• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.11
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• pp.819-825
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• 1987

The ALE problem, which tries to recover a sinusoidal signal corrupted by noise, has been solved using FIR filters. Recently several methods have been proposed using a norch filter of IIR type. In this study, the notch filter was represented with a parameter and auxiliary signals were generated by using an adaptive observer. A simple method is proposed to estimate the parameter. This method is tested under various circumstances by changing the input frequency, S/N ratio, and the type of the noise. The simulation shows that this method gives much better results than the other known methods with respect to the input S/N ratio and converging times. This method is simple and does not require much conputation, so it can be easily implemented in real time applications.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1549-1557
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• 2018

Optimizing the design of large-scale electric machines based on nonlinear finite element analysis (FEA) requires longer computation time than other applications of FEA, mainly due to the huge size of the machines. This paper addresses a new region decision method (RDM) with mesh adaptive direct search (MADS) for the optimal design of wind generators in order to reduce the computation time. The validity of the proposed algorithm is evaluated using Rastrigin and Goldstein-Price benchmark function. Moreover, the algorithm is employed for the optimal design of a 5.6MW interior permanent magnet synchronous generator to minimize the torque ripple. Additionally, mechanical stress analysis as well as electromagnetic field analysis have been implemented to prevent breakdown caused by large centrifugal forces of the modified design.

• Journal of Power Electronics
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• v.14 no.4
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• pp.687-694
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• 2014

This paper focuses on the analysis and compensation of thrust ripples in permanent magnet linear synchronous motors (PMLSM). The main drawback in PMLSMs is the presence of thrust ripples, which are mainly due to the interaction between the permanent magnets and armature slotted core. These thrust ripples reduce the performance of the drive system in high precision applications especially at low speeds. This paper analyzes thrust ripples using the discrete wavelet transform. These undesired thrust ripples are compensated by using an adaptive feed forward controller. It is observed that this novel controller reduces about 65 percent of the thrust ripples. An extensive simulation is performed through MATLAB and it is validated through experimental results using a d-SPACE system with a DS1104 control board.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.753-762
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• 2002

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor dose it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics in investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparisons with existing algorithms are shown for real measured digital microwave channel.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.110-115
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• 2019

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.307-312
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• 2021

This paper proposes an adaptive time-delayed control approach with the integral sliding-mode surface for the fast convergence rate of robot manipulators. Adaptive switching gain aims to guarantee the system stability in such a way as to suppress time-delayed estimation error in the proposed control approach. Moreover, it makes an effort to increase the convergence ability in reaching the phase. An integral sliding-mode surface is employed to achieve a fast convergence rate in the sliding phase. The stability of the proposed one is proved to be asymptotically stable in the Lyapunov stability. The efficiency of the proposed control approach is illustrated with a tutorial example in robot manipulator, which is compared to that of the existing control approach.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.281-294
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• 2021

In this paper, we propose an adaptive regression method based on the single-layer neural network structure. We adopt a symmetric activation function as units of the structure. The activation function has a flexibility of its form with a parametrization and has a localizing property that is useful to improve the quality of estimation. In order to provide a spatially adaptive estimator, we regularize coefficients of the activation functions via ℓ₁-penalization, through which the activation functions to be regarded as unnecessary are removed. In implementation, an efficient coordinate descent algorithm is applied for the proposed estimator. To obtain the stable results of estimation, we present an initialization scheme suited for our structure. Model selection procedure based on the Akaike information criterion is described. The simulation results show that the proposed estimator performs favorably in relation to existing methods and recovers the local structure of the underlying function based on the sample.

• Microbiology and Biotechnology Letters
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• v.48 no.4
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• pp.423-428
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• 2020

In vitro evolution is a powerful technique for the engineering of yeast strains to study cellular mechanisms associated with evolutionary adaptation; strains with desirable traits for industrial processes can also be generated. There are two distinct approaches to generate evolved strains in vitro: the sequential transfer of cells in the stationary phase into fresh medium or the continuous growth of cells in a chemostat bioreactor via the constant supply of fresh medium. In culture, evolutionary forces drive diverse adaptive mechanisms within the cell to overcome environmental or intracellular stressors. Especially, this engineering strategy has expanded to the field of human cell lines; the understanding of such adaptive mechanisms provides promising targets for the treatment of human genetic diseases and cancer. Therefore, this technology has the potential to generate numerous industrial, medical, and academic applications.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.