• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.759-764
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• 2005

In this paper we describe a face recognition using EBGM(Elastic Bunch Graph Matching) method. Usally, the PCA and LDA based face recognition method with the low-dimensional subspace representation use holistic image of faces, but this study uses local features such as a set of convolution coefficients for Gabor kernels of different orientations and frequencies at fiducial points including the eyes, nose and mouth. At pre-recognition step, all images are represented with same size face graphs and they are used to recognize a face comparing with each similarity for all images. The proposed algorithm has less computation time due to simplified face graph than conventional EBGM method and the fuzzy matching method for calculating the similarity of face graphs renders more face recognition results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.2
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• pp.116-125
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• 1997

This paper presents a novel method, entitled Orthogonal Weights Searching(OWS), for the Direction-Of-Arrival(DOA) estimation. Utilizing the modified Conjugate Gradient Method(MCGM), the weight vector which is orthogonal to the signal subspace is directly computed from the signal matrix. The proposed method does not require the computation of the eigenvalues and eigenvectors. In addition, the new technique excludes the procedure for the detection of the number of signals under the assumption that the number of weights in the array is greater than the number of input signals. Since the proposed technique can be performed independently of the detection procedure, it shows a good performance in adverse signal environments in which the detection of the number of array inputs cannot be obtained successfully. The performance of the proposed technique is compared with that of the convectional eigen-decomposition method in terms of angle resolution for a given signal-to-noise ratio(SNR) and a required amount of computations.

• Steel and Composite Structures
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• v.16 no.1
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• pp.21-44
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• 2014

The aim of this study is to determine the dynamic characteristics of long reinforced concrete highway bridges with post-tension tendons using analytical and experimental methods. It is known that the deck length and height of bridges are affected the dynamic characteristics considerably. For this purpose, Berta Bridge constructed in deep valley, in Artvin, Turkey, is selected as an application. The Bridge has two piers with height of 109.245 m and 85.193 m, and the total length of deck is 340.0 m. Analytical and experimental studies are carried out on Berta Bridge which was built in accordance with the balanced cantilever method. Finite Element Method (FEM) and Operational Modal Analysis (OMA) which considers ambient vibration data were used in analytical and experimental studies, respectively. Finite element model of the bridge is created by using SAP2000 program to obtain analytical dynamic characteristics such as the natural frequencies and mode shapes. The ambient vibration tests are performed using Operational Modal Analysis under wind and human loads. Enhanced Frequency Domain Decomposition (EFDD) and Stochastic Subspace Identification (SSI) methods are used to obtain experimental dynamic characteristics like natural frequencies, mode shapes and damping ratios. At the end of the study, analytical and experimental dynamic characteristic are compared with each other and the finite element model of the bridge was updated considering the material properties and boundary conditions. It is emphasized that Operational Modal Analysis method based on the ambient vibrations can be used safely to determine the dynamic characteristics, to update the finite element models, and to monitor the structural health of long reinforced concrete highway bridges constructed with the balanced cantilever method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.35-42
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• 2021

This paper presents an on-line finite element model updating method for in-service structures using measured data. Conventional updating methods, which are based on numerical optimization, are not efficient for on-line updating because they generally require repeated eigenvalue analyses until convergence criteria are met. The proposed method enables fully automated on-line finite element model updating, almost simultaneously with vibration measurement, without any user intervention or off-line procedures. The automated covariance-driven stochastic subspace identification (Cov-SSI) method is utilized to identify modal frequencies and vectors, and the identified modal data is fed to the neural network of the inverse eigenvalue function to produce the updated finite element model parameters. Numerical examples for a wind excited 20-story building structure shows that the proposed method can update the series of finite element model parameters automatically. It is also shown that sudden changes in the structural parameters can be detected and traced successfully.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Smart Structures and Systems
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• v.11 no.5
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• pp.533-553
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• 2013

In this paper, system identification of a cable-stayed bridge in Korea, the Hwamyung Bridge, is performed using vibration responses measured by a wireless sensor system. First, an acceleration based-wireless sensor system is employed for the structural health monitoring of the bridge, and wireless sensor nodes are deployed on a deck, a pylon and several selected cables. Second, modal parameters of the bridge are obtained both from measured vibration responses and finite element (FE) analysis. Frequency domain decomposition and stochastic subspace identification methods are used to obtain the modal parameters from the measured vibration responses. The FE model of the bridge is established using commercial FE software package. Third, structural properties of the bridge are updated using a modal sensitivity-based method. The updating work improves the accuracy of the FE model so that structural behaviors of the bridge can be represented better using the updated FE model. Finally, cable forces of the selected cables are also identified and compared with both design and lift-off test values.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.82-86
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• 2006

The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.147-154
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• 2004

In this paper, Inverted Coordinates Image Space (ICIS) is proposed as a solution for the problem of the unbounded accumulator space in the automatic detection of the finite/infinite vanishing points in image space. Since the ICIS is based on the direct transformation from the image space, it does not lose any geometrical information from the original image and it does not require camera calibration as opposed to the Gaussian sphere based methods. Moreover, the proposed method can accurately detect both the finite and infinite vanishing points under a small fixed memory amount as opposed to the conventional image space based methods. Experiments are conducted on various real images in architectural environments to show the advantages of the proposed approach over conventional methods.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.933-941
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• 2010

Biplot is a modern graphical methodology allowing for the projection of high-dimensional data to a low-dimensional subspace that is rich in information on variation in the data, correlation among variables as well as class separation. For the construction of biplots, we use a BiplotGUI package in a free statistical software R with increasing popularity. Moreover, using data from questionnaires given to Chonbuk National University freshmen in 2009, the relationship between career goals and career maturity are studied by applying the biplot method.

• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1127-1136
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• 2016

Speech enhancement algorithm plays an important role in numerous speech signal processing applications. Over the last few decades, many algorithms have been studied for speech enhancement. The algorithms are based on spectral subtraction, Wiener filter, and subspace method etc. They have good performance of speech enhancement, but the performance can be deteriorated in specific noises or low SNR environment. In this paper, a new speech enhancement algorithms are proposed based on adaptive noise canceller. And the proposed algorithm improved performance of adaptive noise cancelling using 2-D binary mask. From objective experimental index, it is confirmed that the proposed algorithm is useful and has better performance than recently proposed speech enhancement algorithms.