• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.120-125
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• 2004

In the rolling of steel or non-steel metal the most important quality aspect are thickness and flatness. In thickness, there are two important factors. One of them is getting close with accurate goal, nominal gauge, the other is minimize gauge bandwidth, the variation in gauge. In this thesis, we proposed the fuzzy model AGC to minimize gauge variation along the length, developed the rolling mill dynamic model using the math mode of the rolling mill process and the rolling model related with the variety character of the rolling material. We compared the gauge control efficiency of fuzzy model AGC and PI mass flow AGC. We have got a simulation result, that the exit gauge variation of PI mass flow AGC was 2 micron and fuzzy model AGC was 1.2 micron at 1200mpm of rolling speed when each controller was rolling 5 micron of material that is the entry gauge variation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.59-66
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• 2013

In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.25-28
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• 2003

The $\ulcorner$CIDEAR$\lrcorner$ model proceeds the following six steps : $\ulcorner$Decision Theory ＆ Evaluation Model$\lrcorner$, $\ulcorner$AR Decision ＆ Evaluation Model$\lrcorner$, $\ulcorner$Resource ＆ Performance Analysis Model$\lrcorner$, $\ulcorner$Cross Impact Assumption Model$\lrcorner$, $\ulcorner$Priority Oder Decision Model$\lrcorner$, and $\ulcorner$Efficiency Cause Analysis Model$\lrcorner$ - In this study, twenty-one R＆D projects of a leading company in electronic industry are selected to examine the usefulness of the constructed $\ulcorner$CIDEAR$\lrcorner$ model. Simulation method, Excel, Lindo(Linear Interactive and Discrete Optimizer) and Team EC are used in this case study.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.287-304
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• 2012

Cloud computing, which means a new way of deploying information technology(IT) in organizations as a service and charging per use, has a deep impact on organizations' IT accessibility, agility and efficiency of its usage. More than that, the emergence of cloud computing surpasses a mere technological innovation, making business model innovation possible. We call this innovation realized by could computing a cloud business model. This study develops a comprehensive framework of business model, first, and then defines and analyzes the cloud business model through this framework. This study also examines the case of M-Pesa mobile payment as a cloud business model in which a new value creation and profit realization schemes have been realized and industry value network has changed. Finally, this study discusses the business implications from this new business model.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.157-169
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• 2018

The purpose of this study was to examine the volatility of bitcoin, diagnose if bitcoin are a systematic risk asset, and evaluate their effectiveness by estimating market beta representing systematic risk using GARCH (Generalized Auto Regressive Conditional Heteroskedastieity) model. First, the empirical results showed that the market beta of Bitcoin using the OLS model was estimated at 0.7745. Second, using GARCH (1, 2) model, the market beta of Bitcoin was estimated to be significant, and the effects of ARCH and GARCH were found to be significant over time, resulting in conditional volatility. Third, the estimated market beta of the GARCH (1, 2), AR (1)-GARCH (1), and MA (1)-GARCH (1, 2) models were also less than 1 at 0.8819, 0.8835, and 0.8775 respectively, showing that there is no systematic risk. Finally, in terms of efficiency, GARCH model was more efficient because the standard error of a market beta was less than that of the OLS model. Among the GARCH models, the MA (1)-GARCH (1, 2) model considering non-simultaneous transactions was estimated to be the most appropriate model.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.2
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• pp.137-144
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• 2011

A Simple equivalent circuit model is developed for a wireless energy transfer system via coupled magnetic resonances and a practical design method is also provided. Node equations for the resonance system are built with the method, expanding on the equations for a transformer, and the optimum distances of coils in the system are derived analytically for optimum coupling coefficients for high transfer efficiency. In order to calculate the frequency characteristics for a lossy system, the equivalent model is established at an electric design automation tool. The model parameters of the actual system are extracted and the modeling results are compared with measurements. Through the developed model, it is seen that the system can transfer power over a mid-range of a few meters and impedance matching is important to achieve high efficiency. This developed model can be used for a design and prediction on the similar systems such as increasing the number of receiving coils and receiving modules, etc.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.15 no.3
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• pp.159-166
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• 2003

In this study, we develop a finite element model to directly solve the Laplace equation while keeping the same computational efficiency as the models based on the extended mild-slope equation which has been widely used for calculation of wave transformation in shallow water. For this, the computational domain is discretized into finite elements with a single layer in the vertical direction. The velocity potential in the element is then expressed in terms of the potentials at the nodes located at water surface, and the Galerkin method is used to construct the numerical model. A common shape function is adopted in horizontal direction, and the cosine hyperbolic function in vertical direction, which describes the vertical behavior of progressive waves. The model was developed for vertical two-dimensional problems. In order to verify the developed model, it is applied to vertical two-dimensional problems of wave reflection and transmission. It is shown that the present finite element model is comparable to the models based on extended mild-slope equations in both computational efficiency and accuracy.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.1001-1013
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• 2011

Development of an optimal water supply system considering water quantity, quality, and economical efficiency is needed to decide optimal available area by combine water supply systems in overlapped area where are more than 2 water sources. The EPAnet and the KModSim were coupled to develop optimal network model. The developed network model was calibrated by measured data from water supply system in Geoje City, Korea in 2007 which have three water sources such as Sadeong booster pumping station, Guchun dam reservoir and Yoncho dam reservoir. The optimum network model was validated by operating results of 2011 to assess the economically optimized service area and optimal pump combination under the given hydraulic operating rules developed in this study. The developed model can be applied into designing water supply systems and operating rules for the conjunctive operation since the model can give the optimal solution satisfied with water quantity, economical efficiency and quality.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.