• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.703-719
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• 2011

In multi-user wireless communication systems, adaptive modulation and scheduling are promising techniques for increasing the system throughput. However, a mass of wireless recourse will be occupied and spectrum efficiency will be decreased to feedback channel quality indication (CQI) of all users in every subcarrier or chunk for adaptive orthogonal frequency division multiplexing (OFDM) systems. Thus numerous limited feedback schemes are proposed to reduce the system overhead. The recently proposed compressive sensing (CS) theory provides a new framework to jointly measure and compress signals that allows less sampling and storage resources than traditional approaches based on Nyquist sampling. In this paper, we proposed two novel CQI feedback schemes based on general CS and subspace CS, respectively, both of which could be used in a wireless OFDM system. The feedback rate with subspace CS is greatly decreased by exploiting the subspace information of the underlying signal. Simulation results show the effectiveness of the proposed methods, with the same feedback rate, the throughputs with subspace CS outperform the discrete cosine transform (DCT) based method which is usually employed, and the throughputs with general CS outperform DCT when the feedback rate is larger than 0.13 bits/subcarrier.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1822-1830
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• 2002

The paper describes the development of a decomposition based multidisciplinary design optimization (MDO) method that coordinates each of disciplinary subspace optimization (DSO). A multidisciplinary design system considered in the present study is decomposed into a number of subspaces based on their own design objective and constraints associated with engineering discipline. The coupled relations among subspaces are identified by interdisciplinary design variables. Each of subsystem level optimization, that is DSO would be performed in parallel, and the system level coordination is determined by the first order optimal sensitivities of subspace objective functions with respect to interdisciplinary design variables. The central of the present work resides on the formulation of system level coordination strategy and its capability in decomposition based MDO. A fluid-structure coupled design problem is explored as a test-bed to support the proposed MDO method.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07d
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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.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.182-191
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• 2001

The paper describes the improvement on concurrent subspace optimization(CSSO) via automatic differentiation. CSSO is an efficient strategy to coupled multidisciplinary design optimization(MDO), wherein the original design problem is non-hierarchically decomposed into a set of smaller, more tractable subspaces. Key elements in CSSO are consisted of global sensitivity equation, subspace optimization, optimum sensitivity analysis, and coordination optimization problem that require frequent use of 1st order derivatives to obtain design sensitivity information. The current version of CSSO adopts automatic differentiation scheme to provide a robust sensitivity solution. Automatic differentiation has numerical effectiveness over finite difference schemes tat require the perturbed finite step size in design variable. ADIFOR(Automatic Differentiation In FORtran) is employed to evaluate sensitivities in the present work. The use of exact function derivatives facilitates to enhance the numerical accuracy during the iterative design process. The paper discusses how much the automatic differentiation based approach contributes design performance, compared with traditional all-in-one(non-decomposed) and finite difference based approaches.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.164-171
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• 2012

ESPRIT(Estimation of Signal Parameter via Rotational Invariance Techniques) estimates DOAs(directions of arrival) of the incident signals on a sensor array by exploiting the shift invariance between its two subarrays. This paper suggests an efficient DOA estimation method based on ESPRIT when coherent signals impinge on the sensor array. When applying ESPRIT, it is necessary to find a signal subspace. Though the widely known SS(spatial smoothing) method allows us to obtain a signal subspace in the presence of coherent signals, its computational complexity is very high. Recently a CV(correlation vector) based method has been presented which is computationally simple. However, the number of resolvable signals in the method is smaller than that in the SS based method when multiple coherent signal groups are present. The proposed method in this paper, which obtains a signal subspace by utilizing only part of the correlation matrix, significantly reduces the computational complexity as compared with the SS based one, while the former is resolving the same number of coherent signals as the latter,

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.306-314
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• 2004

Some receiver(and most transmit) beamforming algorithms with an array antenna at a cellular CDMA base stations require accurate internal and external calibrations. The external calibration, which usually needs to be done only once, determines the array manifolds, i.e. the complex response of each antenna as a function of DOA(Directions of Arrival). The internal calibrations are necessary because characteristics of RF/IF circuity of each receiver chain vary differently in response to temperature or humidity changes. We propose an iterative subspace-based calibration algorithm for an asynchronous CDMA-based antenna away in the presence of unknown gain and phase error is presented. We verify the subspace-based calibration algorithms by performing the experiment using measured data. Also, we propose an efficient algorithm using the simulated annealing technique. This algorithm overcomes the problem of the initial guessing in the subspace-based approach.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.75.1-75
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• 2002

Several model order reduction methods for large RLC circuits have been developed in the last few years. Krylop subspace based methods are extremely effective for generating the low order models of large system but there is no optimal theory for the resulting models. Alternatively, methods based truncated balanced realization have an optimality property but are too computationally expensive to use on complicated problems such as large RLC circuits. In this paper, we present a method for improving time domain response of reduced order RLC circuits. The method used here is based on combing Krylop subspace based method and truncated balanced realization method plus residualization. The metho...

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4A
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• pp.269-277
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• 2012

When there are mismatches between the beamforming steering vector and the array response vector for the desired signal, the performance can be severely degraded as the adaptive array attempts to suppress the desired signal as well as interferences. In this paper, an robust method is proposed for the adaptive array in the presence of both direction errors and random errors in the steering vector. The proposed method first finds a signal-plus-interference subspace (SIS) from the correlation matrix, which in turn is exploited to extract an interference subspace based on the structure of a uniform linear array (ULA), the effect of the desired signal direction vector being reduced as much as possible. Then, the weight vector is attained to be orthogonal to the interference subspace. Simulation shows that the proposed method, in terms of signal-to-interference plus noise ratio (SINR), outperforms existing ones such as the doubly constrained robust Capon beamformer (DCRCB).