• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.4
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• pp.286-293
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• 2019

This study compares and analyzes online update techniques, which estimate the parameters of battery equivalent circuit models in real time. Online update techniques, which are based on extended Kalman filter and recursive least square methods, are constructed by considering the dynamic characteristics of batteries. The performance of the online update techniques is verified by simulation and experiments. Each online update technique is compared and analyzed in terms of complexity and accuracy to propose a suitable guide for selecting algorithms on various types of battery applications.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.333-351
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• 2022

The skew-t distribution is an attractive family of asymmetrical heavy-tailed densities that includes the normal, skew-normal and Student's-t distributions as special cases. In this work, we propose an EM-type algorithm for computing the maximum likelihood estimates for skew-t linear regression models with censored response. In contrast with previous proposals, this algorithm uses analytical expressions at the E-step, as opposed to Monte Carlo simulations. These expressions rely on formulas for the mean and variance of a truncated skew-t distribution, and can be computed using the R library MomTrunc. The standard errors, the prediction of unobserved values of the response and the log-likelihood function are obtained as a by-product. The proposed methodology is illustrated through the analyses of simulated and a real data application on Letter-Name Fluency test in Peruvian students.

• 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.

• Journal of the Korean GEO-environmental Society
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• v.16 no.11
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• pp.29-37
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• 2015

Non-excavation method is needed to secure the stability of existing structures during construction. Therefore, prediction of ground settlement is essential. Causes of settlement when using steel pipe indentation method are leading pipe-steel pipe gap, excessive excavation and soil-steel pipe friction etc. Also they are similar to the causes of settlement when using Shield TBM during construction. In this study, ground settlement during steel pipe indentation is predicted by the Gap Parameter Method and Volume Loss Method which are kinds of Shield TBM prediction Method. and compared with those of prediction methods by conducting field test. As a result, Volume Loss Prediction Method is the most similar to the field tests. However, It is needed to additional studies, such as decision of the factors and adaptability for total settlement predictions of non-excavation method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.540-549
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• 2010

The existing reliability evaluation models which have already developed by the corporations are so various because of using Maximum Likelihood Method. The existing models are very complicated owing to using system designing methods. Therefore, it is very difficult to utilize the existing models in business fields of many corporations. The purposes of this paper are as follows: The first purpose is to study the simple estimated Parameter to be easily utilized in the business fields of the corporations. The second purpose is to testify the simplification of the developed Parameter of estimated method by comparing the developed reliability evaluation model with the existing reliability evaluation models which are used in the business fields of the corporations.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.168-180
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• 2018

In order to achieve the high quality output voltage of single-phase voltage source inverters, in this paper an Adaptive Complementary Sliding Mode Control (ACSMC) is proposed. Firstly, the dynamics model of the single-phase inverter with lumped uncertainty including parameter variations and external disturbances is derived. Then, the conventional Sliding Mode Control (SMC) and Complementary Sliding Mode Control (CSMC) are introduced separately. However, when system parameters vary or external disturbance occurs, the controlling performance such as tracking error, response speed et al. always could not satisfy the requirements based on the SMC and CSMC methods. Consequently, an ACSMC is developed. The ACSMC is composed of a CSMC term, a compensating control term and a filter parameters estimator. The compensating control term is applied to compensate for the system uncertainties, the filter parameters estimator is used for on-line LC parameter estimation by the proposed adaptive law. The adaptive law is derived using the Lyapunov theorem to guarantee the closed-loop stability. In order to decrease the control system cost, an inductor current estimator is developed. Finally, the effectiveness of the proposed controller is validated through Matlab/Simulink and experiments on a prototype single-phase inverter test bed with a TMS320LF28335 DSP. The simulation and experimental results show that compared to the conventional SMC and CSMC, the proposed ACSMC control strategy achieves more excellent performance such as fast transient response, small steady-state error, and low total harmonic distortion no matter under load step change, nonlinear load with inductor parameter variation or external disturbance.

• Smart Structures and Systems
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• v.25 no.5
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• pp.569-580
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• 2020

The identification of delays and delay compensation are critical problems in real-time hybrid simulations (RTHS). Conventional delay compensation methods are mostly based on the assumption of a constant delay. However, the system delay may vary during tests owing to the nonlinearity of the loading system and/or the behavioral variations of the specimen. To address this issue, this study presents an adaptive delay compensation method based on a discrete model of the loading system. In particular, the parameters of this discrete model are identified and updated online with the least-squares method to represent a servo hydraulic loading system. Furthermore, based on this model, the system delays are compensated for by generating system commands using the desired displacements, achieved displacements, and previous displacement commands. This method is more general than the existing compensation methods because it can predict commands based on multiple displacement categories. Moreover, this method is straightforward and suitable for implementation on digital signal processing boards because it relies solely on the displacements rather than on velocity and/or acceleration data. The virtual and real RTHS results show that the studied method exhibits satisfactory estimation smoothness and compensation accuracy. Furthermore, considering the measurement noise, the low-order parameter models of this method are more favorable than that the high-order parameter models.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.2
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• pp.99-109
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• 2023

In this study, a comparative test operation was conducted through the alternate haul method to examine the selectivity of the four mesh sizes (60 mm, 90 mm, 110 mm, and 130 mm) of the trawl codend. The selectivity was analyzed using the SELECT model considering the fishing efficiency (split parameter) of each fishing gear in the comparative test fishing operation in the trawl and the maximum likelihood method for parameter estimation. A selectivity master curve was estimated for several mesh sizes using the extended-SELECT model. As a result of analyzing the selectivity for silver croaker based on the results of three times hauls for each experimental gear, it was found that the size of the fish caught increased as the size of the mesh size increased. When the selectivity for each mesh size analyzed by the SELECT model considering the split ratio was evaluated based on the size of the AIC value, the estimated split model was superior to the equal split model. Based on the master curve, the 50% selection length value was 2.893, which was estimated to be 136 mm based on the mesh size of 60 mm. In some selectivity models, there was a large deviance between observed and theoretical values due to the non-uniformity of the distribution of fished length classes. As a result, it is considered that appropriate sea trials and selectivity evaluation methods with high reliability should be applied to present trawl fishery resource management methods.

• Atmosphere
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• v.24 no.1
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• pp.101-110
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• 2014

This study estimates and evaluates the daily January temperature from 2003 to 2012 with 30 m-resolution over South Korea, using a modified Parameter-elevation Regression on Independent Slopes Model (K-PRISM). Several factors in K-PRISM are also adjusted to 30 m grid spacing and daily time scales. The performance of K-PRISM is validated in terms of bias, root mean square error (RMSE), and correlation coefficient (Corr), and is then compared with that of inverse distance weighting (IDW) and hypsometric methods (HYPS). In estimating the temperature over Jeju island, K-PRISM has the lowest bias (-0.85) and RMSE (1.22), and the highest Corr (0.79) among the three methods. It captures the daily variation of observation, but tends to underestimate due to a high-discrepancy in mean altitudes between the observation stations and grid points of the 30 m topography. The temperature over South Korea derived from K-PRISM represents a detailed spatial pattern of the observed temperature, but generally tends to underestimate with a mean bias of -0.45. In bias terms, the estimation ability of K-PRISM differs between grid points, implying that care should be taken when dealing with poor skill area. The study results demonstrate that K-PRISM can reasonably estimate 30 m-resolution temperature over South Korea, and reflect topographically diverse signals with detailed structure features.