• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.4
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• pp.253-287
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• 2018

This paper studies the global stability of a class of discrete-time pathogen infection models with latently infected cells. The rate of pathogens infect the susceptible cells is taken as bilinear, saturation and general. The continuous-time models are discretized by using nonstandard finite difference scheme. The basic and global properties of the models are established. The global stability analysis of the equilibria is performed using Lyapunov method. The theoretical results are illustrated by numerical simulations.

• Journal for History of Mathematics
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• v.27 no.3
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• pp.197-210
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• 2014

Species like insects and fishes have, in many cases, non-overlapping time intervals of one generation and their descendant one. So the population dynamics of such species can be formulated as discrete models. In this paper various discrete population models are introduced in chronological order. The author's investigation starts with the Malthusian model suggested in 1798, and continues through Verhulst model(the discrete logistic model), Ricker model, the Beverton-Holt stock-recruitment model, Shep-herd model, Hassell model and Sigmoid type Beverton-Holt model. We discuss the mathematical and practical significance of each model and analyze its properties. Also the stability properties of stationary solutions of the models are studied analytically and illustratively using GSP, a computer software. The visual outputs generated by GSP are compared with the analytical stability results.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.807-810
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• 1997

Digital control of robot manipulator employs discrete-time robot models. It is important to explore effective discrete-time robot models and to analyze their properties in control system designs. This paper presents a new type discrete-time robot model. The model is derived by using trapezoid rule to approximate the convolution integral term, then eliminating nonlinear force terms from robot dynamical equations. The new model obtained has very simple structure, and owns the properties of independence to the nonlinear force terms. According to evaluation criteria, three aspects of the model properties: model accuracy, model validity range and model simplicity are examined and compared with commonly used discrete-time robot models. The validity of the proposed model and its advantages to control system designs are verified by simulation results.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.179-186
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• 2011

Objective: This study is to review three representative psychological perspectives that explain scientific construct of emotion, that are the discrete emotion model, appraisal model, and dimensional model. Background: To develop emotion sensitive interface is the fusion area of emotion and scientific technology, it is necessary to have a balanced mixture of both the scientific theory of emotion and practical engineering technology. Extensional theories of the emotional structure can provide engineers with relevant knowledge in functional application of the systems. Method: To achieve this purpose, firstly, literature review on the basic emotion model and the circuit model of discrete emotion model as well as representative theories was done. Secondly, review on the classical and modern theories of the appraisal model emphasizing cognitive appraisal in emotion provoking events was conducted. Lastly, a review on dimensional theories describing emotion by dimensions and representative theories was conducted. Results: The paper compared the three models based on the prime points of the each model. In addition, this paper also made a comment on a need for a comprehensive model an alternative to each model, which is componential model by Scherer(2001) describing numerous emotional aspects. Conclusion: However, this review suggests a need for an evolved comprehensive model taking consideration of social context effect and discrete neural circuit while pinpointing the limitation of componential model. Application: Insight obtained by extensive scientific research in human emotion can be valuable in development of emotion sensitive interface and emotion recognition technology.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.267-269
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• 2005

This paper describes a consideration on the sampling in linearly time-varying (LTV) systems in view of the convenience in digital signal processing. The relation between a continuous-time and a discrete-time system models is investigated for a simple linear time-invariant system. Based on the results of the investigation, we first consider discrete-time models for LTV systems, Then the simplicity of the models in terms of microprocessor-based digital signal processing is compared.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.405-424
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• 2001

Many simulation models have been built to facilitate simulation technique in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover simulation has a capability to find local optimum value within each component through entire supply chain. Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in supply chain and how these equations are used in the supply chain simulation models. A simple example of supply chain model dealing with the strategic level of supply chain presented in this paper shows the possibility and the prospect of this approach.

• Journal of the Korea Society for Simulation
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• v.10 no.2
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• pp.75-89
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• 2001

Many simulation models have been built to facilitate simulation technique in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover simulation has a capability to find local optimum value within each component through entire supply chain. Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in supply chain and how these equations are used in the supply chain simulation models. A simple example of supply chain model dealing with the strategic level of supply chain presented in this paper shows the possibility and the prospect of this approach.

• IE interfaces
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• v.15 no.3
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• pp.286-296
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• 2002

Products which are sold with warranty, preventive maintenance actions by manufacturers and/or buyers have an impact on the total costs for both parties. In this paper, we develop the models to study the expected warranty cost for products with free repairable warranty with three types of discrete preventive maintenance. We deal with by utilizing the concept that preventive maintenance reduces the virtual age of the system. We assume that the maintenance planning horizon can be segmented into k discrete and equally sized periods. In such a scenario, numerical examples are presented.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.605-616
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• 2000

Three primary interests frequently raised by mortgage companies are introduced and the corresponding statistical approaches for the default probability in mortgage companies are examined. Statistical models considered in this paper are time series, logistic regression, decision tree, neural network, and discrete time models. Usage of the models is illustrated using an artificially modified data set and the corresponding models are evaluated in appropriate manners.