• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1075-1079
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• 2014

In this paper, we describe the analysis and the compensation method of the g-sensitivity error for MEMS vibratory gyroscopes. Usually, the g-sensitivity error has been ignored in the commercial MEMS gyroscope, but it deserves our attention to apply for the missile application as a tactical grade performance. Thus, it is necessary to compensate for the g-sensitivity error to reach a tactical grade performance. Generally, the g-sensitivity error seems intuitively to be a gyroscope bias error proportional to the linear acceleration. However, we assert that the g-sensitivity error mainly causes not a bias error but a scale-factor error. And we verify that the g-sensitivity scale-factor error occurs due to the non-linearity of parallel plate electrodes. Therefore, we propose the compensation method to remove the g-sensitivity scale-factor error. The experimental result showed that a proposed compensation method improved successfully the performance of the MEMS vibratory gyroscope.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.6
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• pp.438-445
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• 2019

In this paper, we present a new approach to extract the g-sensitivity scale-factor error for a MEMS gyroscope. MEMS gyroscopes, based on the use of both angular momentum and the Coriolis effect, have a g-sensitivity error due to mass unbalance. Generally, the g-sensitivity error is not considered in general use of gyroscopes, but it deserves our attention if we are to develop for tactical class performance and reliability. The g-sensitivity error during vehicle flight increases navigation error; so it must be analyzed and compensated for the use of MEMS IMU for high dynamics vehicle systems. Therefore, we analyzed how to extract the g-sensitivity scale-factor error from the inertial sensor error model. Furthermore we propose a new method to extract the g-sensitivity error using flight motion simulator. We verified our proposed method with experimental results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1017-1021
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• 1995

Geometric and thermal errors are responsible for major components of the errors of a computer numerically controlled turning center. The planar error of a CNC turning center are comprised of 11 geometric and thermal error components. The error synthesis model is formulated by homogeneous coordinate transformation method and expresses the effect of such error components on the planar error of a CNC turning center. In this paper, the sensitivity analysis of the model on the noises through sensing and the change of temperature is addressed. The sensitivity analysis show that the error systhesis model is robust on the noses and z planar error is much affected by the change of temperatures.

• Atmosphere
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• v.20 no.4
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• pp.399-414
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• 2010

Sand and dust storm in East Asia, so called Asian dust, is a seasonal meteorological phenomenon. Mostly in spring, dust particles blown into atmosphere in the arid area over northern China desert and Manchuria are transported to East Asia by prevailing flows. An Asian dust event occurred on 6-8 May 2007 is chosen to investigate how sensitive the Asian dust transport forecast to the initial condition uncertainties and to interpret the characteristics of sensitivity structures from the viewpoint of dynamics and predictability. To investigate the forecast sensitivities to the initial condition, adjoint sensitivities that calculate gradient of the forecast aspect (i.e., response function) with respect to the initial condition are used. The forecast aspects relevant to Asian dust transports are dry energy forecast error and lower tropospheric pressure forecast error. The results show that the sensitive regions for the dry energy forecast error and the lower tropospheric pressure forecast error are initially located in the vicinity of the trough and then propagate eastward as the surface low system moves eastward. The vertical structures of the adjoint sensitivities for the dry energy forecast error are upshear tilted structures, which are typical adjoint sensitivity structures for extratropical cyclones. Energy distribution of singular vectors also show very similar structures with the adjoint sensitivities for the dry energy forecast error. The adjoint sensitivities of the lower tropospheric pressure forecast error with respect to the relative vorticity show that the accurate forecast of the trough (or relative vorticity) location and intensity is essential to have better forecasts of the Asian dust event. Forecast error for the atmospheric circulation during the dust event is reduced 62.8% by extracting properly weighted adjoint sensitivity perturbations from the initial state. Linearity assumption holds generally well for this case. Dynamics of the Asian dust transport is closely associated with predictability of it, and the improvement in the overall forecast by the adjoint sensitivity perturbations implies that adjoint sensitivities would be beneficial in improving the forecast of Asian dust events.

• Steel and Composite Structures
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• v.21 no.5
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• pp.1031-1043
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• 2016

Optimization of the construction scheme of the cable-strut tensile structure based on error sensitivity analysis is studied in this paper. First, the element length was extracted as a fundamental variable, and the relationship between element length change and element internal force was established. By setting all pre-stresses in active cables to zero, the equation between the pre-stress deviation in the passive cables and the element length error was obtained to analyze and evaluate the error effects under different construction schemes. Afterwards, based on the probability statistics theory, the mathematical model of element length error is set up. The statistical features of the pre-stress deviation were achieved. Finally, a cable-strut tensile structure model with a diameter of 5.0 m was fabricated. The element length errors are simulated by adjusting the element length, and each member in one symmetrical unit was elongated by 3 mm to explore the error sensitivity of each type of element. The numerical analysis of error sensitivity was also carried out by the FEA model in ANSYS software, where the element length change was simulated by implementing appropriate temperature changes. The theoretical analysis and experimental results both indicated that different elements had different error sensitivities. Likewise, different construction schemes had different construction precisions, and the optimal construction scheme should be chosen for the real construction projects to achieve lower error effects, lower cost and greater convenience.

• Bulletin of the Korean Chemical Society
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• v.15 no.11
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• pp.976-980
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• 1994

The sensitivity factors and the error propagation factors are defined for the three-parameter ellipsometry (TPE). The sensitivity factor is useful for understanding the nature of the TPE measurements in connection with determination of the optical properties and the thickness of a film. On the other hand, the error propagation factors provide a quantitative tool for predicting the optimum condition for TPE experiments. Their usefulness is demonstrated for the passive film formed on nickel in aqueous solution.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.27-31
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• 2003

Underwater acoustic navigation systems are classified into three systems: ultra-short baseline (USBL), short baseline (SBL), and long baseline (LBL). Because the USBL system estimates the angle of a submersible, the estimation error becomes large if the submersible is far from the USBL transducer array mounted under a support vessel. SBL and LBL systems estimate submersible's location more accurately because they have wider distribution of measuring sensors. Especially LBL systems are widely used as a navigation system for deep ocean applications. Although it is most accurate system it still has estimation errors because of noise, measurement error, refraction and multi-path of acoustic signal, or wrong information of the distributed transponders. In this paper the estimation error of the LBL system are analyzed from a point of sensitivity. It is assumed that the error exists only in the distance between a submersible and the transponders. For this purpose sensitivity of the estimated position with respect to relative distances between them is analyzed. The result says that estimation error is small if the submersible is close to transponders but not near the ocean bottom.

• Atmosphere
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• v.20 no.3
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• pp.287-306
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• 2010

An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF) Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size, assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all, experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.165-171
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• 2012

In this paper, an analytical method is used to perform a sensitivity analysis of geometric errors in a three-axis machine tool. First, an error synthesis model is constructed for evaluating the position volumetric error due to the geometric errors, and then an output variable is defined, such as the magnitude of the position volumetric error. Next, the global sensitivity analysis is executed using an analytical method. Finally, the sensitivity indices are calculated using the quantitative values of the geometric errors.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.