• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.9-22
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• 2019

In this paper, we propose a method that can be applied to the efficient implementation of multi-channel active noise controller. Since the normalized MFxLMS (Modified Filtered-x Least Mean Square) algorithm for the multi-channel active noise control requires a large amount of computation, the difficulty has lied in implementing the algorithm using a low-cost MCU (Microcontoller Unit). We implement the multi-channel active noise controller efficiently by optimizing the software based on the features of the MCU. By maximizing the usage of single-cycle MAC (Multiply- Accumulate) operations and minimizing move operations of the delay memory, we can achieve more than 3 times the performance in the aspect of computational optimization, and by parellel processing using the auxillary processor included in the MCU, we can also obtain more than 4 times the performance. In addition, the usage of additional parts can be minimized by maximizing the usage of the peripherals embedded in the MCU.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.305-344
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• 2008

This paper presents the active control of flexible beam vibration. The beam was excited by a steady-state point force by mini shaker and the control was performed by mini shaker. To perform active control, least-mean-square (LMS) algorithm was used because it can easily obtain the complex transfer function in real-time. So an adaptive controller based on Filtered-X LMS algorithm was used and the controller was defined by minimizing the square of the response at a location of error sensor. In order to fine out performance tendency, input amplitude was changed in several cases and active vibration control was performed.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.984-990
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• 2013

This paper develops a new Intelligent Electronic Device (IED), and then presents an application method of a monthly load forecasting algorithm on the smart IEDs. A Multiple Linear Regression (MLR) model implemented with Recursive Least Square (RLS) estimation is established in the algorithm. Case Study proves the accuracy and reliability of this algorithm and demonstrates the practical meanings through designed screens. The application method shows the general way to make use of IED's smart characteristics and thereby reveals a broad prospect of smart function realization in application.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.69-72
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• 1999

In this paper, an efficient time domain equalization algorithm for discrete wavelet multitone(DWMT) data transmission is developed. In this algorithm, the time domain equalizer(TEQ) consists of two stages, i.e., the channel impulse response shortening equalizer(TEQ-S) in the first stage and the channel frequency flattening equalizer(TEQ-F) in the second stage. TEQ-S reduces the length of transmission channel impulse response to decrease intersymbol interference(ISI) followed by TEQ-F that enhances the channel frequency response characteristics to the level of an ideal channel, hence diminishes the bit error rate. TEQ-S is implemented using the least-squares(LS) method, while TEQ-F is designed by using the least mean-square(LMS) algorithm. Since DWMT system also requires of the frequency domain equalizer in order to further reduce ICI and ISI the hardware complexity is an another concern. However, by adopting an well designed and trained TEQ, the hardware complexity of the whole DWMT system can be greatly reduced.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.14-20
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• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• Wind and Structures
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• v.17 no.4
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3C
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• pp.183-189
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• 2011

The LMS(Least Mean Square) algorithm is generally used because of tenacity, high mating spots and simplicity of realization. But the LMS algorithm has trade-off between nonuniform collect and EMSE(Excess Mean Square Error). To overcome this weakness, variable step size is used widely but it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and adapt in case of environment changes, uses original signal and noise signal of IGC(Instantaneous Gain Control). For the real time processing of IGC algorithm, we remove the logarithmic function. The performance of proposed algorithm is tested to adaptive noise canceller in automobile. We show implemented LabVIEW FPGA system of IGC algorithm is more efficient than others.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.184.1-184.1
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• 2005

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.11
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• pp.529-534
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• 1986

In recent year, there have been much techniques for designing globally stable adaptive control systems when the unknown plant is minimum phase. This paper presents a new seheme for indirect adaptive control structure for nonminimum phase plant of which parameters are estimated from the least-square algorithm. Its effectiveness has been demonstrated by computer simulation carried out for a second-order plant.