• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1087-1104
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• 2015

An eye location based head posture recognition method is proposed in this paper. First, face is detected using skin color method, and eyebrow and eye areas are located based on gray gradient in face. Next, pupil circles are determined using edge detection circle method. Finally, head postures are recognized based on eye location information. The proposed method has high recognition precision and is robust for facial expressions and different head postures, and can be used in mouse operation. The experimental results reveal the validity of proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.350-356
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• 2017

With increasing attention to real-time body detection, active research is being conducted on human body detection based on skin color. Despite this, most existing skin detection methods utilize static skin color models and have detection rates in images, in which colors are distorted. This study proposed a method of detecting the skin region using a fuzzy classification of the gradient map, saturation, and Cb and Cr in the YCbCr space. The proposed method, first, creates a gradient map, followed by a saturation map, CbCR map, fuzzy classification, and skin region binarization in that order. The focus of this method is to rigorously detect human skin regardless of the lighting, race, age, and individual differences, using features other than color. On the other hand,the borders between these features and non-skin regions are unclear. To solve this problem, the membership functions were defined by analyzing the relationship between the gradient, saturation, and color features and generate 108 fuzzy rules. The detection accuracy of the proposed method was 86.35%, which is 2~5% better than the conventional method.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.471-477
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• 2018

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

• Computers and Concrete
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• v.1 no.2
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• pp.131-150
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• 2004

We present in this work a coupled phenomenological chemo-mechanical model that represents the degradation of concrete-like materials. The chemical behaviour is described by the nowadays well known simplified calcium leaching approach. And the mechanical damage behaviour is described by a continuum damage model which involves the gradient of the damage quantity. The coupled nonlinear problem at hand is addressed within the context of the finite element method. For the equation governing the calcium dissolution-diffusion part of the problem, special care is taken to treat the highly nonlinear calcium conductivity and solid calcium functions. The algorithmic design is based on a Newton-type iterative scheme where use is made of a recently proposed relaxed linearization procedure. And for the equation governing the damage part of the problem, an augmented Lagrangian formulation is used to take into account the damage irreversibility constraint. Finally, numerical simulations are compared with experimental results on cement paste.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.59-80
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• 2003

An incomplete factorization method for preconditioning symmetric positive definite matrices is introduced to solve normal equations. The normal equations are form to solve linear least squares problems. The procedure is based on a block incomplete Cholesky factorization and a multilevel recursive strategy with an approximate Schur complement matrix formed implicitly. A diagonal perturbation strategy is implemented to enhance factorization robustness. The factors obtained are used as a preconditioner for the conjugate gradient method. Numerical experiments are used to show the robustness and efficiency of this preconditioning technique, and to compare it with two other preconditioners.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1078-1082
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• 2017

This paper presents an L1 optimization based filtering technique which effectively eliminates the optical misalignment effects encountered in the squint guidance mode with strapdown seekers. We formulated a series of L1 optimization problems in order to separate the bias and the gradient components from the measured data, and solved them via the alternating direction method of multipliers (ADMM) and sparse matrix decomposition techniques. The proposed technique was able to rapidly detect arbitrary discontinuities and gradient changes from the measured signals, and was shown to effectively cancel the undesirable effects coming from the seeker misalignment angles. The technique was implemented on embedded flight computers and the real-time operational performance was verified via the hardware-in-the-loop simulation (HILS) tests in parallel with the automatic target recognition algorithms and the intra-red synthetic target images.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.259-266
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• 2022

Based upon differential quadrature method (DQM) and nonlocal strain gradient theory (NSGT), an investigation on the free vibrations of 2D plate systems with nano-dimensions has been provided taking into account the effects of different mechanical loadings. In order to capture different mechanical loadings, a general form of variable compressive load applied in the axial direction of the plate system has been introduced. The studied plate has been constructed from two types of particles which results in graded material properties and nanoscale pores. The established formulation for the plate is in the context of a novel shear deformable model and the equations have been solved via a semi-numerical trend. Presented results indicate the prominence of material composition, nonlocal coefficient, strain gradient coefficient and boundary conditions on vibrational frequencies of nano-size plate.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.111-118
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• 2004

The work presented in this paper concerns the aerodynamic characteristics and compression wave generated in a tunnel when a high speed train enters it. A large number of solutions have been proposed to reduce the amplitude of the pressure gradient in tunnels and some of the most efficient solutions consist of (a) addition ofa blind hood, (b) addition of inclined part at the entrance, and (c) holes in the ceiling of the tunnel. These are numerically studied by using the three-dimensional unsteady compressible Euler equation solver with ALE, CFD code, based on FEM method. Computational results showed that the smaller inclined angle leads to the lower pressure gradient of compression wave front. This study indicated that the most efficient slant angle is in the range from $30^{\circ}$ to $50^{\circ}$. The maximum pressure gradient is reduced by $26.81\%$ for the inclined angle of $30^{\circ}$ as compared to vertical entry. Results also showed that maximum pressure gradient can be reduced by $15.94\%$ in blind hood entry as compared to $30^{\circ}$ inclined tunnel entry. Furthermore, the present analysis showed that inclined slant angle has little effect on aerodynamic drag. Comparison of the pressure gradient between the inclined tunnel hood and the vertical entry with air vent holes indicated that the optimum inclined tunnel hood is much more effective way in reducing pressure gradient and increasing the pressure rise time.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.695-699
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• 2009

In this paper, we propose an automatic prostate segmentation technique using image intensity and gradient information. Our method is composed of four steps. First, rays at regular intervals are generated. To minimize the effect of noise, the start and end positions of the ray are calculated. Second, the profiles on each ray are sorted based on the gradient. And priorities are applied to the sorted gradient in the profile. Third, boundary points are extracted by using gradient priority and intensity distribution. Finally, to reduce the error, the extracted boundary points are corrected by using B-spline interpolation. For accuracy evaluation, the average distance differences and overlapping region ratio between results of manual and automatic segmentations are calculated. As the experimental results, the average distance difference error and standard deviation were 1.09mm $\pm0.20mm$. And the overlapping region ratio was 92%.