• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.449-452
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• 2000

This paper proposes a new, adaptive Dead Reckoning model, called Dynamic Dead Reckoning , for Distributed Interactive Simulation and humanoid avatar systems. The proposed model can overcome the weak points of traditional Dead Reckoning caused by a fixed threshold and strong dependency on rotation event. This paper introduces new criteria for update message filtering , named as Update lifetime. The Dynamic Dead Reckoning keeps the balance between extrapolation fidelity and filtering performance by two component models, Variable Threshold Mechanism and Rotation Event model. The experimental results show that the proposed model can lower the increment rate of update traffic to the increase of rotation frequency without any significant loss of accuracy.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.201-211
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• 2011

In many cases, populations in the real world are composed of different subpopulations. Furthermore, in addition to the heterogeneity in the lifetimes of items, there also could be the heterogeneity in the efficiency or performance of items. In this case, the reliability measures should be defined in a different way. In this article, we consider the mixture of stochastically ordered subpopulations. Efficiency based reliability measures are defined when the performance of items in the subpopulations has different levels. Discrete and continuous mixing models are studied. The concept of the association between the lifetime and the performance of items in subpopulations is defined. It is shown that the consideration of efficiency can change the shape of the mixture failure rate dramatically especially when the lifetime and the performance of items in subpopulations are negatively associated. Furthermore, the modelling method proposed in this paper is applied to the case when the stress levels of the operating environment of items are different.

• Journal of Radiation Protection and Research
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• v.48 no.2
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• pp.90-99
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• 2023

Background: Quantitative risk assessments should be accompanied by uncertainty analyses of the risk models employed in the calculations. In this study, we aim to develop a computational code named SUMRAY for use in cancer risk projections from radiation exposure taking into account uncertainties. We also aim to make SUMRAY publicly available as a resource for further improvement of risk projection. Materials and Methods: SUMRAY has two versions of code written in R and Python. The risk models used in SUMRAY for all-solid-cancer mortality and incidence were those published in the Life Span Study of a cohort of the atomic bomb survivors in Hiroshima and Nagasaki. The confidence intervals associated with the evaluated risks were derived by propagating the statistical uncertainties in the risk model parameter estimates by the Monte Carlo method. Results and Discussion: SUMRAY was used to calculate the lifetime or time-integrated attributable risks of cancer under an exposure scenario (baseline rates, dose[s], age[s] at exposure, age at the end of follow-up, sex) specified by the user. The results were compared with those calculated using another well-known web-based tool, Radiation Risk Assessment Tool (RadRAT; National Institutes of Health), and showed a reasonable agreement within the estimated confidential interval. Compared with RadRAT, SUMRAY can be used for a wide range of applications, as it allows the risk projection with arbitrarily specified risk models and/or population reference data. Conclusion: The reliabilities of SUMRAY with the present risk-model parameters and their variance-covariance matrices were verified by comparing them with those of the other codes. The SUMRAY code is distributed to the public as an open-source code under the Massachusetts Institute of Technology license.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.244-252
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• 2011

This study was to estimate the radiation dose associated with 64-slice multidetector CT(MDCT) in clinical practice and quantify the potential cancer risk associated with these examinations. Lifetime attributable risks(LAR) were estimated with models developed in the national Academies' Biological Effects of Ionizing Radiation VII report. Mean effective dose were 1.48mSv in Brain axial scan, 7.66mSv in chest routine contrast, 12.17mSv in coronary angiogram, 24.52mSv in Dynamic abdomen scan. LAR estimates for brain routine varied from 1 in 7463 for man to 1 in 4926 for women. In chest routine with contrast, LAR varied from 1 in 1449 for men to 1 in 952. LAR of Abdomen dynamic CT varied from 1 in 453 for men to 1 in 298 for women. So, 64-slice MDCT scan is associated with non-negligible LAR of cancer. Doses can be reduced by careful attention to scanning protocol.

