• Wind and Structures
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• v.25 no.6
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• pp.589-608
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• 2017

A novel probabilistic approach is presented for estimating the equivalent static wind loads that produce a static response of the structure, which is "equivalent" in a probabilistic sense, to the extreme dynamic responses due to the unsteady pressure random field induced by the wind. This approach has especially been developed for complex structures (such as stadium roofs) for which the unsteady pressure field is measured in a boundary layer wind tunnel with a turbulent incident flow. The proposed method deals with the non-Gaussian nature of the unsteady pressure random field and presents a model that yields a good representation of both the quasi-static part and the dynamical part of the structural responses. The proposed approach is experimentally validated with a relatively simple application and is then applied to a stadium roof structure for which experimental measurements of unsteady pressures have been performed in boundary layer wind tunnel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2035-2051
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• 2011

Theoretical Bit Error Rate (BER) and channel capacity analysis are always of great interest to the designers of wireless communication systems. At the center of such analyses people are often encountered with a high-dimensional multiple integrals with quite complex integrands. Conventional Gaussian quadrature is inefficient in handling problems like this, as it tends to entail tremendous computational overhead, and the principal order of its error term increase rapidly with the dimension of the integral. In this paper, we propose a new approach to calculate complex multi-fold integrals based on the number theory. In contrast to Gaussian quadrature, the proposed approach requires less computational effort, and the principal order of its error term is independent of the dimension. The effectiveness of the number theory based approach is examined in BER and capacity analyses for practical systems. In particular, the results generated by numerical computation turn out in good match with that of Monte-Carlo simulations.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.553-560
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• 2001

Recently, ultrasonic testing simulation has becomes very important in the field of nondestructive evaluation due to its unique capability of providing testing signals without real inspection. The ultrasonic testing simulation requires three elementary models including the transducer beam radiation model, the flaw scattering model, and the reception model. In the present work, we briefly describe an approach to develop the ultrasonic testing model together with its elementary models with the multi-gaussian beam model. Based on this approach, we developed ultrasonic testing simulation program with MATLAB. The performance of the developed program is demonstrated by the predicting of ultrasonic signals from two types of flaws, circulars crack and spheres.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.76-81
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• 2009

We propose an effective emotion recognition algorithm based on the minimum classification error (MCE) incorporating multi-modal system The emotion recognition is performed based on a Gaussian mixture model (GMM) based on MCE method employing on log-likelihood. In particular, the reposed technique is based on the fusion of feature vectors based on voice signal and galvanic skin response (GSR) from the body sensor. The experimental results indicate that performance of the proposal approach based on MCE incorporating the multi-modal system outperforms the conventional approach.

• Structural Engineering and Mechanics
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• v.51 no.5
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• pp.707-725
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• 2014

In order to improve the crane-hook's performance and service life, we formulate a multi-criteria shape design problem considering practical conditions. The structural weight, the displacement at specified points and the induced matrix norm of stiffness matrix are adopted as the evaluation items to be minimized. The heights and widths of cross-section are chosen as the design variables. The design variables are expressed in terms of shape functions based on the Gaussian function. For this multi-objective optimization problem with three items, we utilize a multi-objective evolutionary algorithm, that is, the multi-objective Particle Swarm Optimization (MOPSO). As a common feature of obtained solutions, the side views are tapered shapes similar to those of actual crane-hook designs. The evaluation item values of the obtained designs demonstrate importance of the present optimization as well as the feasibility of the proposed optimal design approach.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.364-371
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• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.184-189
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• 2014

This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.54-59
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• 2014

Identification of RTF (Relative Transfer Function) between sensors is essential to multichannel speech enhancement system. In this paper, we present an approach for estimating the relative transfer function of speech signal. This method adapts a CASA (Computational Auditory Scene Analysis) technique to the conventional OM-LSA (Optimally-Modified Log-Spectral Amplitude) based approach. Evaluation of the proposed approach is performed under simulated stationary and nonstationary WGN (White Gaussian Noise). Experimental results confirm advantages of the proposed approach.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.2
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• pp.112-120
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• 2016

The nonlinearity parameter is frequently measured as a sensitive indicator in damaged material characterization or tissue harmonic imaging. Several previous studies have employed the plane wave solution, and ignored the effects of beam diffraction when measuring the non-linearity parameter ${\beta}$. This paper presents a multi-Gaussian beam approach to explicitly derive diffraction corrections for fundamental and second harmonics under quasilinear and paraxial approximation. Their effects on the nonlinearity parameter estimation demonstrate complicated dependence of ${\beta}$ on the transmitter-receiver geometries, frequency, and propagation distance. The diffraction effects on the non-linearity parameter estimation are important even in the nearfield region. Experiments are performed to show that improved ${\beta}$ values can be obtained by considering the diffraction effects.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.781-790
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• 2004

This paper presents a fourth-order cumulants based iterative algorithm for blind channel equalization. It is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum phase characteristic of the channel. In this approach, the transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel outputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple reordering and scaling. Both a closed-form and a stochastic version of the proposed algorithm are tested with three-ray multi-path channels in simulation studies, and their performances are compared with a method based on conventional second-order cumulants. Relatively good results are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.