• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.7-13
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• 1999

The potential for growth and the future impact of Rapid Prototyping that it will have on the product development cycle are enormous. Since making tools, precedes making parts, Rapid Tooling becomes widely used in automobile, aerospace, electronic, and other industries. In this study, master models formed by Rapid Prototyping of Stereolithography have been applied for vacuum casting to obtain silicone patterns which have transformed into epoxy models. The epoxy models have been measured to check dimension errors, and tested their functions. These checking and measurement have provided information on plastic injection possibilities and data for die design, Temporary die making with the materials of Aluminum/Epoxy and powder injection metal (PIM) has also been discussed in terms of hardness, surface roughness, and SEM microstructures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.169-172
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• 2006

Most studies using state-space models have been conducted under the assumption of independently distributed noises in measurement and state equation without adequate verification of the assumption. To avoid the improper use of state-space model, testing the assumption prior to the parameter estimation of state-space model is very important. The purpose of this paper is to investigate the general relationship between parameters of state-space models and those of ARIMA processes. Under the assumption, we derive restricted parameter spaces of ARIMA(p,0,p-1) models with mutually different AR roots where $p\;{\le}\;5$. In addition, the results of ARIMA(p,0,p-1) case can be expanded to more general ARIMA models, such as ARIMA(p-1,0,p-1), ARIMA(p-1,1,p-1), ARIMA(p,0,p-2) and ARIMA(p-1,1,p-2).

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.7
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• pp.688-696
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• 2000

A scheme for fault detection on the subsystem level is presented. The method uses analytical redundancy and consists in generating residuals by comparing each measurement with an estimate computed from the reference models. In this study regression neural network models are used as reference models. The regression neural network is memory-based feed forward network that provides estimates of continuous variables. The simulation result demonstrated that the proposed method can effectively detect faults in an air handling unit(AHU). The results show that the regression models are accurate and reliable estimators of the highly nonlinear and complex AHU.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.4
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• pp.305-312
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• 2015

In this study, inter-turn short fault models of interior permanent magnet synchronous motors (IPMSM) are developed by adding saliency modeling to surface-mounted permanent magnet motor models. The saliency model is obtained using the deformed flux models based on both fault-winding flux information and inductance variations caused by cross-flux linkages that depend on the distribution of the same phase windings. By assuming the balanced three-phase current injection, we obtain the positive and negative sequence voltages and the fault current in the positive and the negative synchronous reference frames. The output torque model is developed by adding the magnet and the reluctance torque, which are derived from the developed models. To verify the proposed IPMSM model with an inter-turn short fault, finite element method-based simulation and experimental measurement results are presented.

• Smart Structures and Systems
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• v.15 no.3
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• pp.751-767
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• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• Journal of KIBIM
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• v.12 no.1
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• pp.10-22
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• 2022

Prefabricated bridges require strict management of tolerance during fabrication and assembly. In this paper, digital engineering models for prefabricated bridge components such as deck, girder, pier, abutment are suggested to support information delivery through the life-cycle of the bridge. Rule-based modeling is used to define geometry of the members considering variable dimensions due to fabrication and assembly error. DfMA(design for manufacturing and assembly) provides the rules for ease of fabrication and assembly. The digital engineering model consists of geometry, constraints and corresponding parameters for each phase. Alignment and control points are defined to manage tolerances of the prefabricated bridge during fabrication and assembly. Quality control by digital measurement of dimensions was also considered in the model definition. A pilot bridge was defined virtually to validate the suggested digital engineering models. The digital engineering models for DfMA showed excellent potential to realize prefabricated bridges.

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.327-340
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• 2003

For motion analysis electromagnetic sensors are often used to measure positions and orientations of human subjects. It is observed from several experiments of the Ergonomics Research group that there exist systematic errors and unexpected serious distortions due to some metal masses in the test area. A calibration process is necessary to fix these errors. In this article three models are proposed to correct position measurement errors based on observations from calibration experiments.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.7
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• pp.82-88
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• 1992

This paper presents one of the first comprehensive studies of how hot electron degradation impacts the input offset voltage of a CMOS differential amplifiers. This study utilizes the concept of a virtual source-coupled MOSFET pair in order to evaluate offset voltaged egradation directly from individual device measurement. Next, analytical models are developed to describe the offset voltage degradation. These models are used to examine how hot electron induced offset voltage is affected with the device parameters.