• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.24-32
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• 2007

Variable message sign (VMS), which is used for providing real-time information on traffic conditions and accident occurrences, is one of the important components of intelligent transportation systems. It is essential that the scheme of VMS message phase and duration should be designed with the consideration of drivers' message reading time that will be depended on various causal factors, such as the amount of VMS messages, drivers' travel speed, and geometric conditions. However, there have been not much efforts to explore the relationships between the drivers' message reading time and the above causal factors. Driving simulator experiments were conducted to obtain drivers' message reading time with respect to the amount of VMS messages, drivers' travel speed, and different geometric conditions. Regression modeling techniques were applied to develop an estimation model for drivers' message reading time. Probabilistic outcomes of the proposed model would be greatly utilized to design proper VMS message phase and duration.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.32-41
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• 1999

In this paper, an inversion method using chirp signals and two near field receivers is proposed. Inversion problems can be formulated into the probabilistic models composed of signals, a forward model and noise. Forward model to simulate chirp signals is chosen to be the source-wavelet-convolution planewave modeling method. The solution of the inversion problem is defined by a posteriori pdf. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the ranges for a priori uniform distribution is based. The genetic algorithm can be applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂norm of the difference between measured and modeled signals. The observed signals can be separated into a set of two signals reflected from the upper and lower boundaries of a sediment. The separation of signals and successive applications of the genetic algorithm optimization process reduce the search space, therefore improving the inversion results. Not only the marginal pdf but also the statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm. The examples applied here show that, for synthetic data with noise, it is possible to carry out an inversion for sedimentary layers using the proposed inversion method.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.10
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• pp.5125-5129
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• 2012

Economic decision models are being increasingly used to assess medical interventions. Advances in this field are mainly due to enhanced processing capacity of computers, availability of specific software to perform the necessary tasks, and refined mathematical techniques. We here estimated the incremental cost-effectiveness of ten strategies for colon cancer screening, as well as no screening, incorporating quality of life, noncompliance and data on the costs and profit of chemotherapy in Iran. We used a Markov model to measure the costs and quality-adjusted life expectancy of a 50-year-old average-risk Iranian without screening and with screening by each test. In this paper, we tested the model with data from the Ministry of Health and published literature. We considered costs from the perspective of a health insurance organization, with inflation to 2011, the Iranian Rial being converted into US dollars. We focused on three tests for the 10 strategies considered currently being used for population screening in some Iranians provinces (Kerman, Golestan Mazandaran, Ardabil, and Tehran): low-sensitivity guaiac fecal occult blood test, performed annually; fecal immunochemical test, performed annually; and colonoscopy, performed every 10 years. These strategies reduced the incidence of colorectal cancer by 39%, 60% and 76%, and mortality by 50%, 69% and 78%, respectively, compared with no screening. These approaches generated ICER (incremental cost-effectiveness ratios) of $9067, $654 and $8700 per QALY (quality-adjusted life year), respectively. Sensitivity analyses were conducted to assess the influence of various scales on the economic evaluation of screening. The results were sensitive to probabilistic sensitivity analysis. Colonoscopy every ten years yielded the greatest net health value. Screening for colon cancer is economical and cost-effective over conventional levels of WTP8.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.91-93
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• 2003

It has been estimated that the equivalent of approximately $US 50 billion has been spent on research on the behavior and fate of pesticides in the environment since Rachel Carson published “Silent Spring” in 1962. Much of the resulting knowledge has been summarized explicitly in computer algorithms in a variety of empirical, deterministic, and probabilistic simulation models. These models describe and predict the transport, degradation and resultant concentrations of pesticides in various compartments of the environment during and after application. In many cases the known errors of model predictions are large. For this reason they are typically designed to be “conservative”, i.e., err on the side of over-prediction of concentrations in order to err on the side of safety. These predictions are then compared with toxicity data, from tests of the pesticide on a series of standard representative biota, including terrestrial and aquatic indicator species and higher animals (e.g., wildlife and humans). The models' predictions are good enough in some cases to provide screening of those compounds which are very unlikely to do harm, and to indicate those compounds which must be investigated further. If further investigation is indicated a more detailed (and therefore more complicated) model may be employed to give a better estimate, or field experiments may be required. A model may be used to explore “what if” questions leading to possible alternative pesticide usage patterns which give lower potential environmental concentrations and allowable exposures. We are currently at a maturing stage in this research where the knowledge base of pesticide behavior in the environmental is growing more slowly than in the past. However, innovative uses are being made of the explosion in available computer technology to use models to take ever more advantage of the knowledge we have. In this presentation, current developments in the state of the art as practiced in North America and Europe will be presented. Specifically, we will look at the efforts of the ‘Focus’ consortium in the European Union, and the ‘EMWG’ consortium in North America. These groups have been innovative in developing a process and mechanisms for discussion amongst academic, agriculture, industry and regulatory scientists, for consensus adoption of research advances into risk management methodology.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.139-145
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• 2006

