• ETRI Journal
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• v.45 no.4
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• pp.636-649
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• 2023

Renewable energy generation cannot be consistently predicted or controlled. Therefore, it is currently not widely used in the electricity market, which requires dependable production. In this study, reliability- and variance-based controls of energy storage strategies are proposed to utilize renewable energy as a steady contributor to the electricity market. For reliability-based control, photovoltaic (PV) generation is assumed to be registered in the power generation plan. PV generation yields a reliable output using energy storage units to compensate for PV prediction errors. We also propose a runtime state-ofcharge management method for sustainable operations. With variance-based controls, changes in rapid power generation are limited through ramp rate control. This study introduces new reliability and variance indices as indicators for evaluating these strategies. The reliability index quantifies the degree to which the actual generation realizes the plan, and the variance index quantifies the degree of power change. The two strategies are verified based on simulations and experiments. The reliability index improved by 3.1 times on average over 21 days at a real power plant.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1797-1802
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• 2005

In this study, we were developed an accuracy of the proposed two dimensional statical inclinometer what used a position sensitive detector(PSD) by an error analysis. The inclinometer consists of a laser source, a mass, an optic-fiber, and a PSD. The gravity direction on a base platform of the inclinometer is changed by an unknown inclination angle. And a laser spot is moved from the origin to another position of a PSD following a variation of an optical path by the gravity. These processes enable the inclinometer to estimate the inclination angle from distance information of the moving spot. A design methodology on the basis of a sensitivity analysis was applied to improve the measurement performance such as a full measuring range and a resolution. But it still has error factors, so we analyze the uncertainty of the inclinometer to evaluate the systematic errors from alignments, assembly error and so on. The experimental performance evaluation about the design objectives as a measuring range and a resolution was performed. And the validity and the feasibility of the design process were certified by an experimental process. Systematic errors eliminated to improve the accuracy of the inclinometer by the corrected measuring model from the calibration process between the inclination angle and the PSD position instead of the nominal measuring model. The ANOVA(analysis of variance) confirmed the effect of eliminating the systematic errors in the inclinometer. From these methodologies, the proposed inclinometer was able to measure with a high resolution(35.14sec) and a wide range(from $-15^{\circ}\;to\;15^{\circ}$

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.148-156
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• 2014

In the MVDR (minimum variance distortionless response) adaptive array, its performance could be greatly deteriorated in the presence of steering vector errors as the desired signal is treated as an interference. This paper suggests an computationally simple adaptive beamforming method which is robust against these errors. In the proposed method, a minimization problem that is formulated according to the DCB (doubly constrained beamforming) principle is solved to find a solution vector, which is in turn projected onto a subspace to obtain a new steering vector. The minimization problem and the subspace projection are dealt with using some principal eigenpairs, which are obtained using a modified PASTd(projection approximation subspace tracking with deflation). We improve the existing MPASTd(modified PASTd) algorithm such that the computational complexity is reduced. The proposed beamforming method can significantly reduce the complexity as compared with the conventional ones directly eigendecomposing an estimate of the corelation matrix to find all eigenvalues and eigenvectors. Moreover, the proposed method is shown, through simulation, to provide performance improvement over the conventional ones.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.415-433
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• 1999

This paper considers the design of step-stress accelerated life tests for the Weibull distribution with a nonconstant shape parameter under Type I censoring. It is assumed that scale and shape parameters are log-linear functions of (possibly transformed) stress and that a cumulative exposure model holds for the effect of changing stress. The asymptotic variance of the maximum likelihood estimator of a stated quantile at design stress is used as an optimality criterion. The optimum three step-stress plans are presented for selected values of design parameters and the effects of errors in pre- estimates of the design parameters are investigated.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.31-44
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• 1995

In fitting a model, there always exists a discrepancy between the fitted model and the true functional relationship. In measuring this discrepancy, Box and Drapper (1959) used the criterion dividing the discrepancy into two parts which are the bias error part and the variance error one in single response case. In this paper, an optimum design which makes these two types of errors as small as possible is found by extending the Box and Drapper criterion to multiple response situation. Especially, a design is found to meat rotatability conditions when we fit a quadratic model to each response fearing cubic bias. Using the central composite design, an application of general results to a specific case is shown to help understanding the material.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2516-2520
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• 2005

In this paper, finite impulse response (FIR) smoothers are proposed for discrete-time systems. The proposed FIR smoother is designed under the constraints of linearity, unbiasedness, FIR structure, and independence of the initial state information. It is also obtained by directly minimizing the performance criterion with unbiased constraints. The approach to the MVF smoother proposed in this paper is logical and systematic, while existing results have heuristic assumption, such as infinite covariance of the initial state. Additionally, the proposed MVF smoother is based on the general system model that may have the singular system matrix and has both system and measurement noises. Thorough simulation studies, it is shown that the proposed MVF smoother is more robust against modeling uncertainties numerical errors than fixed-lag Kalman smoother which is infinite impulse response (IIR) type estimator.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.193-200
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• 2014

It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.4
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• pp.133-139
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• 2008

With the increasing demand for higher production quality and growing competition in the global market, coordinate measuring machine(CMM) has been widely used in industry to improve the efficiency and effectiveness of measurement. In this paper the performance evaluation of coordinate measuring machine is proposed using design of experiments. A factorial design is applied to carry out the performance test proposed by ISO 10360 with a length bar and to investigate CMM measurement errors associated to orientation and length in the work volume. The determination of the significance of effects in an experiment is made through the analysis of variance(ANOVA). The results show that the proposed method is suitable to analyze the factors which affect the CMM measurement performance.

• Journal of Soil and Groundwater Environment
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• v.22 no.6
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• pp.57-65
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• 2017

Six soil subsampling methods were evaluated with explosive compounds-contaminated soils to quantify the variance associated with each method. The methods include modified grab sampling, simplified ripple splitting, fractional shoveling, coning & quatering, degenerate fractional shoveling, and rolling & quatering. All the methods resulted in significantly lower CV (coefficient of variation) of 1~5%, compared to common grab sampling that gave 8~98% of CV, possibly due to the reduction of grouping and segregation errors described by Gy sampling theory. Among the methods, simplified ripple splitting tends to result in lower explosive compounds concentrations, while the rolling & quatering gave the opposite result. Fractional shoveling method showed the least variance and the highest reproducibility in the analysis.