• 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.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.716-723
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• 2019

Fingerprint matching accuracy is essential in deploying a music search service. This paper deals with a method to improve fingerprint matching accuracy by utilizing an auxiliary information which is called power weight. Power weight is an expected robustness of each hash bit. While the previous power mask binarizes the expected robustness into strong and weak bits, the proposed method utilizes a real-valued function of the expected robustness as weights for fingerprint matching. As a countermeasure to the increased storage cost, we propose a compression method for the power weight which has strong temporal correlation. Experiments on the publicly-available music datasets confirmed that the proposed power weight is effective in improving fingerprint matching performance.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.486-489
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• 2002

This paper proposes a tabu search (TS) algorithm to optimal weight selection in design of robust H$_{\infty}$ power system stabilize. (PSS), In H$_{\infty}$ control design, the weight selection and the representation of system uncertainties are the major difficulties. To cope with these problems, TS is employed to automatically search for the optimal weights. On the other hand, the normalized coprime factorization (NCF) is used. The H$_{\infty}$ controller can be directly developed without ${\gamma}$-iteration. Also, the pole-zero cancellation phenomena are prevented. The performance and robustness of the proposed PSS under different loading conditions are investigated in comparison with a robust tuned PSS by examining the case of a single machine infinite bus (SMIB) system. The simulation results illustrate the effectiveness and robustness of the proposed PSS.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.839-844
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• 1992

There are many specific requirements in the actuation, system for high speed underwater vehicle, such as size, weights, power etc.. In this paper, a high performance compact hydraulic actuation system to satisfy such requirements was designed. The controller of the system was designed using both the conventional PID and VSC which were known to have reliability, robustness respectively. The performance analysis was done for the designed actuation system through computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.630-637
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• 2003

This paper proposed a new watermarking algorithm using chaotic sequence instead of conventional M-sequence for protecting copyright to the author. Robustness and security is very important for watermarking process. We use multi-threshold value according to the human visual system for improving robustness of watermarking to each subband images coefficient differently after wavelet transform. And then, we embedded watermark image to original image by multi-watermark weights which are made by random sequence generator. We detect watermark image from the difference data which is made from each wavelet subband images. We also simulate the efficiency from the various possible attacks. Chaotic sequence is better than M-sequence, because the one is very easy to make sequence and the chaotic sequence is changed easy according to the initial value. So, the chaotic sequence has the better security than the conventional M-sequence.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.541-550
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• 2010

The $L_1$-regression estimator is susceptible to the leverage points, even though it is highly robust to the vertical outliers. This article is concerned with the improvement of robustness of the $L_1$-estimator. To improve its robustness, in terms of the breakdown point, we attempt to dampen the influence of the leverage points by means of reducing the weights corresponding to the leverage points. In addition the algorithm employs the linear scaling transformation technique, for higher computational efficiency with the large data sets, to solve the linear programming problem of $L_1$-estimation. Monte Carlo simulation results indicate that the proposed algorithm yields $L_1$-estimates which are robust to the leverage points as well as the vertical outliers.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.2
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• pp.159-163
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• 2004

Data pertaining to first lactation records of 316 Murrah buffaloes, progeny of 47 sires, maintained at NDRI Farm for a period of 18 years were analysed to construct selection indices and to examine their robustness by changing the relative economic values of different economic traits. A total of 120 selection indices were constructed for three sets of relative economic values ( 40 for each set) considering different combinations of seven first lactation traits viz. age at first calving (AFC), first lactation 305 day or less milk yield (FLMY), first lactation length (FLL), first calving interval (FCI), milk yield per day of first lactation length (MY/FLL), milk yield per day of first calving interval (MY/FCI) and milk yield per day age at second calving (MY/ASC). The three sets of relative economic values were based on economic values of different traits, 1% standard deviation of different traits and regression of different traits on FLMY. The 'optimum' indices for the first two sets had five traits each namely AFC, FLMY, FLL, FCI and MY/ASC giving improvement in aggregate genotype of Rupees 269.11 and Rs. 174.88, respectively. The accuracy of selection from both indices was 70.79 and 69.39%, respectively. The 'best' selection index from the third set of data again had five traits (AFC, FLMY, FLL, FCI and MY/FLL) giving genetic gain of Rs. 124.16 and accuracy of selection of 71.81%. The critcal levels or break-even points for FLMY for varying levels of AFC and FCI estimated from the "optimum index" suggested the need of enhancement of present production level of the herd or reduction of AFC or FCI. It was concluded that economic values of various first lactation traits were the most appropriate to construct selection indices as compared to other criteria of assigning relative economic weights in Murrah buffaloes.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.501-508
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• 2000

It is demanded to find the dynamic model of a real structure to design a controller. However, as the structure has inherently infinite number of degree-of-freedom, it is impossible to obtain an exact dynamic model of the structure. Instead a reduction model with finite degree-of-freedom is used for the design of a controller. So there exists uncertainty between a real model and a reduction model which causes poor performance of control. All these uncertainties can degrade the control performance and even cause the control instability. Thus, robust control strategy considering the above uncertainties can be an alternative one to guarantee the performance and stability of the control. This study deals with the experimental verification of robust controller design for the active mass driver. $\mu$-synthesis technique is employed as a robust control strategy. Some weights are chosen based on the difference between the initial plant with which the controller is designed and the perturbed plant to be controlled having the actuator uncertainty. The robustness of $\mu$-synthesis technique is compared with the result of LQG strategy, which does not consider the uncertainty.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1033-1039
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• 2007

In this paper, we design a terminal sliding mode controller based on self-recurrent wavelet neural network (SRWNN) for the second-order nonlinear systems with model uncertainties. The terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time in comparison with the classical sliding mode control (CSMC) method. In addition, the TSMC method has advantages such as the improved performance, robustness, reliability and precision. We employ the SRWNN to approximate model uncertainties. The weights of SRWNN are trained by adaptation laws induced from Lyapunov stability theorem. Finally, we carry out simulations for Duffing system and the wing rock phenomena to illustrate the effectiveness of the proposed control scheme.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.