• Proceedings of the CALSEC Conference
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• 1997.11a
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• pp.263-268
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• 1997

Functional Modeling methodology, IDEF0 is widely used for modeling, analysis and description of enterprise system. The limitation of modeling components restricts applicability and give rise to confusion about model. In this paper, we propose new method to extend IDEF0. The first is adding modeling components which are semantic representations. In addition to ICOMs, we add the time and cost component which is required to execute the function. The second is tracing mechanism. When we need some information, we drive the functions related with the information by reverse tracing of the function which produces the information as a output and input. Through the tracing, we find out the bottleneck process or high cost process. Finally, we suggest the final decomposition level. We call the final decomposed function into unit function which has only one output data. We can combine and reconstruct some of functions because an unit function is similar to ‘lego block’. To reach the integrated system, the main problem to be solved is the integration of information produced by different functional subsystem. This can be resolved when the creation of data must be dependent on only one function. Through view integration of function output, we can guarantee the integrity of data.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.103-108
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• 2008

In case of the 3D Sketch System for spatial modeling, The use of 3D input devices in 3D environment is the best method to express designer's intention. However, the designer's 3D drawing skill is not accurate. 80, we use the multiple strokes used generally by 2D sketch. Multiple strokes make the designer recognize model's current drawing features and what he change We use the cubic-based drawing method to calculate many surfaces in real time. We arrange the relations of cubes for composing surfaces and multi strokes. We implement the sketch system taking cubic modeling and multiple strokes technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.6
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• pp.93-101
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• 2001

This paper presents modeling of an inductive micro position sensing system and its analysis. The parameters affected the system response are excitation frequency, turn ratio, input position, air-gap size, load resistance, and geometric dimensions. To analyze the system, we try to establish a modeling based on an equivalent magnetic circuit with permeances. The model is verified by the experimental results from 1 kHz to 20 kHz. The magnetic circuit model is well fitted to the experimental data except a little error due to LC resonance in the large turn-ratio system. Modeling enables us to theoretically approach the response characteristics. Based on the magnetic circuit model, system parameters can be selected in such a way to obtain the required characteristics such as high sensitivity, good linearity, or small size.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.542-548
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• 2012

This study presents a new method of system modeling by using the Discrete Fourier Transform for the position control system of DC Motor. And the proposed method is similar to the method of System Identification by analysis of correlation of the measured input-output data. The measured output signals are transformed to the frequency domain using DFT. The Fourier Spectrum of the transformed signals is used for knowing to the feature of having an important effect on the system. And transfer function of the second order system is estimated by the dominant parameter which is computed in the magnitude and the phase of Fourier spectrum of the transformed signals. In addition, the output signal includes the unique feature of system. So, although the basic parameter of the system is unknown for us, the proposed method has an advantage to system modeling. And the controller is easily designed by the estimated transfer function. Thus, in this paper, the proposed method is applied to the system modeling for the position control system of DC Motor and the PD-controller is designed by the estimated model. And the efficiency and the reliability of the proposed method are verified by the experimental result.

• Smart Structures and Systems
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• v.17 no.4
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.23-28
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• 1997

We discuss development of a simulator for automated manufacturing systems (AMSs) which have sophisticated automated material handling equipments and complicated work flows. The simulator is designed to satisfy the following requirements. A user should be able to easily configure or specify an AMS through a graphical user interface (GUI) and minimal data input. The user should be able to model diverse and complied control logic for automated material handling systems like automated guided vehicle (AGV) systems, robot workcell systems and conveyor systems as well as complicated job flow program. Real time animation is desired. Finally, the simulator should be easily maintained and extended. To satisfy the requirements, we use an object-oriented paradigm for modeling, designing, and programming of the simulator. We use an object-oriented modeling framework to design the modeling elements library, and take the process interaction approach for scheduling processes and events. To model a user-defined diverse control logic, we also develop a script language and its interpreter. We explain design and implementation strategies. We implement the simulator using Visual C++ 4.2 and Open GL on Windows NT and the Windows95. Some modeling examples will be demonstrated.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.54-61
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• 2008

This paper presents a method for reducing modelling error in the linear motion system with voicecoil actuator (VCA). A model of linear motion system composed of a mechanism and control was prepared to verify the proposed method. In modeling of the system, the damping coefficient obtained experimentally is applied to the model in order to consider the effect of the viscous friction for the moving part in VCA. The response velocity of VCA for duty ratio of PWM signal was analyzed in the time domain. Consequently, the relation between velocity and duty ratio was obtained. The result from the experiment showed an error of 9% when compared with that of simulation. In order to reduce the modeling error, impedance variation according to input frequency was analyzed, and equivalent impedance with multi-frequency was applied to the control part. As a result, the modeling error decreased to 5%.

• Journal of Internet of Things and Convergence
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• v.4 no.1
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• pp.1-5
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• 2018

This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

• Journal of Power Electronics
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• v.19 no.1
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• pp.24-33
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• 2019

A new modeling method for AC-AC switched capacitor converters (SCCs) is introduced in this study. The proposed analytical method aims to accurately describe the input-output characteristics of AC-AC SCCs and establish a mathematical model for static voltage conversion ratio and equivalent resistance, which are key performance metrics for SCCs. A quantitative analysis of converter regulation capability is addressed on the basis of the modeling method. In this analysis, the effects of the control parameters and individual components on SCCs are illustrated extensively. Component stresses, such as the peak value and transient variation of the voltage/current of the converter, are also presented. The effectiveness of the proposed method is verified by comparing it with the existing modeling method and applying it to an AC-AC SCC with a conversion ratio of three. Two 1 kW prototypes are built in a laboratory, and their experimental results exhibit good agreement with the theoretical analysis.