• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.105-107
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• 2002

The device characteristics and the interface electronic structures of organic light-emitting devices based on tris-(8-hydroxyquinoline)aluminum were investigated with $Al/CaF_2$, Al/LiF, and Al-only cathodes. Similar to the Al/LiF cathode, the $Al/CaF_2$ cathode greatly improved the performance of the device over Al-only cathode. However, a photoelectron spectroscopy study revealed that despite the performance improvement, the evolution of the new peaks during $Al/CaF_2$ cathode formation closely resembled those of the Al-only cathode rather than the Al/LiF cathode.

• Journal of the Korean Statistical Society
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• v.35 no.3
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• pp.281-303
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• 2006

In an earlier work, we investigated the problem of using linear programming to bound performance measures in a discrete time Jackson network. There it was assumed that the system evolution is controlled by the early arrival scheme. This assumption implies that the system can't be modelled by a Markov chain. This problem was resolved and performance bounds were calculated. In the present work, we use a modification of the early arrival scheme (without corrupting it) in order to make the system evolves as a Markov chain. This modification enables us to obtain explicit expressions for certain moments that could not be calculated explicitly in the pure early arrival scheme setting. Moreover, this feature implies a reduction in the linear program size as well as the computation time. In addition, we obtained tighter bounds than those appeared before due to the new setting.

• ETRI Journal
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• v.18 no.3
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• pp.127-145
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• 1996

A new analytic framework based on a linear algebra approach is proposed for examining the performance of a direct sequence spread spectrum (DS/SS) slotted ALOHA wireless communication network systems with delay capture. The discrete-time Markov chain model has been introduced to account for the effect of randomized time of arrival (TOA) at the central receiver and determine the evolution of the finite population network performance in a single-hop environment. The proposed linear algebra approach applied to the given Markov problem requires only computing the eigenvector ${\prod}$ of the state transition matrix and then normalizing it to have the sum of its entries equal to 1. MATLAB computation results show that systems employing discrete TOA randomization and delay capture significantly improves throughput-delay performance and the employed analysis approach is quite easily and staightforwardly applicable to the current analysis problem.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.50-56
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• 2021

Due to the recent increase in the mobile streaming market, mobile traffic is increasing exponentially. IMT-2020, named as the next generation mobile communication standard by ITU, is called the 5th generation mobile communication (5G), and is a technology that satisfies the data traffic capacity, low latency, high energy efficiency, and economic efficiency compared to the existing LTE (Long Term Evolution) system. 5G implements this technology by utilizing a high frequency band, but there is a problem of path loss due to the use of a high frequency band, which is greatly affected by system performance. In this paper, small cell technology was presented as a solution to the high frequency utilization of 5G mobile communication system, and furthermore, the system performance was improved by applying machine learning technology to macro communication and small cell communication method decision. It was found that the system performance was improved due to the technical application and the application of machine learning techniques.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.7 s.337
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• pp.27-34
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• 2005

Iterative detection (ID) has proven to be a near-optimal solution for concatenated Finite State Machines (FSMs) with interleavers over an additive white Gaussian noise (AWGN) channel. When perfect channel state information (CSI) is not available at the receiver, an adaptive ID (AID) scheme is required to deal with the unknown, and possibly time-varying parameters. The basic building block for ID or AID is the soft-input soft-output (SISO) or adaptive SISO (A-SISO) module. In this paper, Reduced State SISO (RS-SISO) algorithms have been applied for complexity reduction of the A-SISO module. We show that serially concatenated CPM (SCCPM) with AID has turbo-like performance over fading ISI channels and also RS-A-SISO systems have large iteration gains. Various design options for RS-A-SISO algorithms are evaluated. Recently developed density evolution technique is used to analyze RS-A-SISO algorithms. We show that density evolution technique that is usually used for AWGN systems is also a good analysis tool for RS-A-SISO systems over frequency-selective fading channels.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.293-303
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• 2016

Analysis of bargaining games utilizing evolutionary computation in recent years has dealt with important issues in the field of game theory. In this paper, we investigated interaction and coevolution process among heterogeneous artificial agents using evolutionary computation in the bargaining game. We present three kinds of evolving-strategic agents participating in the bargaining games; genetic algorithms (GA), particle swarm optimization (PSO) and differential evolution (DE). The co-evolutionary processes among three kinds of artificial agents which are GA-agent, PSO-agent, and DE-agent are tested to observe which EC-agent shows the best performance in the bargaining game. The simulation results show that a PSO-agent is better than a GA-agent and a DE-agent, and that a GA-agent is better than a DE-agent with respect to co-evolution in bargaining game. In order to understand why a PSO-agent is the best among three kinds of artificial agents in the bargaining game, we observed the strategies of artificial agents after completion of game. The results indicated that the PSO-agent evolves in direction of the strategy to gain as much as possible at the risk of gaining no property upon failure of the transaction, while the GA-agent and the DE-agent evolve in direction of the strategy to accomplish the transaction regardless of the quantity.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.707-712
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• 1998

This paper presents several softcomputing techniques such as neural networks, fuzzy logic and genetic algorithms : Neural networks as brain metaphor provide fundamental structure, fuzzy logic gives a possibility to utilize top-down knowledge from designer, and genetic algorithms as evolution metaphor determine several system parameters with the process of bottom up development. With these techniques, we develop a pattern recognizer which consists of multiple neural networks aggregated by fuzzy integral in which genetic algorithms determine the fuzzy density values. The experimental results with the problem of recognizing totally unconstrained handwritten numeral show that the performance of the proposed method is superior to that of conventional methods.

• 한국전산유체공학회:학술대회논문집
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• 2001.05a
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• pp.9-14
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• 2001

Astrophysics has been one of the disciplines which utilize actively supercomputers for their researches. In this paper, studies of astrophysical flows by large scale numerical simulations in supercomputers are discussed. The followings are described: 1) characteristics of astrophysical flows, 2) numerical codes to study astrophysical flows, 3) performance of the codes in parallel supercomputers. A couple of examples of such studies are briefly presented: 1) three-dimensional evolution of the nonlinear Kelvin-Helmholtz instability in a magnetized medium, 2) three-dimensional simulations of astrophysical jets in a magnetized medium.