• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.203-210
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• 2003

A simulation study was carried to analyze the vehicle fuel consumption on component basis. Experiments was also carried out to identify the simulation results, under FTP-75 Hot Phase driving conditions. and arbitrary driving conditions. A good quantitative agreement was obtained. Based on the simulation, fuel energy was used in pumping loss(3.7%), electric power generation(0.7%), engine friction(12.7%), engine inertia(0.7%), torque converter loss(4.6%), drivetrain friction(0.6%), road-load(9.2%), and vehicle inertia(13.4%) under FTP-75 Hot Phase driving conditions. Using simulation program, the effects of capacity factor and idle speed on fuel consumption were estimated. A increment of capacity factor of torque converter resulted in fuel consumption improvement under FTP-75 Hot Phase driving conditions. Effect of a decrement of idle speed on fuel consumption was negligible under the identical driving conditions.

• Journal of Power Electronics
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• v.19 no.2
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• pp.560-568
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• 2019

This paper studies the inherent relationship between currents and zero-sequence components. Then a precise algorithm is proposed to calculate the injected zero-sequence component to control the DC-Link neutral-point voltage balance, which can result in a more efficient and flexible neutral point voltage balance with a desirable performance. In addition, it is shown that carried-based PWM with the calculated zero-sequence component scheme can be equivalent to space-vector pulse-width modulation (SVPWM). Based on the proposed method, the optimal zero-sequence component of the feasible modulation indices is analyzed. In addition, the unbalanced load limitation of the DC-Link neutral-point voltage balance control is also revealed. Simulation and experimental results are shown to verify the validity and practicality of the proposed algorithm.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.215-220
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• 2012

In an earlier work, we proposed the chromatic dispersion monitoring technique of non-return to zero (NRZ) signal based on clock-frequency component (CFC) through numerical simulations. However, we have not yet shown any experimental demonstration or analytic derivation of it. In this paper, we show an experimental demonstration and analytic derivation of the proposed chromatic dispersion monitoring technique. We confirm that the experimental results and the analytic results correspond with the simulation results. We also demonstrate that monitoring range and accuracy can be improved by using a simple clock-extraction method.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.91-100
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.97-113
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• 2017

This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.04a
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• pp.40-44
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• 2000

미리 제작되어 있는 소프트웨어 컴포넌트를 조립하여 소프트웨어 개발공정을 산업화하는 과정인 CBD(Component Based Development)는 Farmer 모델에 의하여 설계된 시스템을 실제로 구현하기 위하여 매우 적합한 개념이다. CBD개념의 도입을 위하여 Farmer 모델이 갖는 개체노드, 측면노드 및 KLB/OLB 등과 같은 객체들이 수행하는 기능에 대한 명세를 나타내는 인터페이스 명세 모델이 반드시 필요하게 된다. 본 논문에서는 Farmer 모델에 의하여 정의된 개념을 컴포넌트 인터페이스 메타모델에서의 개념으로 변형시키는 방안에 대하여 설명하고 있다.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.1-13
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• 1994

Deep reasoning procedures are model-based, inferring single or multiple causes and/or timing relations from the knowledge of behavior of component models and their causal structure. The overall goal of this paper is to develop an automated deep reasoning methodology that exploits deep knowledge of structure and behavior of a system. We have proceeded by building a software environment that uses such knowledge to reason from advanced symbolic simulation techniques introduced by Chi and Zeigler. Such reasoning system has been implemented and tested on several examples in the domain of performance evaluation, and event-based control.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.29-33
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes the service prediction-based job scheduling model and present its algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts a processing time of each processing component and distributes a job to processing component with minimum processing time. This paper implements the job scheduling model on the DEVSJAVA modeling and simulation environment and simulates with a case study to evaluate its efficiency and reliability Empirical results, which are compared to the conventional scheduling policies such as the random scheduling and the round-robin scheduling, show the usefulness of service prediction-based job scheduling.