• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2649-2666
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• 2015

In the dynamic cloud computing environment, to ensure, under the terms of service-level agreements, the maximum efficiency of resource utilization, it is necessary to investigate the online dynamic management of virtual machine resources and their operational application systems/components. In this study, the feasibility and properties of the division of virtual machine resources on the cloud platform, using the virtual machine cluster as the management unit, are investigated. First, the definitions of virtual machine clusters are compared, and our own definitions are presented. Then, the feasibility of division using the virtual machine cluster as the management unit is described, and the isomorphism and reconfigurability of the clusters are proven. Lastly, from the perspectives of clustering and cluster segmentation, the dynamics of virtual machines are described and experimentally compared. This study aims to provide novel methods and approaches to the optimization management of virtual machine resources and the optimization configuration of the parameters of virtual machine resources and their application systems/components in large-scale cloud computing environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.348-353
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• 2013

Recently, control theory including stochastic optimal control and various machine-learning-based artificial intelligence methods have become major tools in the field of financial engineering. In this paper, we briefly review some recent papers utilizing stochastic optimal control theory in the fields of the pair trading for mean-reverting markets and the trend-following strategy, and consider a couple of strategies utilizing both stochastic optimal control theory and machine learning methods to acquire more flexible and accessible tools. Illustrative simulations show that the considered strategies can yield encouraging results when applied to a set of real financial market data.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.43-46
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• 2003

In this paper, a novel rotor flux estimation method of an induction motor using support vector machine(SVM) is presented. Two veil-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. The theory of the SVM algorithm is based on statistical teaming theory. Training of SVH leads to a quadratic programming(QP) problem. The proposed SVM rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of Proposed algorithm are throughly verified through numerical simulation.

• International Journal of Concrete Structures and Materials
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• v.5 no.1
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• pp.29-33
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• 2011

This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.11
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• pp.440-444
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• 1985

Voltage equations of a synchronous machine are derived from the electromagnetic field theory in order to develop a new equivalent circuit model considering core loss. The result from the new equivalent circuit model is superior to that of the conventional one in the analysis of machine performance and characteristics on optimal efficiency control of a synchronous motor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.143-144
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• 2007

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.367-372
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• 2007

Classification as a tool to extract information from data plays an important role in science and engineering. Among various classification methodologies, support vector machine has recently seen significant developments. The central problem this paper addresses is the accuracy of support vector machine. In particular, we are interested in the situations where fast rates of convergence to the Bayes risk can be achieved by support vector machine. Through learning examples, we illustrate that support vector machine may yield fast rates if the space spanned by an adopted kernel is sufficiently large.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.63-71
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• 2014

As power consumption increases, more power utilities are required to satisfy the demand and consequently results in tremendous cost to build the utilities. Another issue in construction of power utilities to meet the peak demand is an inefficiency caused by surplus power during non-peak time. Therefore, most power company considers power demand management with time-based electricity rate policy which applies different rate over time. This paper considers an optimal machine operation problem under the time-based electricity rates. In TOC (Theory of Constraints), the production capacities of all machines are limited to one of the bottleneck machine to minimize the WIP (work in process). In the situation, other machines except the bottleneck are able to stop their operations without any throughput loss of the whole manufacturing line for saving power utility cost. To consider this problem three integer programming models are introduced. The three models include (1) line shutdown, (2) block shutdown, and (3) individual machine shutdown. We demonstrate the effectiveness of the proposed IP models through diverse experiments, by comparing with a TOC-based machine operation planning considered as a current model.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.179-185
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• 2012

This paper proposes a design of RSIDS(RST and SVM based Intrusion Detection System) using RST(Rough Set Theory) and SVM(Support Vector Machine) algorithm. The RSIDS consists of PrePro(PreProcessing) module, RRG(RST based Rule Generation) module, and SAD(SVM based Attack Detection) module. The PrePro module changes the collected information to the data format of RSIDS. The RRG module analyzes attack data, generates the rules of attacks, extracts attack information from the massive data by using these rules, and transfers the extracted attack information to the SAD module. The SAD module detects the attacks by using it, which the SAD module notifies to a manager. Therefore, compared to the existing SVM, the RSIDS improved average ADR(Attack Detection Ratio) from 77.71% to 85.28%, and reduced average FPR(False Positive ratio) from 13.25% to 9.87%. Thus, the RSIDS is estimated to have been improved, compared to the existing SVM.