelihood function have only been specified as more or less convenient approximations to whatever the analyzer's true belief might be. If the inferences from the Bayesian analysis are to be trusted, it is important to determine that they are robust to such variations of prior and likelihood as might also be consistent with the analyzer's stated beliefs. Materials and Methods: The robust Bayesian inference was applied to atmospheric dispersion assessment using Gaussian plume model. The scopes of contaminations were specified as the uncertainties of distribution type and parametric variability. The probabilistic distribution of model parameters was assumed to be contaminated as the symmetric unimodal and unimodal distributions. The distribution of the sector-averaged relative concentrations was then calculated by applying the contaminated priors to the model parameters. Results and Discussion: The sector-averaged concentrations for stability class were compared by applying the symmetric unimodal and unimodal priors, respectively, as the contaminated one based on the class of ${\varepsilon}$-contamination. Though ${\varepsilon}$ was assumed as 10%, the medians reflecting the symmetric unimodal priors were nearly approximated within 10% compared with ones reflecting the plausible ones. However, the medians reflecting the unimodal priors were approximated within 20% for a few downwind distances compared with ones reflecting the plausible ones. Conclusion: The robustness has been answered by estimating how the results of the Bayesian inferences are robust to reasonable variations of the plausible priors. From these robust inferences, it is reasonable to apply the symmetric unimodal priors for analyzing the robustness of the Bayesian inferences.

• Structural Engineering and Mechanics
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• v.22 no.6
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• pp.665-683
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• 2006

The strength behaviors of Fiber Reinforced Plastics (FRP) Composites can be greatly influenced by the properties of constitutive materials, the laminate structures, and load conditions etc, accompanied by many uncertainty factors. So the reliability study on FRP is an important subject of research. Many achievements have been made in reliability studies based on the probability theory, but little has been done on the roles played by fuzzy variables. In this paper, a fuzzy reliability model for FRP laminates is established first, in which the loads are considered as random variables and the strengths as fuzzy variables. Then a numerical model is developed to assess the fuzzy reliability. The Monte Carlo simulation method is utilized to compute the reliability of laminas under the maximum stress criterion. In the second part of this paper, a generalized fuzzy reliability model (GFRM) is proposed. By virtue of the fact that there may exist a series of states between the failure state and the function state, a fuzzy assumption for the structure state together with the probabilistic assumption for strength parameters is adopted to construct the GFRM of composite materials. By defining a generalized limit state function, the problem is converted to the conventional reliability formula that enables the first-order reliability method (FORM) applicable in calculating the reliability index. Several examples are worked out to show the validity of the models and the efficiency of the methods proposed in this paper. The parameter sensitivity analysis shows that some of the mean values of the strength parameters have great influence on the laminated composites' reliability. The differences resulting from the application of different failure criteria and different fuzzy assumptions are also discussed. It is concluded that the GFRM is feasible to use, and can provide an effective and synthetic method to evaluate the reliability of a system with different types of uncertainty factors.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.347-357
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• 2017

The applications of active control is being more popular nowadays. Several control algorithms have been developed to determine optimum control force. In this paper, a Chaotic Particle Swarm Optimization (CPSO) technique, based on Logistic map, is used to compute the optimum control force of active tendon system. A chaotic exploration is used to search the solution space for optimum control force. The response control of Multi-Degree of Freedom (MDOF) shear buildings, equipped with active tendons, is introduced as an optimization problem, based on Instantaneous Optimal Active Control algorithm. Three MDOFs are simulated in this paper. Two examples out of three, which have been previously controlled using Lattice type Probabilistic Neural Network (LPNN) and Block Pulse Functions (BPFs), are taken from prior works in order to compare the efficiency of the current method. In the present study, a maximum allowable value of control force is added to the original problem. Later, a twenty-story shear building, as the third and more realistic example, is considered and controlled. Besides, the required Central Processing Unit (CPU) time of CPSO control algorithm is investigated. Although the CPU time of LPNN and BPFs methods of prior works is not available, the results show that a full state measurement is necessary, especially when there are more than three control devices. The results show that CPSO algorithm has a good performance, especially in the presence of the cut-off limit of tendon force; therefore, can widely be used in the field of optimum active control of actual buildings.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.1
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• pp.116-122
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• 2001

In this paper, to make a base of the fire safety assessment about ship's fire protection design and Classification Society rule, statistical informations and modeling techniques for the fire safety engineering are investigated and probabilistic safety assessment methods in the structural reliability engineering are introduced. FSEM(Fire Safety Evaluation Module) developed in this paper calculates the probability of fatality, which can be used as an index of fire safety. FSEM is used to calculate the probability of fatality of the evacuees in a small room installed according to the rules for fire-proof. Sensitivity analysis is executed to investigate FSEM's applicability to ship. From results, the necessity of new criterion for ship's fire safety design, the need to study the human behavior in the evacuation from fire, and the development of new fire progress model considering special situations in ships are acknowledged.

