• Coupled systems mechanics
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• v.11 no.1
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• pp.33-41
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• 2022

Quite often we have a lot of measurement data and would like to find some relation between them. One common task is to see whether some measured data or a curve of known shape fit into the cumulative measured data. The problem can be visualized since data could generally be presented as curves or planes in Cartesian coordinates where each curve could be represented as a vector. In most cases we have measured the cumulative 'curve', we know shapes of other 'curves' and would like to determine unknown coefficients that multiply the known shapes in order to match the measured cumulative 'curve'. This problem could be presented in more complex variants, e.g., a constant could be added, some missing (unknown) data vector could be added to the measured summary vector, and instead of constant factors we could have polynomials, etc. All of them could be solved with slightly extended version of the procedure presented in the sequel. Solution procedure could be devised by reformulating the problem as a measurement problem and applying the generalized inverse of the measurement matrix. Measurement problem often has some errors involved in the measurement data but the least squares method that is comprised in the formulation quite successfully addresses the problem. Numerical examples illustrate the solution procedure.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.111-119
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• 2002

One of the advantages of SP methods is the possibility of getting a number of responses from each respondent. However, when the repeated observations from each respondent are analysed by applying the simple modeling method, a potential problem is created because of upbiased significance due to the repeated observation from each respondent. This study uses a variety of approaches to explore this issue and to test the robustness of the simple model estimates. Among several different approaches, the Jackknife method and Kocurs method were applied. The Jackknife method was implemented using a program JACKKNIFE. The model estimate results of Jackknife method and Kocurs method were compared with those of the uncorrected estimates in order to test whether there was repeated measurement problem or not and the extent to which this problem affected the model estimates. The standard errors between the uncorrected model estimates and Jackknife estimates were also compared. The results reveals that the t-ratios of Kocurs are much lower than those of the uncorrected method and Jackknife estimates, indicating that Kocurs method underestimates the significance of the coefficients. Jackknife method produced the almost same coefficients as those of the uncorrected model but the lower t-ratios. These results indicate that the coefficients of the uncorrected method are accurate but that their significance are somewhat overestimated. In this study. 1 concluded that the repeated measurement Problem did exist in our data, but that it did not affect the model estimation results significantly. It is recommended that such a test should become a standard procedure. If it turns out that the analysis based on the simple uncorrected method are influenced by the repeated measurement problem. it should be corrected.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.1
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• pp.39-46
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• 1993

In developing a multiple regression relationship, independent variables are frequently measured with error. For these situations the problem of estimating unknown parameters has been extensively discussed in the literature while little attention has been given to the prediction problem. In this paper a criterion is developed for assessing the severeness of measurement errors in each independent variable on the predicted values. Using the developed criterion we can present a guideline as to which measurement error should be controlled for a more accurate prediction. Proposed methods are illustrated with a standard data system in work measurement.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.8-12
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• 2020

In this paper we study the Minimum Error Discrimination problem (MED) for ensembles of linearly independent (LI) pure states. By constructing a map from the set on those ensembles we show that the Pretty Good Measurement (PGM) and the optimal measurement for the MED are related by the map.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.102-108
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• 2004

In this paper, we studied a vibration problem that is critical and common to most precision measurement systems. For micro mechanical part measurements, results obtained from the vision-based precision measurement system may contain errors due to the vibration. In order to defeat this generic problem, for the current study, a PC based image processing technique was used first, to assess the effect of the vibration to the precision measurement and second, to develop an in-situ calibration algorithm that automatically compensate the measurement results in real time. We used a set of stereoscopic CCD cameras to acquire the images for the dimensional measurement and the reference measurement. The mapping function was obtained through the in-situ calibration to compensate the measurement results and the statistical analysis for the actual results is provided in the paper. Based on the current statistical study, it is expected to obtain high precision results for the micro measurement systems.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.533-545
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• 2022

Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.75-79
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• 2000

This paper proposes the optimal placement with minimum set of Phasor Measurement Units (PMU's) using tabu search and makes an alternative plan to secure the robustness of the network with PMU's. The optimal PMU Placement (OPP) problem is generally expressed as a combinatorial optimization problem subjected to the observability constraints. Thus, it is necessary to make a use of an efficient method in solving the OPP problem. In this paper, a tabu search based approach to solve efficiently this OPP problem proposed. The observability of the network with PMU's is fragile at any single PMU contingency. To overcome the fragility, an alternative scheme that makes efficient use of the existing measurement system in power system state estimation proposed. The performance of the proposed approach and the alternative scheme is evaluated with IEEE sample systems.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1136-1141
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• 2014

This paper proposes a new contact type ball tracking sensor to improve the control performance of a low cost ball-and-beam system. It is well-known that the impulsive measurement noise contained in ball position measurement is one of the factors which severely degrades the ball-and-beam control performance. The impulsive ball position measurement noises often appear under the sporadical ball floating on the beam. This fact motivates us to devise a simple analog preprocessing circuit to determine whether the ball loses the contact or not. Once the abnormal ball position measurement is detected, the design problem of the ball tracking sensor can be cast into the typical state estimation problem with missing data. In order to tackle the real-time implementation issue, a steady-state Kalman filter is applied to the problem. Through the experimental results, the usefulness of the proposed scheme is demonstrated.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.127.4-127
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• 2001

Inspection and shape measurement of three-dimensional objects are widely needed in industries for quality monitoring and control. A number of visual or optical technologies have been successfully applied to measure three dimensional surfaces. Especially, the shape measurement using an interferometric principle becomes a successful methodology. However, those conventional interferometric methods to measure surface profile have an inherent shortcoming, namely 2∏ ambiguity problem. The problem inevitably happens when the object to be measured has discontinuous shape due to the repetition of interferometric signal with phase period of 2∏. Therefore, in this paper, we choose as a shape measuring method, ...

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.38-42
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• 2018

In this paper, we consider measurement allocation problem in a spatially correlated sensor field. Our goal is to determine the probability of each sensor's being measured based on its contribution to the estimation reliability; it is desirable that a sensor improving the estimation reliability is measured more frequently. We consider a spatial correlation model of a sensor field reflecting transmission power limit, noise in measurement and transmission channel, and channel attenuation. Then the estimation reliability is defined distortion error between event source and its estimation at sink. Motivated by the correlation nature, we model the measurement allocation problem into a cooperative game, and then quantify each sensor's contribution using Shapley value. Against the intractability in the computation of exact Shapley value, we deploy a randomized method that enables to compute the approximate Shapley value within a reasonable time. Besides, we envisage a measurement scheduling achieving the balance between network lifetime and estimation reliability.