• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.70-78
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• 2011

In this study, we develop a fire stochastic simulation model by season based on the historical fire data in Korea. The model is utilized to generate sequences of fire events that are consistent with Korean fire history. We employ a three-stage approach. First, a random draw from a Bernoulli distribution is used to determine if any fire occurs for each day of a simulated fire season. Second, if a fire does occur, a random draw from a geometric multiplicity distribution determines their number. Last, ignition times for each fire are randomly drawn from a Poisson distribution. This specific distributional forms are chosen after analysis of Korean historical fire data. Maximum Likelihood Estimation (MLE) is used to estimate the primary parameters of the stochastic models. Fire sequences generated with the model appear to follow historical patterns with respect to diurnal distribution and total number of fires per year. We expect that the results of this study will assist a fire manager for planning fire suppression policies and suppression resource allocations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.469-475
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• 2017

Software reliability is one of the most basic and essential problems in software development. In order to detect the software failure phenomenon, the intensity function, which is the instantaneous failure rate in the non-homogeneous Poisson process, can have the property that it is constant, non-increasing or non-decreasing independently at the failure time. In this study, was compared the reliability performance of the software reliability model using the Landely lifetime distribution with the intensity function decreasing pattern and Erlang lifetime distribution from increasing to decreasing pattern in the software product testing process. In order to identify the software failure phenomenon, the parametric estimation was applied to the maximum likelihood estimation method. Therefore, in this paper, was compared and evaluated software reliability using software failure interval time data. As a result, the reliability of the Landely model is higher than that of the Erlang distribution model. But, in the Erlang distribution model, the higher the shape parameter, the higher the reliability. Through this study, the software design department will be able to help the software design by applying various life distribution and shape parameters, and providing software reliability attributes data and basic knowledge to software reliability model using software failure analysis.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.509-522
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• 2018

Purpose: The purpose of this study was to propose more accurate mathematical model which can represent result of government quality assurance activity, especially corrective action and flaw. Methods: The collected data during government quality assurance activity was represented through histogram. To find out which distributions (Poisson distribution, Zero-Inflated Poisson distribution) could represent the histogram better, this study applied Pearson's correlation coefficient. Results: The result of this study is as follows; Histogram of corrective action during past 3 years and Zero-Inflated Poisson distribution had strong relationship that their correlation coefficients was over 0.94. Flaw data could not re-parameterize to Zero-Inflated Poisson distribution because its frequency of flaw occurrence was too small. However, histogram of flaw data during past 3 years and Poisson distribution showed strong relationship that their correlation coefficients was 0.99. Conclusion: Zero-Inflated Poisson distribution represented better than Poisson distribution to demonstrate corrective action histogram. However, in the case of flaw data histogram, Poisson distribution was more accurate than Zero-Inflated Poisson distribution.

• IE interfaces
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• v.17 no.1
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• pp.1-12
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• 2004

In a modern semiconductor device manufacturing industry, statistical bin limits on wafer level test bin data are used for minimizing value added to defective product as well as protecting end customers from potential quality and reliability excursion. Most wafer level test bin data show skewed distributions. By Monte Carlo simulation, this paper evaluates methods and sample size effect regarding determination of statistical bin limits. In the simulation, it is assumed that wafer level test bin data follow the Poisson distribution. Hence, typical shapes of the data distribution can be specified in terms of the distribution's parameter. This study examines three different methods; 1) percentile based methodology; 2) data transformation; and 3) Poisson model fitting. The mean square error is adopted as a performance measure for each simulation scenario. Then, a case study is presented. Results show that the percentile and transformation based methods give more stable statistical bin limits associated with the real dataset. However, with highly skewed distributions, the transformation based method should be used with caution in determining statistical bin limits. When the data are well fitted to a certain probability distribution, the model fitting approach can be used in the determination. As for the sample size effect, the mean square error seems to reduce exponentially according to the sample size.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1508-1517
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• 1993

In this paper, we propose the modeling of a wireless communication system using CSMA protocol, present analytical evalution and simulation as a function of arrival rate and mean END-to-END delay in the distributed packet radio network. Asynchronous 1-persistent CSMA protocol is used in wireless communication system with half duplex. We assume that all terminals are to be in the close range of each other, suitably located in the local area. The traffic presented to a common channel is assumed to be poisson distribution. Analytical model is based on a M/D/1 with breakdown. In conclusion for wireless network model proposed in this paper is suitable for packet arrival rate of 2 packet/sec with mean packet delay time less than 2 times the packet transmission time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2267-2272
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• 2012

This study has presented the analysis of subthreshold swings based on scaling theory for double gate MOSFET. To solve the analytical potential distribution of Poisson's equation, we use Gaussian function to charge distribution. The scaling theory has been used to analyze short channel effect such as subthreshold swing degradation. These scaling factors for gate length, oxide thickness and channel thickness has been modified with the general scaling theory to include effects of double gates. We know subthreshold swing degradation is rapidly reduced when scaling factor of gate length is half of general scaling factor, and parameters such as projected range and standard projected deviation have greatly influenced on subthreshold swings.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.105-107
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• 2015

In this paper, we proposed LTD(Load Tolerance Density-distribution) algorithm using dynamic density for analyzing distribute routing path. MANET(Mobile Ad-hoc Networks) consists of the node that has a mobility. By the Mobility, the topology is exchanged frequently. To reduce the exchange of topology, the hierarchy network is studied. However, if the load is concentrated at the cluster head node, the communication is disconnected. the proposed algorithm measure the dynamic density of the node using poisson distribution. And this algorithm provides distribute routing path using dynamic density. The simulation results, the proposed algorithm shows improved packet delivery ratio than the compared algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.101-107
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• 1990

In this work, one-dimensional simulation is carried out for PT-GaAs Schottky barrier diodes with finite difference method. Shockley's semiconductor governing equations: Poisson equation and current continuity equation are discertized, and linearized by Newton-Raphson method. The linear system of equation is solved by Gaussian elimination method until convergence is achieved. The boundary condition for this equation is taken from thermionic emission-diffusion theory. Simulation is done for PT-GaAs epitaxial-layer Schottky barrier diodes. The claculated results of electron and potential distribution are shown. Simulation results show exellent agreement with experiments.

• Convergence Security Journal
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• v.7 no.2
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• pp.1-8
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• 2007

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault (intensity function). In this paper, intensity function of Goel-Okumoto model was reviewed, proposes Kappa (2) and the Burr distribution, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method. For model determination and selection, explored goodness of fit (the error sum of squares) The methodology developed in this paper is exemplified with a software reliability real data set introduced by NTDS (Naval Tactical Data System)

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.7 s.361
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• pp.16-23
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• 2007

For a fully depleted SOI type symmetric double gate MOSFET, a simple expression for the threshold voltage has been derived in a closed-form To solve analytically the 2D Poisson's equation in a silicon body, the two-dimensional potential distribution is assumed approximately as a polynomial of fourth-order of x, vertical coordinate perpendicular to the silicon channel. From the derived expression for the surface potential, the threshold voltage can be obtained as a simple closed-form. Simulation result shows that the threshold voltage is exponentially dependent on channel length for the range of channel length up to $0.01\;[{\mu}m]$.