• Title/Summary/Keyword: regularized

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Regularized LS Signal Detection for OFDM in Fast Time Varying Channels (고속 시변 채널 OFDM을 위한 안정화된 LS 신호검출)

  • Lim, Dongmin
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.1
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    • pp.83-85
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    • 2016
  • The OFDM with LS signal detection performs worse in fast time varying channels as the channel matrix has higher chance of becoming ill-conditioned. Various regularization methods are applied to avoid performance degradation in LS signal detection. In this paper, we proposed a CGLS method with the stopping criteria imposed by the characteristics of the modulation method, which shows performance comparable to that of the optimal LMMSE.

Estimation of Hysteretic Behaviors of a Seismic Isolator Using a Regularized Output Error Estimator (정규화된 OEE를 이용한 지진격리장치의 이력거동 추정)

  • 박현우;전영선;서정문
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • 2003.03a
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    • pp.85-92
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    • 2003
  • Hysteretic behaviors of a seismic isolator are identified by using the regularized output error estimator (OEE) based on the secant stiffness model. A proper regularity condition of tangent stiffness for the current OEE is proposed considering the regularity condition of Duhem hysteretic operator. The proposed regularity condition is defined by 12-norm of the tangent stiffness with respect to time. The secant stiffness model for the OEE is obtained by approximating the tangent stiffness under the proposed regularity condition by the secant stiffness at each time step. A least square method is employed to minimize the difference between the calculated response and measured response for the OEE. The regularity condition of the secant stiffness is utilized to alleviate ill-posedness of the OEE and to yield numerically stable solutions through the regularization technique. An optimal regularization factor determined by geometric mean scheme (GMS) is used to yield appropriate regularization effects on the OEE.

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How to identify fake images? : Multiscale methods vs. Sherlock Holmes

  • Park, Minsu;Park, Minjeong;Kim, Donghoh;Lee, Hajeong;Oh, Hee-Seok
    • Communications for Statistical Applications and Methods
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    • v.28 no.6
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    • pp.583-594
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    • 2021
  • In this paper, we propose wavelet-based procedures to identify the difference between images, including portraits and handwriting. The proposed methods are based on a novel combination of multiscale methods with a regularization technique. The multiscale method extracts the local characteristics of an image, and the distinct features are obtained through the regularized regression of the local characteristics. The regularized regression approach copes with the high-dimensional problem to build the relation between the local characteristics. Lytle and Yang (2006) introduced the detection method of forged handwriting via wavelets and summary statistics. We expand the scope of their method to the general image and significantly improve the results. We demonstrate the promising empirical evidence of the proposed method through various experiments.

Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

  • Xiaochun Zhang;Zhiyu Zhou
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.3
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    • pp.816-839
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    • 2023
  • In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

FPGA Modem Platform Design for eHSPA and Its Regularized Verification Methodology (eHSPA 규격을 만족하는 FPGA모뎀 플랫폼 설계 및 검증기법)

  • Kwon, Hyun-Il;Kim, Kyung-Ho;Lee, Chung-Yong
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.46 no.2
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    • pp.24-30
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    • 2009
  • In this paper, the FPGA modem platform complying with 3GPP Release 7 eHSPA specifications and its regularized verification flow are proposed. The FFGA platform consists of modem board supporting physical layer requirements, MCU and DSP core embedded control board to drive the modem board, and peripheral boards for RF interfacing and various equipment interfaces. On the other hand, the proposed verification flow has been regularized into three categories according to the correlation degrees of hardware-software inter-operation, such as simple function test, scenario test call processing and system-level performance test. When it comes to real implementations, the emulation verification strategy for low power mobile SoC is also introduced.

Effective Reconstruction of Stereo Image through Regularized Adaptive Disparity Estimation Scheme (평활화된 적응적 변이추정 기법을 이용한 스테레오 영상의 효과적인 복원)

  • Kim, Yong-Ok;Bae, Kyung-Hoon;Kim, Eun-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.4C
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    • pp.424-432
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    • 2003
  • In this paper, an effective method of stereo image reconstruction through the regularized adaptive disparity estimation is proposed. Althougth the conventional adaptive disparity estimation method can sharply improve the PSNR of a reconstructed stereo image, but some problems of overlapping between the matching windows and disallocation of the matching windows can be occurred, because the matching window size changes adaptively in accordance with the magnitude of feature values. Accordingly, in thia paper, a new regularized adaptive disparity estimation technique is proposed. That is, by regularizing the estimated disparity vector with the neughboring disparity vectors, problems of the conventional adaptive disparity estimated scheme might be solved, and also the predicted stereo image can be more effectively reconstructed. From some experiments using the CCETT'S stereo image pairs of 'Man' and 'Claude', it is analyzed that the proposed disparity estimation scheme can improve PSNRs of the reconstructed images to 10.89dB, 6.13dB for 'Man' and 1.41dB, 0.81dB for 'Claude' by comparing with those of the conventional pixel-based and adaptive estimation method, respectively.

An improved regularized particle filter for remaining useful life prediction in nuclear plant electric gate valves

  • Xu, Ren-yi;Wang, Hang;Peng, Min-jun;Liu, Yong-kuo
    • Nuclear Engineering and Technology
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    • v.54 no.6
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    • pp.2107-2119
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    • 2022
  • Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

Filter-Bank Based Regularized Common Spatial Pattern for Classification of Motor Imagery EEG (동작 상상 EEG 분류를 위한 필터 뱅크 기반 정규화 공통 공간 패턴)

  • Park, Sang-Hoon;Kim, Ha-Young;Lee, David;Lee, Sang-Goog
    • Journal of KIISE
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    • v.44 no.6
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    • pp.587-594
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    • 2017
  • Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

CONVERGENCE OF EXPONENTIALLY BOUNDED C-SEMIGROUPS

  • Lee, Young S.
    • Korean Journal of Mathematics
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    • v.9 no.2
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    • pp.115-121
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    • 2001
  • In this paper, we establish the conditions that a mild C-existence family yields a solution to the abstract Cauchy problem. And we show the relation between mild C-existence family and C-regularized semigroup if the family of linear operators is exponentially bounded and C is a bounded injective linear operator.

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