• Title/Summary/Keyword: Subspace

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Blind Adaptive Multiuser Detection for the MC-CDMA Systems Using Orthogonalized Subspace Tracking

  • Ali, Imran;Kim, Doug-Nyun;Lim, Jong-Soo
    • ETRI Journal
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    • v.31 no.2
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    • pp.193-200
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    • 2009
  • In this paper, we study the performance of subspace-based multiuser detection techniques for multicarrier code-division multiple access (MC-CDMA) systems. We propose an improvement in the PASTd algorithm by cascading it with the classical Gram-Schmidt procedure to orthonormalize the eigenvectors after their sequential extraction. The tracking of signal subspace using this algorithm, which we call OPASTd, has a faster convergence as the eigenvectors are orthonormalized at each discrete time sample. This improved PASTd algorithm is then used to implement the subspace blind adaptive multiuser detection for MC-CDMA. We also show that, for multiuser detection, the complexity of the proposed scheme is lower than that of many other orthogonalization schemes found in the literature. Extensive simulation results are presented and discussed to demonstrate the performance of the proposed scheme.

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Codebook design for subspace distribution clustering hidden Markov model (Subspace distribution clustering hidden Markov model을 위한 codebook design)

  • Cho, Young-Kyu;Yook, Dong-Suk
    • Proceedings of the KSPS conference
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    • pp.87-90
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    • 2005
  • Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

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SIX SOLUTIONS FOR THE SEMILINEAR WAVE EQUATION WITH NONLINEARITY CROSSING THREE EIGENVALUES

  • Choi, Q-Heung;Jung, Tacksun
    • Korean Journal of Mathematics
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    • v.20 no.3
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    • pp.361-369
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    • 2012
  • We get a theorem which shows the existence of at least six solutions for the semilinear wave equation with nonlinearity crossing three eigenvalues. We obtain this result by the variational reduction method and the geometric mapping defined on the finite dimensional subspace. We use a contraction mapping principle to reduce the problem on the infinite dimensional space to that on the finite dimensional subspace. We construct a three-dimensional subspace with three axes spanned by three eigenvalues and a mapping from the finite dimensional subspace to the one-dimensional subspace.

GEOMETRIC RESULT FOR THE ELLIPTIC PROBLEM WITH NONLINEARITY CROSSING THREE EIGENVALUES

  • Jung, Tacksun;Choi, Q-Heung
    • Korean Journal of Mathematics
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    • v.20 no.4
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    • pp.507-515
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    • 2012
  • We investigate the number of the solutions for the elliptic boundary value problem. We obtain a theorem which shows the existence of six weak solutions for the elliptic problem with jumping nonlinearity crossing three eigenvalues. We get this result by using the geometric mapping defined on the finite dimensional subspace. We use the contraction mapping principle to reduce the problem on the infinite dimensional space to that on the finite dimensional subspace. We construct a three dimensional subspace with three axis spanned by three eigenvalues and a mapping from the finite dimensional subspace to the one dimensional subspace.

Genetic Algorithm based Hybrid Ensemble Model (유전자 알고리즘 기반 통합 앙상블 모형)

  • Min, Sung-Hwan
    • Journal of Information Technology Applications and Management
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    • v.23 no.1
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    • pp.45-59
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    • 2016
  • An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

On Construction of Anti-jam and Multipath Mitigation GNSS receiver by Subspace Projection (Subspace Projection을 이용한 전파방해신호 제거와 다중경로 간섭신호 제거 GNSS 수신기 설계)

  • Shin, Jeong-Hwan;Heo, Jun
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.43 no.12
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    • pp.24-30
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    • 2006
  • This paper considers interference suppression and multipath mitigation in Global Navigation Satellite Systems (GNSSs). We propose an anti-jam GNSS receiver which suppresses interference and multipath by subspace projection method. The resulting interference suppressed and multipath mitigated signal is then process by a beamformer, whose weight vector maximizes the signal-to-noise ratio of the output signal. The enhanced performance is shown by refined cross correlation and beam pattern.

