• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.130-140
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• 1998

Studies present a guide to parameter estimation of software reliability models using SAS JMP. In this paper, we consider only software reliability growth model(SRGM), where mean value function has a S-shaped growth curve, such as Yamada et al. model, and ohba inflection model. Besides these stochastic SRGM, deterministic SRGM's, by fitting Logistic and Gompertz growth curve, have been widely used to estimate the error content of software systems. Introductions or guide lines of JMP are concerned. Estimation of parameters of Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is discussed, along with the variability in the estimates or error sum of squares. This paper have shown that JMP can be an effective tool I these research.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.443-446
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• 2003

Conventional disturbance rejection methods have to derive the inverse model of a system. However, the inverse model of n nonholonomic system is not unique, because an inverse it changes depending on initial conditions and desired values. A kind of internal model control (IMC) using feedback error learning is discussed for the motion control of nonholonomic mobile robots in this paper, The present method is different from a conventional IMC whose control system consists of an inverse model, a direct model and a filter. The present disturbance rejection method need not use a direct model, where the remaining two elements are composed of the same inverse model based on neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.135-141
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• 2000

The major role of temperature sensors in thermal error compensation system of machine tools is improving machining accuracy by supplying reliable temperature data on the machine structure. This paper presents a new method for fault diagnosis of temperature sensors and recovery of faulted data to establish the reliability of thermal error compensation system. The detection of fault and its location is based on the correlation coefficients among temperature data from the sensors. The multiple linear regression model which is prepared using complete normal data is also used fur the recovery of faulted data. The effectiveness of this method was tested by comparing the computer simulation results and measured data in a CNC machining center.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.97-103
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• 2001

Accuracy of a machined part is determined by the relative motion between the cutting tool and the workpiece. One of the important factors which affects the relative motion is the geometric errors of a machine tool. In this study, firstly, geometric errors are measured by laser interferometer, and the positioning error of each control point selected uniformly on the control surface CAD model can be estimated from th oirm shaping model and geometric error data base. Where a form shaping function is derived from the link of homogeneous transformation matrix. Secondly, control points are shifted to the estimated amount of positioning errors. A new control surface is modeled with NURBS(Non Uniform Rational B-Spline) surface approximation to the shifted control points. By generating tool paths to the redesigned control surface, we reduce the machining error quite.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.71-79
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• 2019

Geolocation accuracy is one of the important factors in utilizing all weather available SAR satellite imagery. In this study, an error budget analysis was performed on key variables affecting on geolocation accuracy by generating KOMPSAT-5 simulation data. To perform the analysis, a Range-Doppler model was applied as a geometric model of the SAR imagery. The results show that the geolocation errors in satellite position and velocity are linearly related to the biases in the azimuth and range direction. With 0.03cm/s satellite velocity biases, the simulated errors were up to 0.054 pixels and 0.0047 pixels in the azimuth and range direction, and it implies that the geolocation accuracy is sensitive in the azimuth direction. Moreover, while the clock drift causes a geolocation error in the azimuth direction, a signal delay causes in the range direction. Monte-Carlo simulation analysis was performed to analyze the influence of multiple geometric error sources, and the simulated error was up to 3.02 pixels in the azimuth direction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1299-1307
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• 2014

In this work, an appropriate analysis model of a precise foot sensor with low detection pressure capability under a low range of variation in the dimensional variables was proposed. With a simple two-dimensional model, it was found that a remarkably high error level sometimes occurred between the analysis and experimental results. In order to overcome the error and improve the performance, a three-dimensional model was introduced, and the detection pressure and sensor characteristics were compared with those of the experimental results, which showed its enhanced performance with less error and higher precision.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.130-137
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• 2004

A dynamic model of railway reducer is developed by the lumped parameter method. The model accounts for shafts, bearings flexibilities, gyroscopic effects and the force couplings among the transverse and torsion motions due to gearing. Vibration/noise analysis as well as strength of gear teeth, and bearing life are considered. Excitation forces of railway reduction are considered as the mass unbalance of the rotors, misalignment and a function of gear transmission error which comes from the modified tooth surface. A campbell diagram, in which the excitation sources caused by the mass unbalance of the rotors, misalignment and the transmitted errors of the gearing are considered, shows that, at the operating speed, there are not the critical speed. The program which can be used to analyze and predict vibration/noise characteristics by mass unbalance, misalignment and gear transmission error of railway reduction is developed with this system model.