• Korean Journal of Clinical Pharmacy
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• v.26 no.1
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• pp.59-69
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• 2016

Background: The subcutaneous formulation of biologic disease-modifying antirheumatic drugs (DMARDs) was preferred due to favored self-administration and would be an economical treatment option for patients with rheumatoid arthritis. This study was to compare the economic impact of biologic DMARDs administered by subcutaneous injection in patients with rheumatoid arthritis who had inadequate response to conventional DMARDs. Methods: The cost-minimization analysis was conducted to estimate the lifetime health care costs of treatment sequences with subcutaneous biologic DMARDs as first-line therapy from a health care system perspective. The Markov model was developed to represent the transitions through treatment sequences based on American College of Rheumatology response rate and discontinuation rate. The health care costs comprised the cost of medications, administration, dispensing, outpatient visits, test/diagnostic examination, palliative therapy and treatment of serious infection. All costs were expressed in 2016 Korean Won (KRW) and discounted at 5%. Results: The mean lifetime health care cost per patient was lowest in the etanercept sequence, which was estimated at KRW 63,441,679. The incremental costs of the treatment sequence started with adalimumab, golimumab, abatacept, and tocilizumab were KRW 7,985,730, KRW 4,064,669, KRW 2,869,947, and KRW 4,282,833, respectively, relative to etanercept sequence. These differences in costs mainly were attributable to medication costs. One-way and probabilistic sensitivity analyses confirmed that etanercept represented the option with the lowest cost compared with comparators. Conclusion: This study found that etanercept is likely a cost-saving treatment option among subcutaneous biologic DMARDs in patients with rheumatoid arthritis.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.391-399
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• 2012

This paper proposes a particle filter approach for SLAM(Simultaneous Localization and Mapping) of a mobile robot. The SLAM denotes estimation of both the robot location and map while the robot navigates in an unknown environment without map. The proposed method estimates robot location simultaneously with the locations of the ultrasonic beacons which constitute landmarks for navigation. The particle filter method represents the locations of the robot and landmarks in probabilistic manner by the distribution of particles. The method takes care of the uncertainty of the landmarks' location as well as that of the robot motion. Therefore, the locations of the landmarks are updated including uncertainty at every sampling time. Performance of the proposed method is verified through simulation and experiments. The method yields practically useful mapping information even if the range data from the landmarks include random noise. Also, it provides more accurate and robust estimation of the robot location than the usual least squares methods or dead-reckoning method.

• Journal of Radiation Protection and Research
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• v.42 no.2
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• pp.130-140
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• 2017

Background: With the need for a domestic level 3 probabilistic safety assessment (PSA), it is essential to develop a Korea-specific code. Health effect assessments study radiation-induced impacts; in particular, long-term health effects are evaluated in terms of cancer risk. The objective of this study was to analyze the latest cancer risk models developed by foreign organizations and to compare the methodology of how they were developed. This paper also provides suggestions regarding the development of Korean cancer risk models. Materials and Methods: A review of cancer risk models was carried out targeting the latest models: the NUREG model (1993), the BEIR VII model (2006), the UNSCEAR model (2006), the ICRP 103 model (2007), and the U.S. EPA model (2011). The methodology of how each model was developed is explained, and the cancer sites, dose and dose rate effectiveness factor (DDREF) and mathematical models are also described in the sections presenting differences among the models. Results and Discussion: The NUREG model was developed by assuming that the risk was proportional to the risk coefficient and dose, while the BEIR VII, UNSCEAR, ICRP, and U.S. EPA models were derived from epidemiological data, principally from Japanese atomic bomb survivors. The risk coefficient does not consider individual characteristics, as the values were calculated in terms of population-averaged cancer risk per unit dose. However, the models derived by epidemiological data are a function of sex, exposure age, and attained age of the exposed individual. Moreover, the methodologies can be used to apply the latest epidemiological data. Therefore, methodologies using epidemiological data should be considered first for developing a Korean cancer risk model, and the cancer sites and DDREF should also be determined based on Korea-specific studies.

• Journal of Radiation Protection and Research
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• v.43 no.2
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• pp.50-58
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• 2018

Background: In terms of the Level 3 probabilistic safety assessment (Level 3 PSA), ingestion of food that had been exposed to radioactive materials is important to assess the intermediate- and long-term radiological dose. Because the ingestion dose is considerably dependent upon the agricultural and dietary characteristics of each country, the reliability of the assessment results may become diminished if the characteristics of a foreign country are considered. Thus, this study intends to evaluate and analyze the ingestion dose of Korean during a severe accident by completely considering the available agricultural and dietary characteristics in Korea. Materials and Methods: This study uses COMIDA2, which is a program based on dynamic food chain model. It sets the parameters that are appropriate to Korean characteristics so that we can evaluate the inherent ingestion dose of Korean. The results were analyzed by considering the accident date and food category with regard to the $^{137}Cs$. Results and Discussion: The dose and contribution of the food category depicted distinctive differences based on the accident date. Particularly, the ingestion dose during the first and second years depicted a considerable difference by the accident date. However, after the third year, the effect of foliar absorption was negligible and exhibited a similar tendency along with the order of root uptake rate based on the food category. Conclusion: In this study, the agricultural and dietary characteristics of Korea were analyzed and evaluated the ingestion dose of Korean during a severe accident using COMIDA2. By considering the inherent characteristics of Korean, it can be determined that the results of this study will significantly contribute to the reliability of the Level 3 PSA.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.367-376
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• 2015

Today, big data has become a hot keyword in that big data may be defined as collection of data sets so huge and complex that it becomes difficult to process by traditional methods. Clustering method is to identify the information in a big database by assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. The similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we computed upper and lower limits for probability interestingness measure based similarity measures without marginal probability such as Yule I and II, Michael, Digby, Baulieu, and Dispersion measure. And we compared these measures by real data and simulated experiment. By Warrens (2008), Coefficients with the same quantities in the numerator and denominator, that are bounded, and are close to each other in the ordering, are likely to be more similar. Thus, results on bounds provide means of classifying various measures. Also, knowing which coefficients are similar provides insight into the stability of a given algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.