• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2006년도 춘계 학술발표회 논문집
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3135-3148
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• 2013

The popularization of streaming networks and mobile devices brings a new kind of network called mobile peer-assisted streaming network. In this network, service provider not only offers downloading services, but also pre-pushes resources to users for caching. Users can download their demanded resources while working as servers for uploading the cached data. Also the mobile characteristic makes high package losing probability in this network. So we study how the service provider pre-pushes or the user downloads resources efficiently and effectively while considering the package losing in this paper. We build utility models for service provider and user, and solve the models using Bellman's theory to achieve Nash Equilibrium which shows the service provider's optimal pre-pushing speed and user's optimal downloading speed. The numerical simulation demonstrates the efficiency and effectiveness of our proposed pre-pushing and downloading scheme by comparing to the traditional scheme.

• 한국시뮬레이션학회논문지
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• 제10권2호
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• pp.61-74
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• 2001

The reliability evaluation of the large scale network becomes very complicate according to the growing size of network. Moreover if the reliability is not constant but follows probability distribution function, it is almost impossible to compute them in theory. This paper studies the network evaluation methods in order to overcome such difficulties. For this an efficient path set algorithm which seeks the path set connecting the start and terminal nodes efficiently is developed. Also, various variance reduction techniques are applied to compute the system reliability to enhance the simulation performance. As a numerical example, a large scale network is given. The comparisons of the path set algorithm and the variance reduction techniques are discussed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 C
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• pp.2345-2347
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• 2005

In this paper, impedance characteristics of overhead medium-voltage (MV) power lines is reported for power line communication (PLC) over an MV power line network. For analysis, a two-port equivalent network model of MV power lines is derived. By applying the transmission line theory, reflection behavior and impedance of power lines are investigated. For verification, impedance of power lines is measured at a test field for an MV PLC. The results show that impedance of MV power lines is between $200{\Omega}$ and $300{\Omega}$ and converges to a half of their characteristic impedance.

• 대한교통학회지
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• 제12권3호
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• pp.49-64
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• 1994

The development of a mathematical modular framework based on Petri Network theory to model a traffic network is the subject of this paper. Traffic intersections are the primitive elements of a transportation network and are characterized as event driven and asynchronous systems. Petri network have been utilized to model these discrete event systems; further analysis of their structure can reveal information relevant to the concurrency, parallelism, synchronization, and deadlock avoidance issuse. The Petri-net based model of a generic traffic junction is presented. These modular networks are effective in synchronizing their components and can be used for modeling purposes of an asynchronous large scale transportation system. The derived model is suitable for simulations on a multiprocessor computer since its program execution safety is secured. The software pseudocode for simulating a transportation network model on a multiprocessor system is presented.

• Journal of Information Science Theory and Practice
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• 제10권spc호
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• pp.123-134
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• 2022

Existing network cryptography systems are threatened by recent developments in quantum computing. For example, the Shor algorithm, which can be run on a quantum computer, is capable of overriding public key-based network cryptography systems in a short time. Therefore, research on new cryptography systems is actively being conducted. The most powerful cryptography systems are quantum key distribution (QKD) and post quantum cryptograph (PQC) systems; in this study, a network based on both QKD and PQC is proposed, along with a quantum key management system (QKMS) and a Q-controller to efficiently operate the network. The proposed quantum cryptography communication network uses QKD as its backbone, and replaces QKD with PQC at the user end to overcome the shortcomings of QKD. This paper presents the functional requirements of QKMS and Q-Controller, which can be utilized to perform efficient network resource management.

• 조명전기설비학회논문지
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• 제27권1호
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• pp.99-108
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• 2013

The high-voltage pulse generator is consist of transformers of fundamental wave and harmonic waves, and shunt capacitances. The pulse has the fundamental wave and the harmonic waves that have been increased as a series circuit by the transformers to make high voltage pulse. This paper shows that pulse generator circuit is analyzed using Miller's theorem and network theory(ABCD Matrix) and simulated in frequency and time domain using Matlab program. The output voltage of pulse were obtained to 2.5kHz, 1.8kV. Output pulse voltage increases as $L_m$ increases in low voltage circuit. In high voltage circuit, outer capacitors are related to frequency band pass characteristics.

• Kyungpook Mathematical Journal
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• 제56권4호
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• pp.1191-1205
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• 2016

The emergence of various Internet worms, including the stand-alone Code Red worm that caused a distributed denial of service (DDoS), has prompted many studies on their propagation speed to minimize potential damages. Many studies, however, assume the same probabilities for initially infected nodes to infect each node during their propagation, which do not reflect accurate Internet worm propagation modelling. Thus, this paper analyzes how Internet worm propagation speed varies according to the number of vulnerable hosts directly connected to infected hosts as well as the link costs between infected and vulnerable hosts. A mathematical model based on centrality theory is proposed to analyze and simulate the effects of degree centrality values and closeness centrality values representing the connectivity of nodes in a large-scale network environment on Internet worm propagation speed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.300-303
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• 2003

An Algorithm based on direct implementation of variable structure systems theory approach is proposed for on-line training of multilayer perceptrons. Network structures which have multiple inputs, single output and one hidden layer are considered and the weights are assumed to have capabilities for continuous time adaptation. The zero level set of the network learning error is regarded as a sliding surface in the learning parameters space. A sliding mode trajectory can be brought on and reached in finite time on such a sliding manifold. Results from simulated on-line identification task for a two-link planar manipulator dynamics are also presented.

• 한국정보시스템학회지:정보시스템연구
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• 제5권
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• pp.205-248
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• 1996

Historically, the en-users' acceptance of the expert systems(ES) have generally been used as a proxy for the ES' implementation success by both practitioners and academicians. However, with regard to bank loan decisions, most loan officers approach the acquisition of an ES with apprehension. In order to overcome this skepticism, more research should focus on the behavioral aspects relate to systems acquisition and usage. This research applied Vroom's(1964) expectancy theory in an effort to predict end-users' motivation to use an ES in a bank loan decision context. Because human behaviors and judgements are nonlinear rather than linear functions, accurately predicting human behavior is very difficult. To increase the prediction power for end-users' motivation to use an ES in a bank loan decision context, this research used an artificial neural network (ANN) model. In this research, an attempt was made to evaluate adequacy of the surrogates by analyzing differences between real bank loan officers and student surrogates in applying expectancy theory to estimate bank loan officers' motivation to use ES in a bank loan decision context.