• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• East Asian mathematical journal
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• v.39 no.5
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• pp.493-503
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• 2023

Tendency prediction of daily confirmed cases is an important issue for public health authorities. To protect the tendency, we estimate the transmission rate of stochastic SEIR model for COVID-19 in Korea using particle Markov chain Monte Carlo method. The results show that the increasing and decreasing tendency of estimated transmission rate appear one or two days in advance compared to daily incidence cases, and as time evolves the standard deviation of the estimates of transmission rate reduces. Since ten months have passed since the first incident case of COVID-19 in Korea, we expect to forecast the tendency of daily confirmed cases for the next one or two days more accurately using our method.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.263-277
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• 2024

This paper proposes new parameterizations of the tilted beta binomial distribution, obtained from the combination of the binomial distribution and the tilted beta distribution, where the beta component of the mixture is parameterized as a function of their mean and variance. These new parameterized distributions include as particular cases the beta rectangular binomial and the beta binomial distributions. After that, we propose new linear regression models to deal with overdispersed binomial datasets. These new models are defined from the proposed new parameterization of the tilted beta binomial distribution, and assume regression structures for the mean and variance parameters. These new linear regression models are fitted by applying Bayesian methods and using the OpenBUGS software. The proposed regression models are fitted to a school absenteeism dataset and to the seeds germination rate according to the type seed and root.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.853-865
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• 2014

Hydrologic dam risk analysis depends on complex hydrologic analyses in that probabilistic relationship need to be established to quantify various uncertainties associated modeling process and inputs. However, the systematic approaches to uncertainty analysis for hydrologic risk analysis have not been addressed yet. In this paper, two major innovations are introduced to address this situation. The first is the use of a Hierarchical Bayesian model based regional frequency analysis to better convey uncertainties associated with the parameters of probability density function to the dam risk analysis. The second is the use of Bayesian model coupled HEC-1 rainfall-runoff model to estimate posterior distributions of the model parameters. A reservoir routing analysis with the existing operation rule was performed to convert the inflow scenarios into water surface level scenarios. Performance functions for dam risk model was finally employed to estimate hydrologic dam risk analysis. An application to the Dam in South Korea illustrates how the proposed approach can lead to potentially reliable estimates of dam safety, and an assessment of their sensitivity to the initial water surface level.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4501-4504
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• 2012

In recent decades, decreasing trends in esophageal cancer mortality have been observed across China. We here describe esophageal cancer mortality trends in Linzhou city, a high-incidence region of esophageal cancer in China, during 1988-2010 and make a esophageal cancer mortality projection in the period 2011-2020 using a Bayesian approach. Age standardized mortality rates were estimated by direct standardization to the World population structure in 1985. A Bayesian age-period-cohort (BAPC) analysis was carried out in order to investigate the effect of the age, period and birth cohort on esophageal cancer mortality in Linzhou during 1988-2010 and to estimate future trends for the period 2011-2020. Age-adjusted rates for men and women decreased from 1988 to 2005 and changed little thereafter. Risk increased from 30 years of age until the very elderly. Period effects showed little variation in risk throughout 1988-2010. In contrast, a cohort effect showed risk decreased greatly in later cohorts. Forecasting, based on BAPC modeling, resulted in a increasing burden of mortality and a decreasing age standardized mortality rate of esophageal cancer in Linzhou city. The decrease of esophageal cancer mortality risk since the 1930 cohort could be attributable to the improvements of socialeconomic environment and lifestyle. The standardized mortality rates of esophageal cancer should decrease continually. The effect of aging on the population could explain the increase in esophageal mortality projected for 2020.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.481-491
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• 2020

The development of agricultural land at Saemangeum has required a significant increase in agricultural water use. It has been well acknowledged that salinity plays a critical role in the farming system. Therefore, a systematic study in salinity is necessary to better manage agricultural water. This study aims to develop a stochastic salinity simulation model that simultaneously simulates salinities obtained from different layers. More specifically, this study proposed a two-stage Autoregressive Exgeneous (ARX) model within a hierarchical Bayesian modeling framework. We derived posterior distributions of model parameters and further used them to obtain the predictive posterior distribution for salinities at three different layers. Here, the BIC values are used and compared to determine the optimal model from a set of candidate models. A detailed discussion of the model is provided.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.265-279
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• 2008

The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.