This paper develops pedestrian fatality models capable of producing the probability of pedestrian fatality in collision between vehicles and pedestrians. Probabilistic neural network (PNN) and binary logistic regression (BLR) ave employed in modeling pedestrian fatality pedestrian age, vehicle type, and collision speed obtained from reconstructing collected accidents are used as independent variables in fatality models. One of the nice features of this study is that an iterative sampling technique is used to construct various training and test datasets for the purpose of better performance comparison Statistical comparison considering the variation of model Performances is conducted. The results show that the PNN-based fatality model outperforms the BLR-based model. The models developed in this study that allow us to predict the pedestrian fatality would be useful tools for supporting the derivation of various safety Policies and technologies to enhance Pedestrian safety.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1071-1083
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• 2005

By estimating probability distributions of the good solutions in the current population, some researchers try to find the optimal solution more efficiently. Particularly, finite mixtures of distributions have a very useful role in dealing with complex problems. However, it is difficult to choose the number of components in the mixture models and merge superior partial solutions represented by each component. In this paper, we propose a new continuous evolutionary optimization algorithm with distribution estimation by variational Bayesian mixtures of factor analyzers. This technique can estimate the number of mixtures automatically and combine good sub-solutions by sampling new individuals with the latent variables. In a comparison with two probabilistic model-based evolutionary algorithms, the proposed scheme achieves superior performance on the traditional benchmark function optimization. We also successfully estimate the parameters of S-system for the dynamic modeling of biochemical networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6B
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• pp.575-583
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• 2004

In this paper, we extend the multiple time scale control framework to the window-based congestion control, in particular, such as the TCP. This is performed by interfacing the TCP with a large time scale control module which adjusts the aggressiveness of the bandwidth consumption behavior exhibited by the TCP as a function of large time scale Self-Similar network state. i.e., conformation that exceeds the horizon of the feedback loop as determined by the RTT. How to effectively utilize such an information-due to its probabilistic nature, dispersion over the multiple time scales, and affection on the top of the existing window-based congestion controls-is a non-trivial problem. The evaluation performance of the multiple time scale TCP is facilitated by a simulation of the bench-mark environment which is based on the physical modeling of a self-similar traffic. We explicate our methodology for discerning and evaluating the impact of changes in transport protocols in the protocol stack under the self-similar traffic conditions. We discuss issues arising in the comparative performance evaluation under heavy-tailed workloads.

• Journal of KIBIM
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• v.8 no.3
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• pp.31-42
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• 2018

This study focuses on the BIM request for information(RFI) processing performance and quantitatively analyzes the performance of the BIM coordinator and the loss due to the waiting of the project participants. For these purposes, a method to quantitatively evaluate the performance of the BIM coordinator was proposed using a queueing model. For the verification, two projects in which BIM was applied in the construction phase were selected, and the BIM RFI data were collected through the analysis of the BIM monthly report and BIM coordinator work log of each project. In addition, the BIM input personnel, labor cost, and productivity data were collected through interviews with the experts of the case projects. The analysis of the BIM RFI processing performance of the BIM coordinator using the queueing model exhibited on a probabilistic basis that the waiting status of the project participants could vary depending on the preliminary BIM application to the design verification as well as the input number and level of the BIM coordinator personnel. In addition, the loss cost due to the waiting of the project participants was analyzed using the number of BIM RFIs waiting to be processed in the queueing system. Finally, the economic feasibility analysis for the optimal BIM coordinator input was performed considering the loss cost. The results of this study can be used to make decisions about the optimal BIM coordinator input and can provide grounds for the BIM return on investment (ROI) analysis considering the waiting cost of the project participants.