• 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.

• 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.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.372-378
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• 2008

Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.63-68
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• 2017

SAR(Synthetic Aperture Radar) uses electromagnetic signals to acquire ground information and has been used for wide coverage reconnaissance missions regardless of weather conditions. However SAR is known to be vulnerable to interference signals by other communication devices or radar instruments and may suffer from undesirable performance degradations and image quality. In this paper, a modified Eigen-subspace based filter is proposed that can be easily applied to SAR images affected by interference signals. The method of constructing Eigen-subspace based filter is briefly described and various simulations are performed to show the performance of the interference mitigation process. The suppression filter is applied to a ALOS PALSAR raw data affected by interfering signals in order to verify its superiority over the Notch filter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.7
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• pp.669-676
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• 2004

In this paper, a new algorithm is proposed for resolving multiple broadband incoherent sources incident on a uniform linear array. The proposed method dose not require any initial estimates for finding the transformation matrix, while the Coherent Signal-Subspace Method(CSM) proposed by Wang and Kaveh requires preliminary estimates of multigroup source location. An effective procedure is derived for finding the enhanced spectral density matrix at the center frequency using signal enhancement approach and then constructing a common signal subspace by selecting a unitary transformation matrix which is obtained via rotation of signal subspace method. The proposed approach is found to provide superior performance relative to that obtained with the CSM method in terms of sample bias of direction-of-arrival estimates.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.169-174
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• 2003

A novel subspace speech enhancement using subband whitening filter is proposed. Previous subspace speech enhancement method either assumes additive white noise or uses whitening filter as a pre-processing for colored noise. The proposed method tries to minimize the signal distortion while reducing residual noise by processing the signal using subband whitening filter. By incorporating the notion of subband whitening filter, spectral resolution in Karhunen-Loeve(KL) domain is improved with the negligible additional computational load. The proposed method outperforms both the subspace method suggested by Ephraim and the spectral subtraction suggested by Boll in terms of segmental signal-to-noise ratio (SNRseg) and perceptual evaluation of speech quality (PESQ).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1A
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• pp.84-92
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• 2004

Eigenstructure-based adaptive beamfoming has advantages of fast convergence and the insentivity to errors in the arrival angle of the desired signal. Eigen-decomposing the sample matrix to extract a basis for the Sl (signal plus interference) subspace, however, is very computationally expensive. In this paper, we present a simple subspace based beamforming which utilizes off-diagonal elements of the sample matrix to estimate the Sl subspace. The outputs of overlapped subarrays are combined to produce the final adaptive output, which improves SINR (signal-to-interference-plus-noise ratio) comapred to exploiting a single subarray. The proposed adaptive beamformer, which employs an efficient angle estimation is very roubust to errors in both the arrival angles and the number of the incident signals, while the eigenstructure-based beamforer suffers from severe performance degradation.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.88-98
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• 1998

Subspace classifier is a popular pattern recognition method based on Karhunen-Loeve transform. This classifier describes a high dimensional pattern by using a reduced dimensional subspace. Because of the loss of information induced by dimensionality reduction, however, a subspace classifier sometimes shows unsatisfactory recognition performance to the patterns having quite similar principal components each other. In this paper, we propose the use of multiple novelty neural network classifiers constructed on novelty vectors to adopt minor components usually ignored and present a method of improving recognition performance through combining those with the subspace classifier. We develop the proposed classifier on handwritten numeral database and analyze its properties. Our proposed classifier shows better recognition performance compared with other classifiers, though it requires more weight links.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.20-26
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• 1999

In this paper, a multiple target angle tracking algorithm that can avoid data association problem and has a simple structure is proposed by obtaining the angular innovation of the targets from a signal subspace. The signal subspace is recursively estimated by a signal subspace tracking algorithm, such as PAST. A nonlinear matrix equation which satisfy the estimated signal subspace and the angular innovation is induced and expanded into a Taylor series for linear approximation. The angular innovation is obtained by solving the approximated linear matrix equation in the least square sense. The good performance of the proposed algorithm is demonstrated by various computer simulations.