Identification of Closed Loop System by Subspace Method (부분공간법에 의한 페루프 시스템의 동정)

  • Lee, Dong-Cheol;Bae, Jong-Il;Hong, Soon-Il;Kim, Jong-Kyung;Jo, Bong-Kwan
    • Proceedings of the KIEE Conference
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    • pp.2143-2145
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    • 2003
  • In the linear system identification using the discrete time constant coefficients, there is a subspace method based on 4SID recently much suggested instead of the parametric method like as the maximum likelihood method. The subspace method is not related with the impulse response and difference equation in its input-output equation, but with the system matrix of the direct state space model from the input-output data. The subspace method is a very useful tool to adopt in the multivariable system identification, but it has a shortage unable to adopt in the closed-loop system identification. In this paper, we are suggested the methods to get rid of the shortage of the subspace method in the closed-loop system identification. The subspace method is used in the estimate of the output prediction values from the estimating of the state space vector. And we have compared the results with the outputs of the recursive least square method in the numerical simulation.

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Development of System Analysis for the Application of MDO to Crashworthiness (자동차 충돌문제에 MDO를 적용하기 위한 시스템 해석 방법 개발)

  • 신문균;김창희;박경진
    • Transactions of the Korean Society of Automotive Engineers
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    • v.11 no.5
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    • pp.210-218
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    • 2003
  • MDO (multidisciplinary design optimization) technology has been proposed and applied to solve large and complex optimization problems where multiple disciplinaries are involved. In this research. an MDO problem is defined for automobile design which has crashworthiness analyses. Crash model which are consisted of airbag, belt integrated seat (BIS), energy absorbing steering system .and safety belt is selected as a practical example for MDO application to vehicle system. Through disciplinary analysis, vehicle system is decomposed into structure subspace and occupant subspace, and coupling variables are identified. Before subspace optimization, values of coupling variables at given design point must be determined with system analysis. The system analysis in MDO is very important in that the coupling between disciplines can be temporary disconnected through the system analysis. As a result of system analysis, subspace optimizations are independently conducted. However, in vehicle crash, system analysis methods such as Newton method and fixed-point iteration can not be applied to one. Therefore, new system analysis algorithm is developed to apply to crashworthiness. It is conducted for system analysis to determine values of coupling variables. MDO algorithm which is applied to vehicle crash is MDOIS (Multidisciplinary Design Optimization Based on Independent Subspaces). Then, structure and occupant subspaces are independently optimized by using MDOIS.

Subspace search mechanism and cuckoo search algorithm for size optimization of space trusses

  • Kaveh, A.;Bakhshpoori, T.
    • Steel and Composite Structures
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    • v.18 no.2
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    • pp.289-303
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    • 2015
  • This study presents a strategy so-called Subspace Search Mechanism (SSM) for reducing the computational time for convergence of population based metaheusristic algorithms. The selected metaheuristic for this study is the Cuckoo Search algorithm (CS) dealing with size optimization of trusses. The complexity of structural optimization problems can be partially due to the presence of high-dimensional design variables. SSM approach aims to reduce dimension of the problem. Design variables are categorized to predefined groups (subspaces). SSM focuses on the multiple use of the metaheuristic at hand for each subspace. Optimizer updates the design variables for each subspace independently. Updating rules require candidate designs evaluation. Each candidate design is the assemblage of responsible set of design variables that define the subspace of interest. SSM is incorporated to the Cuckoo Search algorithm for size optimizing of three small, moderate and large space trusses. Optimization results indicate that SSM enables the CS to work with less number of population (42%), as a result reducing the time of convergence, in exchange for some accuracy (1.5%). It is shown that the loss of accuracy can be lessened with increasing the order of complexity. This suggests its applicability to other algorithms and other complex finite element-based engineering design problems.

MUSIC-Based Direction Finding through Simple Signal Subspace Estimation (간단한 신호 부공간 추정을 통한 MUSIC 기반의 효과적인 도래방향 탐지)

  • Choi, Yang-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.4
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    • pp.153-159
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    • 2011
  • The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.