• Safety and Health at Work
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• v.7 no.3
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• pp.251-255
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• 2016

Background: Building demolition can lead to emission of dust into the environment. Exposure to silica dust may be considered as an important hazard in these sites. The objectives of this research were to determine the amount of workers' exposure to crystalline silica dust and assess the relative risk of silicosis and the excess lifetime risk of mortality from lung cancer in demolition workers. Methods: Four sites in the Tehran megacity region were selected. Silica dust was collected using the National Institute for Occupational Safety and Health method 7601 and determined spectrophotometrically. The Mannetje et al and Rice et al models were chosen to examine the rate of silicosis-related mortality and the excess lifetime risk of mortality from lung cancer, respectively. Results: The amount of demolition workers' exposure was in the range of $0.085-0.185mg/m^3$. The range of relative risk of silicosis related mortality was increased from 1 in the workers with the lowest exposure level to 22.64/1,000 in the employees with high exposure level. The range of the excess lifetime risk of mortality from lung cancer was in the range of 32-60/1,000 exposed workers. Conclusion: Geometric and arithmetic mean of exposure was higher than threshold limit value for silica dust in all demolition sites. The risk of silicosis mortality for many demolition workers was higher than 1/1,000 (unacceptable level of risk). Estimating the lifetime lung cancer mortality showed a higher risk of mortality from lung cancer in building demolition workers.

• Membrane and Water Treatment
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• v.1 no.1
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• pp.61-74
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• 2010

This paper will integrate models at different levels (from filtration, backwashing to chemical cleaning and membrane lifetime) that can be used to minimize overall operating costs of a dead-end ultra filtration process that is used for the purification of surface water. Integration of the models leads to a multi-level optimization problem (at different levels different objectives should be reached). This problem is solved as a MINLP. Systematic modelling and optimization of membrane systems is not extensively discussed in the scientific literature. In this paper the first steps are taken in the formulation of proper models and the use of systems engineering tools to come to real optimal operating conditions. The optimized variables are used to calculate fouling profiles which can subsequently be used as inputs for a control system that actually enforces the profiles to a real pilot plant.

• Korean Management Science Review
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• v.20 no.2
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• pp.135-149
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• 2003

Developing a proper program for customer evaluation is one of the most imminent tasks to implement CRM (Customer Relationship Management). Design of the Customer Value model is an important key to the customer evaluation progrgm. This paper proposes two models for estimating Customer Value. The first one is a Description Model for Customer Value based on customer CSI (Customer Satisfaction Index) data. This model represents as quantitative numbers what customers feel from the company or the service. The second one is a Prediction Model which employs factor analysis and regression to predict customer value. This paper exploits the two models to evaluate Customer Value as well as for customer behavior prediction.

• Smart Structures and Systems
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• v.6 no.1
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• pp.17-27
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• 2010

The approach to damage detection and localization adopted in this paper is based on a statistical comparison of models built from the response time histories collected at different stages during the structure lifetime. Some of these time histories are known to have been recorded when the structural system was undamaged. The consistency of the models associated to two different stages, both undamaged, is first recognized. By contrast, the method detects the discrepancies between the models from measurements collected for a damaged situation and for the undamaged reference situation. The damage detection and localization is pursued by a comparison of the SSE (sum of the squared errors) histograms. The validity of the proposed approach is tested by applying it to the analytical benchmark problem developed by the ASCE Task Group on Structural Health Monitoring (SHM). In the paper, the results of the benchmark studies are presented and the performance of the method is discussed.

• International Journal of Reliability and Applications
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• v.9 no.2
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• pp.167-182
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• 2008

The objective of this paper is to discuss the stochastic analysis and the statistical inference of a three-states semi-Markov reliability model. Using the maximum likelihood procedure, the parameters included in this model are estimated. Based on the assumption that the lifetime and repair time of the system are gener-alized Weibull random variables, the reliability function of this system is obtained. Then, the distribution of the first passage time of this system is derived. Many important special cases are discussed. Finally, the obtained results are compared with those available in the literature.

• International Journal of Reliability and Applications
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• v.6 no.1
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• pp.13-29
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• 2005

This paper deals with the stochastic analysis of a three-states semi-Markov reliability model. Using both the maximum likelihood and Bayes procedures, the parameters included in this model are estimated. Next, assuming that the lifetime and repair time are generalized exponential random variables, the reliability function of this system is obtained. Then, the distribution of the first passage time of this system is discussed. Finally, some of the obtained results are compared with those available in the literature.