• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.1
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• pp.85-92
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• 2015

This article presents the dynamic instability response of sigmoid functionally graded material plates on elastic foundation using the higher-order shear deformation theory. The higher-order shear deformation theory has ability to capture the quadratic variation of shear strain and consequently shear stress through the plate thickness. The governing equations are then written in the form of Mathieu-Hill equations and then Bolotin's method is employed to determine the instability regions. The boundaries of the instability regions are represented in the dynamic load and excitation frequency plane. The results of dynamic instability analysis of sigmoid functionally graded material plate are presented using the Navier's procedure to illustrate the effect of elastic foundation parameter on dynamic response. The relations between Winkler and Pasternak elastic foundation parameter are discussed by numerical results. Also, the effects of static load factor, power-law index and side-to-thickness ratio on dynamic instability analysis are investigated and discussed. In order to validate the present solutions, the reference solutions are used and discussed. The theoretical development as well as numerical solutions presented herein should serve as reference for the dynamic instability study of S-FGM plates.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1109-1117
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• 2014

We study free vibration analysis of sigmoid functionally graded materials(S-FGM) nano-scale plates, using a nonlocal elasticity theory of Eringen in this paper. This theory has ability to capture the both small scale effects and sigmoid function in terms of the volume fraction of the constituents for material properties through the plate thickness. Numerical solutions of S-FGM nano-scale plate are presented using this theory to illustrate the effect of nonlocal theory on natural frequency of the S-FGM nano-scale plates. The relations between nonlocal and local theories are discussed by numerical results. Further, effects of (i) power law index (ii) nonlocal parameters, (iii) elastic modulus ratio and (iv) thickness and aspect ratios on nondimensional frequencies are investigated. In order to validate the present solutions, the reference solutions are compared and discussed. The results of S-FGM nano-scale plates using the nonlocal theory may be the benchmark test for the free vibration analysis.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.18-21
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• 2002

We present a phase covariance model that can well represent stimulus intensity as well af feature binding (i.e., covariance). The model is represented by complex neural equations, which is a mean field model of stochastic neural model such as Boltzman machine and sigmoid belief networks.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12C
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• pp.1177-1183
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• 2009

In this paper, support vector machines (SVM) are applied to multi-user detector (MUD) for direct sequence (DS)-CDMA system. This work shows an analytical performance of SVM based multi-user detector with some of kernel functions, such as linear, sigmoid, and Gaussian. The basic idea in SVM based training is to select the proper number of support vectors by maximizing the margin between two different classes. In simulation studies, the performance of SVM based MUD with different kernel functions is compared in terms of the number of selected support vectors, their corresponding decision boundary, and finally the bit error rate. It was found that controlling parameter, in SVM training have an effect, in some degree, to SVM based MUD with both sigmoid and Gaussian kernel. It is shown that SVM based MUD with Gaussian kernels outperforms those with other kernels.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.74-81
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• 1997

This paper proposes an evolutionary learning algorithm to discipline the projection neural nctworks (PNNs) with special type of hidden nodes which can activate radial basis functions as well as sigmoid functions. The proposed algorithm not only trains the parameters and the connection weights hut also c~ptimizes the network structure. Through the structure optimization, the number of hidden node:; necessary to represent a given target function is determined and the role of each hidden node is decided whether it activates a radial basis function or a sigmoid function. To apply the algorithm, PNN is realized by a self-organizing genotype representation with a linked list data structure. Simulations show that the algorithm can build the PNN with less hidden nodes than thc existing learning algorithm using error hack propagation(EE3P) and network growing strategy.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.185-193
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• 2009

This paper presents a methodology for photovoltaic (PV) system allocation in distribution systems using a discrete particle swarm optimization (DPSO). The PV allocation problem is in the category of mixed integer nonlinear programming and its formulation may include multi-valued dis-crete variables. Thus, the PSO requires a scheme to deal with multi-valued discrete variables. This paper introduces a novel multi-level quantization scheme using a sigmoid function for discrete particle swarm optimization. The technique is employed to a standard PSO architecture; the same velocity update equation as in continuous versions of PSO is used but the particle's positions are updated in an alternative manner. The set of multi-level quantization is defined as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming a particle's position into discrete values. A comparison with a genetic algorithm (GA) is performed to verify the quality of the solutions obtained.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1474-1482
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• 2004

In this paper, we propose an approach for extracting and filtering block motion vectors using an adaptive weight function. We first extract motion vectors from a sequence of images by using size-varibale block matching and then process them by adaptive robust estimation to filter out outliers (motion vectors out of concern). The proposed adaptive robust estimation defines a continuous sigmoid weight function. It then adaptively tunes the sigmoid function to its hard-limit as the residual errors between the model and input data are decreased, so that we can effectively separate non-outliers (motion vectors of concern) from outliers with the finally tuned hard-limit of the weight function. The experimental results show that the suggested approach is very effective in filtering block motion vectors.

• The Journal of the Acoustical Society of Korea
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• v.34 no.3
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• pp.177-183
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• 2015

This paper proposes a robust nonlinear acoustic echo canceller (NAEC) which is effective for modeling the nonlinearity of a speaker module and the long acoustic echo path within a speech communication environment. The proposed NAEC utilizes a sigmoid pre-processor for modeling the speaker nonlinearity and a partitioned block frequnecy-domain adaptive filter for identifying the acoustic echo path with small delay. Simulation results confirmed that the proposed algorithm achieves excellent performance with much lower computational complexity than the previous NAEC.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.39 no.2
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• pp.85-89
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• 2013

Lemierre syndrome is caused by an infection in the oropharyngeal region with subsequent thrombophlebitis in the internal jugular vein. The thrombus from the thrombophlebitis can invade other vital organs, such as liver, lungs, or joints, resulting in secondary infection, which further exacerbates the fatal prognosis of this syndrome. Lemierre syndrome, also called postanginal sepsis or necrobacillosis, was first reported by Dr. Lemierre in 1936. In his report, Lemierre mentioned that out of 20 patients who suffered from this syndrome, only two survived. He also stated that all of the 20 patients complained of infections in the palatine tonsils and developed sepsis and thrombophlebitis in the internal jugular vein. Once called a "forgotten disease," this syndrome showed a very high mortality rate until usage of antibiotics became prevalent. In this case report, the authors present a 71-year-old female patient who suffered from Lemierre syndrome with thrombosis extended to the right sigmoid sinus.