• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.759-768
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• 2005

This paper describes an algorithm that can recognize gestures constructing subspace gesture symbols with hybrid feature information. The previous popular methods based on geometric feature and appearance have resulted in ambiguous output in case of recognizing between similar gesture because they use just the Position information of the hands, feet or bodily shape features. However, our proposed method can classify not only recognition of motion but also similar gestures by the partial feature information presenting which parts of body move and the global feature information including 2-dimensional bodily motion. And this method which is a simple and robust recognition algorithm can be applied in various application such surveillance system and intelligent interface systems.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1578-1581
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• 2002

In this paper, the accuracy of the direction-of-arrival (DOA) estimation of signal impinged on the uniform linear array (ULA) is investigated. The conventional beamformer and Capon’s beamformer categorized in beamformaing techniques as well as MUSIC (MUlti-pie Signal Classification) and ESPRIT (Estimation of Signal Invariance Techniques) categorized in subspace- based methods are employed to estimate the DOAs. From the simulation result under uncorrelated environment, MUSIC can prominently distinguish the DOAs while the beamforming techniques cannot demonstrate the DOAs as clear as MUSIC does. Moreover, Uni-tary ESPRIT is employed to estimate the DOAs under uncorrelated signal conditions. By means of Uni-tary ESPRIT, the estimation has more accuracy with the computational-time reduction. In addition, it incorporates forward-backward averaging; thus Unitary ES-PRIT can overcome the problem of the coherent signal condition.

• Structural Engineering and Mechanics
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• v.57 no.5
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• pp.921-936
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• 2016

In this study, a model order reduction technique is applied to solve the transient responses of submerged floating tunnel (SFT) from Mokpo to Jeju under seismic excitations. Because the SFT is a very long structure as well as a transient response analysis requires large amount of computational resources, the model order reduction is mandatory in the design stage of the SFT. Thus, we apply a model order reduction based on Krylov subspace to the simplified finite element model of the SFT. The responses of the reduced order model are compared with those of the full order model and also are verified by referring a previous work. In conclusion, the computational resources are dramatically reduced with an acceptable accuracy by using the model order reduction, which eventually is useful for designing the full-scale model of SFTs.

• Spatial Information Research
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• v.23 no.3
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• pp.113-122
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• 2015

Recently, according to developing advanced construction technologies, buildings has been enlarged such as high-rise buildings or complex buildings associated with underground facilities. The number of indoor activity population has increased very quickly. Because of that, technical requirements for Indoor location based service (Indoor LBS) also have been increased. Although indoor networks have to be constructed for efficient LBSs in indoor space based on OGC IndoorGML, it is not suitable for large and complex space to apply the simple network structure to constructing indoor navigation networks. The indoor navigation network has to be constructed according to logical, physical, and functional constraints for indoor space. In order to do that, subspacing methods are required to partition large and complex indoor space into proper size of subspace. In this paper, we proposed the basic requirements of subspacing in indoor space for creating efficient indoor network, as well the work process of subspacing in indoor space.

• 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 Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.148-155
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• 2014

In this paper a commute time embedding is implemented by organizing patches according to the graph-based metric, and its properties are investigated via changing the number of nodes on the graph.. It is shown that manifold embedding methods generate the intrinsic geometric structures when waveforms such as speech or music instrumental sound signals are embedded on the low dimensional Euclidean space. Basically manifold embedding algorithms only project the training samples on the graph into an embedding subspace but can not generalize the learning results to test samples. They are very effective for data clustering but are not appropriate for classification or recognition. In this paper a commute time guided transform is adopted to enhance the generalization ability and its performance is analyzed by applying it to the classification of 6 kinds of music instrumental sounds.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.5
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• pp.57-63
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• 2008

This paper presents a seismic response prediction method using vibration tests of cabinet-type electrical equipment installed in a nuclear power plant. The proposed method consists of three steps: 1) identification of earthquake-equivalent forces based on lumped-mass system idealization, 2) identification of a state-space-equation model relating input-output measurements obtained from the vibration tests, 3) seismic prediction using the identified earthquake-equivalent forces and the identified state-space-equation. The proposed method is advantageous compared to other methods based on FEM (finite element method) model update, since the proposed method is not influenced by FEM modeling errors. Through a series of numerical verifications on a frame model and 3-dimensional shell model, it was found that the proposed method could be used to accurately predict the seismic responses, even under considerable measurement noise conditions. Experimental validation is needed for further study.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.461-480
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• 2010

This paper analyses the data collected from the $2^{nd}$ Jindo Bridge, a cable-stayed bridge in Korea that is a structural health monitoring (SHM) international test bed for advanced wireless smart sensors network (WSSN) technology. The SHM system consists of a total of 70 wireless smart sensor nodes deployed underneath of the deck, on the pylons, and on the cables to capture the vibration of the bridge excited by traffic and environmental loadings. Analysis of the data is performed in both the time and frequency domains. Modal properties of the bridge are identified using the frequency domain decomposition and the stochastic subspace identification methods based on the output-only measurements, and the results are compared with those obtained from a detailed finite element model. Tension forces for the 10 instrumented stay cables are also estimated from the ambient acceleration data and compared both with those from the initial design and with those obtained during two previous regular inspections. The results of the data analyses demonstrate that the WSSN-based SHM system performs effectively for this cable-stayed bridge, giving direct access to the physical status of the bridge.

• 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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.