• Journal of IKEEE
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• v.27 no.4
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• pp.430-438
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• 2023

The Modular Multilevel Converter (MMC) has emerged as a key component in HVDC systems due to its ability to efficiently transmit large amounts of power over long distances. In such systems, accurate estimation of the MMC capacitor voltage is of utmost importance for ensuring optimal system performance, stability, and reliability. Traditional methods for voltage estimation may face limitations in accuracy and robustness, prompting the need for innovative approaches. In this paper, we propose a novel distributed neural network observer specifically designed for MMC capacitor voltage estimation. Our observer harnesses the power of a multi-layer neural network architecture, which enables the observer to learn and adapt to the complex dynamics of the MMC system. By utilizing a distributed approach, we deploy multiple observers, each with its own set of neural network layers, to collectively estimate the capacitor voltage. This distributed configuration enhances the accuracy and robustness of the voltage estimation process. A crucial aspect of our observer's performance lies in the meticulous initialization of random weights within the neural network. This initialization process ensures that the observer starts with a solid foundation for efficient learning and accurate voltage estimation. The observer iteratively updates its weights based on the observed voltage and current values, continuously improving its estimation accuracy over time. The validity of proposed algorithm is verified by the result of estimated voltage at each observer in capacitor of MMC.

• Structural Monitoring and Maintenance
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• v.10 no.4
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• pp.361-379
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• 2023

In cable structure maintenance, particularly for cable-stayed bridges, cable safety assessment relies on estimating cable tension. Conventionally, in Japan, cable tension is estimated from the natural frequencies of the cable using the higher-order vibration method. In recent years, dampers have been installed on cables to reduce cable vibrations. Because the higher-order vibration method is a method for damper-free cables, the damper must be removed to measure the natural frequencies of a cable without a damper. However, cables on some cable-stayed bridges have two dampers: one on the girder side and another on the tower side. Notably, removing and reinstalling the damper on the tower side are considerably more time- and labor-intensive. This paper introduces a tension estimation method for cables with two dampers, using natural frequencies. The proposed method was validated through numerical simulation and experiment. In the numerical tests, without measurement error in the natural frequencies, the maximum estimation error among 100 models was 3.3%. With measurement error of 2%, the average estimation error was within 5%, with a maximum error of 9%. The proposed method has high accuracy because the higher-order vibration method for a damper-free cable still has an estimation error of 5%. The experimental verification emphasizes the importance of accurate damper modeling, highlighting potential discrepancies between existing damper design formula and actual damper behavior. By revising the damper formula, the proposed method achieved accurate cable tension estimation, with a maximum estimation error of approximately 10%.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.157-166
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• 2006

This research proposes an efficient Monte Carlo algorithm for computing error probability in high performance digital communication st stems. It characterizes special features of the problem and suggests an importance sampling algorithm specially designed to handle the problem. It uses a shifted exponential density as the importance sampling density, and shows an adaptive way of choosing the rate and the origin of the shifted exponential density. Instead of equal allocation, an intelligent allocation of the samples is proposed so that more samples are allocated to more important part of the error probability. The algorithm uses the nested feature of the error space and avoids redundancy in estimating the probability. The algorithm is applied to an example data set and shows a great improvement in accuracy of the error probability estimation.

• Journal of Korean Society of Transportation
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• v.9 no.2
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• pp.127-134
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• 1991

본 논문의 목적은 교통망 분석에 있어서 중요한 그러나 흔희 발생하지 않는 사건의 발생확률을 추정하는 방법론 개발에 있다. 예를 들어, 안정적(stable) 교통망에서 일시적인 혼잡현상이 발생할 확률을 씨뮬레이숀을 통해 추정하는 방법에 관한 것이다. 이 분야에서 활발한 연구([3], [12]) 가 있어 왔으나 개괄적(Heuristic) 방법에 제한되어 있었다. 본 논문은 위 문제에 대하여 포괄적(unified)이며 이론적인 방법론을 제시하였다. 이를 위해 대 분산이론(Large Deviation Theory)과 중요표본추출(Importance Sampling)기법이 이용되었으며 예로서 사용된 망은 두개의 구간이 이어진 교통망이다. 부수적으로 혼잡현상의 가장 대표적 형태를 구하는 방법이 제시되었다.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.257-269
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• 2001

The simulation is used to estimate an overflow probability in a stable parallel network with coupled inputs. Since the general simulation needs extremely many trials to obtain such a small probability, the fast simulation is proposed to reduce trials instead. By using the Cramer’s theorem, we first obtain an optimally changed measure under which the variance of the estimator is minimized. Then, we use it to derive an importance sampling estimator of the overflow probability which enables us to perform the fast simulation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.391-398
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• 2023

In this paper, a penalized maximum likelihood estimation (PMLE) method that applies a penalty to increase the accuracy of a basis-screening-based Kriging model (BSKM) is introduced. The maximum order and set of basis functions used in the BSKM are determined according to their importance. In this regard, the cross-validation error (CVE) for the basis functions is employed as an indicator of importance. When constructing the Kriging model (KM), the maximum order of basis functions is determined, the importance of each basis function is evaluated according to the corresponding maximum order, and finally the optimal set of basis functions is determined. This optimal set is created by adding basis functions one by one in order of importance until the CVE of the KM is minimized. In this process, the KM must be generated repeatedly. Simultaneously, hyper-parameters representing correlations between datasets must be calculated through the maximum likelihood evaluation method. Given that the optimal set of basis functions depends on such hyper-parameters, it has a significant impact on the accuracy of the KM. The PMLE method is applied to accurately calculate hyper-parameters. It was confirmed that the accuracy of a BSKM can be improved by applying it to Branin-Hoo problem.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.643-646
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• 1994

A method of trajectory error estimation of a hypersonic vehicle, by a covariance analysis technique is presented and discussed. The method itself is a wellkown technique, however, the thema has been rarely treated. As the importance is increasing, it is explained here and some of our newly deviced techniques are also presented.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.65-76
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• 1995

The importance of left truncated and right censoring cases has considered for better information in medical follow-up and engineering life testing studies. We propose some estimation procedure for the mean residual life function with consistency and asymptotic normality on the left truncated and right censoring model. And then, the comparision with Kaplan-Meier estimator ignoring the left truncated effect and the small sample properities are investigated by asymptotic biases and M.S.E.'s thresh Monte Carlo study.

• Journal of Korean Society for Quality Management
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• v.23 no.2
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• pp.91-102
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• 1995

For two independent random variables X and Y, the functional R=Pr[X>Y] is of practical importance in reliability. X can be interpreted as the strength of a component subjected to a stress Y, and R is the component's reliability. In this paper nonparametric approach to estimation of R based on censored observations in the strength variables is analyzed and compared by simulations in the moderate sample sizes.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.193-194
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• 2017

Effect of variable factors on carbon emissions in construction industry is hard to analysis. Therefore this study analyzies effect of variable factors on carbon emissions. This study shows importance of variable factors and emphasizes need of estimation of carbon emissions considering variable factors.