• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
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• pp.376-381
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• 2001

This paper presents a methodology of machining error compensation by using Artificial Neural Network(ANN) model based on the inspection database of On-Machine-Measurement(OMM) system. First, the geometric errors of the machining center and the probing errors are significantly reduced through compensation processes. Then, we acquire machining error distributions from a specimen workpiece. In order to efficiently analyze the machining errors, we define two characteristic machining error parameters. These can be modeled by using an ANN model, which allows us to determine the machining errors in the domain of considered cutting conditions. Based on this ANN model, we try to correct the tool path in order to effectively reduce the errors by using an iterative algorithm. The iterative algorithm allows us to integrate changes of the cutting conditions according to the corrected tool path. Experimentation is carried out in order to validate the approaches proposed in this paper.

• 대한기계학회논문집A
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• 제24권5호
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• pp.1343-1351
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• 2000

Thermally induced errors have been significant factors affecting the machine tool accuracy. In this paper, the spindle thermal error has been focused, where the 5 degree of freedom thermal error components are considered. An effective measurement system has been devised for the 5 DOF thermal errors, consisting of gap sensors and thermocouples around the micro-computer interfaced environment. Several thermal error modeling techniques are also implemented for the thermal error prediction: multiple linear regression, neural network and system identification methods, etc. The performance of the thermal error modeling techniques is evaluated and compared, giving the system identification method as the optimum model having the least deviation. The developed system for the thermal error measurement and modeling was practically applied to a CNC machining center, and the spindle thermal errors were effectively compensated around the micro computer-machine tool interfaced networks. The machine tool accuracy was improved about 4-5 times typically.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.428-433
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• 1999

The errors of IMU(Inertial Measurement Unit) can be divided into deterministic and random errors. Since the required accuracy of the IMU is very high, the errors must be compensated by using an accurate error mode. In this paper, we present a method to get a more accurate error model in a laboratory test. This was done by considering the setting misalignment in the laboratory test in the IMU error model. We considered here the IMU which consits of DTG(dynamically tuned gyroscope) and pendulum type accelerometer. First, it was shown that the estimation result from the model which does not contain the setting misalignment gives considerable estimation error at the validation test. Second, a new model considering the setting misalignment was derived. Finally, by validation test using the estimation results from new model the validity of it was proved.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.1-12
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• 1995

An estimator of coefficients of polynomial measurement error model with vector predictor and first-order interaction terms is derived using Hermite polynomial. Asymptotic normality of estimator is provided and some simulation study is performed to compare the small sample properties of derived estimator with those of OLS estimator.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.209-216
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• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.553-566
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.544-547
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• 1997

In this paper, we propose a dynamic localization method using a rotating sonar and a map. The proposed method is implemented by using extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while AMR is moving continuously. By modeling and estimating systematic errors of a differential encoder, the position is successfully estimated even the interval of the map-based measurement. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error and of compensating systematic errors of the differential encoder well.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.684-690
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• 2015

This paper presents a modified residual-based EKF (Extended Kalman Filter) for performance improvement of indoor positioning using WiFi RSSI (Received Signal Strength Indicator) measurement. Radio signal strength in indoor environments may have irregular attenuation characteristics due to obstacles such as walls, furniture, etc. Therefore, the performance of the RSSI-based positioning with the conventional trilateration method or Kalman filter is insufficient to provide location-based accurate information services. In order to enhance the performance of indoor positioning, in this paper, error analysis of the distance calculated by using the WiFi RSSI measurement is performed based on the radio propagation model. Then, an IARM (Irregularly Attenuated RSSI Measurement) error is defined. Also, it shows that the IARM error is included in the residual of the positioning filter. The IARM error is always positive. So, it is presented that the IARM error can be estimated by taking the absolute value of the residual. Consequently, accurate positioning can be achieved based on the IEM (IARM Error Mitigated) EKF with the residual modified by using the estimated IARM error. The performance of the presented IEM EKF is verified experimentally.

• 한국의류학회지
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• 제22권5호
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• pp.664-675
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• 1998

The purpose of this study was to isolate the observational error included in plane photogrammetric data and suggest more accurate and suitable method for body shape measurement. Three experiments were carried out in this study. First of all, the variables which can be obtained from photogrammetric method were selected among the data which can be measured improperly by direct measurement or showing large deviations between the samples. Secondly, the height, the width and the depth of 50 subjects were measured by plane photogrammetry. The result showed that plane photogrammetric data contain significant observational error even for the same variables, as the angle of which photos had been taken changes. Therefore, in order to reduce the observational error and to measure the human body accurately, three-dimensional measurement, stereo photogrammetry was employed in the last experiment. As it is important to isolateonly the observational error by plane photogrammetry and to exclude the accidental error caused by movement of human body subject, body shape model(manikin) was used as subject. The result showed that the average observational error by plane photogrammetry was more than 4 cm in the height, 0.85-1.29cm in the width and 0.49cm in the depth. In conclusion, it is not adequate to use the height obtained from plane photogrammetric data as human body measurement data. And the width and the depth should be used cautiously, even though they are relatively less significant, the error still can make some difference on clothing construction.