• Steel and Composite Structures
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• v.10 no.6
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• pp.541-561
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• 2010

This paper presents finite element analyses, experimental measurements and finite element model updating of an arch type steel laboratory bridge model using semi-rigid connections. The laboratory bridge model is a single span and fixed base structure with a length of 6.1 m and width of 1.1m. The height of the bridge column is 0.85 m and the maximum arch height is 0.95 m. Firstly, a finite element model of the bridge is created in SAP2000 program and analytical dynamic characteristics such as natural frequencies and mode shapes are determined. Then, experimental measurements using ambient vibration tests are performed and dynamic characteristics (natural frequencies, mode shapes and damping ratios) are obtained. Ambient vibration tests are performed under natural excitations such as wind and small impact effects. The Enhanced Frequency Domain Decomposition method in the frequency domain and the Stochastic Subspace Identification method in the time domain are used to extract the dynamic characteristics. Then the finite element model of the bridge is updated using linear elastic rotational springs in the supports and structural element connections to minimize the differences between analytically and experimentally estimated dynamic characteristics. At the end of the study, maximum differences in the natural frequencies are reduced on average from 47% to 2.6%. It is seen that there is a good agreement between analytical and experimental results after finite element model updating. Also, connection percentages of the all structural elements to joints are determined depending on the rotational spring stiffness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• Structural Engineering and Mechanics
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• v.14 no.3
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• pp.245-262
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• 2002

The free vibration analysis of stiffened laminated composite plates has been performed using the layered (zigzag) finite element method based on the first order shear deformation theory. The layers of the laminated plate is modeled using nine-node isoparametric degenerated flat shell element. The stiffeners are modeled as three-node isoparametric beam elements based on Timoshenko beam theory. Bilinear in-plane displacement constraints are used to maintain the inter-layer continuity. A special lumping technique is used in deriving the lumped mass matrices. The natural frequencies are extracted using the subspace iteration method. Numerical results are presented for unstiffened laminated plates, stiffened isotropic plates, stiffened symmetric angle-ply laminates, stiffened skew-symmetric angle-ply laminates and stiffened skew-symmetric cross-ply laminates. The effects of fiber orientations (ply angles), number of layers, stiffener depths and degrees of orthotropy are examined.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.103-110
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• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.4
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• pp.267-280
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• 2009

In this paper we investigate a simple two-level additive Schwarz preconditioner for the P1 symmetric interior penalty Galerkin method of the Poisson equation on rectangular meshes. The construction is based on the decomposition of the global space of piecewise linear polynomials into the sum of local subspaces, each of which corresponds to an element of the underlying mesh, and the global coarse subspace consisting of piecewise constants. This preconditioner is a direct combination of the block Jacobi iteration and the cell-centered finite difference method, and thus very easy to implement. Explicit upper and lower bounds for the maximum and minimum eigenvalues of the preconditioned matrix system are derived and confirmed by some numerical experiments.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.393-410
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• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.69-72
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• 2001

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequence, nor does it require a priori channel information. Recently, Tong et al. proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the second order statistics techniques, fur example, subspace method, prediction error method, and so on. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind equalizer length mismatch as well as for its simple adaptive filter implementation. Unfortunately, the previous one-step prediction error method is known to be limited in arbitrary delay. In this paper, we induce the optimal delay, and propose the adaptive blind equalizer with multi-step linear prediction using RLS-type algorithm. Simulation results are presented to demonstrate the proposed algorithm and to compare it with existing algorithms.

• Journal of applied mathematics & informatics
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• v.36 no.5_6
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• pp.459-474
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• 2018

Cao et al. in (Numer. Linear. Algebra Appl. 18 (2011) 875-895) proposed a splitting method for saddle point problems which unconditionally converges to the solution of the system. It was shown that a Krylov subspace method like GMRES in conjunction with the induced preconditioner is very effective for the saddle point problems. In this paper we first modify the iterative method, discuss its convergence properties and apply the induced preconditioner to the problem. Numerical experiments of the corresponding preconditioner are compared to the primitive one to show the superiority of our method.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.643-651
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• 2020

Generally, it is necessary to perform transient structural analysis in order to verify and improve the seismic performance of high-rise buildings and bridges against earthquake loads. In this paper, we propose the model order reduction (MOR) method using the Krylov vectors to perform seismic analysis for linear and elastic systems in an efficient way. We then compared the proposed method with the mode superposition method (MSM) by using the limited numbers of modal vectors (or eigenvectors) calculated from the modal analysis. In the calculation, the data of the El Centro earthquake in 1940 were adopted for the seismic loading in the transient analysis. The numerical accuracy and efficiency of the two methods were compared in detail in the case of a simplified high-rise building.