• Bulletin of the Korean Chemical Society
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• 제30권7호
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• pp.1667-1670
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• 2009

• 전기학회논문지
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• 제64권7호
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• pp.1104-1113
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• 2015

In this paper, we propose Two-Dimensional Robust Face Recognition System Realized with the Aid of Facial Symmetry with Illumination Variation. Preprocessing process is carried out to obtain mirror image which means new image rearranged by using difference between light and shade of right and left face based on a vertical axis of original face image. After image preprocessing, high dimensional image data is transformed to low-dimensional feature data through 2-directional and 2-dimensional Principal Component Analysis (2D)2PCA, which is one of dimensional reduction techniques. Polynomial-based Radial Basis Function Neural Network pattern classifier is used for face recognition. While FCM clustering is applied in the hidden layer, connection weights are defined as a linear polynomial function. In addition, the coefficients of linear function are learned through Weighted Least Square Estimation(WLSE). The Structural as well as parametric factors of the proposed classifier are optimized by using Particle Swarm Optimization(PSO). In the experiment, Yale B data is employed in order to confirm the advantage of the proposed methodology designed in the diverse illumination variation

• Smart Structures and Systems
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• 제18권3호
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• pp.425-448
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• 2016

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

• 대한치과보철학회지
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• 제34권3호
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• pp.574-592
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• 1996

This study investigated the time-dependent dimensional changes of elastomeric impression materials using holographic interferometry. Six commercial impression materials, Permlastic(polysulfide), Xantopren VL (condensation silicone), low and medium viscosity of Exafine, Provil (addition silicone), and Impregum(polyether), were selected. Steel plate was used as custom tray, and each impression specimen was 20 mm in width, 15 mm in length and 3 mm in thickness. Each impression material was evaluated at 30 minutes, 1, 2, 4, 6, 8 and 12 hours after setting using real-time holography. The results were as follow : 1. Xantopren VL and Permlastic showed relatively severe and continuous dimensional changes after setting. Low viscosity of Exafine, Provil, Impregum showed relatively slight dimensional changes with function of time and medium viscosity of Exafine showed almost no dimensional change from 2 hours after setting to 6 hours. 2. On initial dimensional changes within 1 hour, the amount of change in low viscosity of Exafine was the least and Xantopren VL was the largest. 3. On dimensional changes at 4 hours after setting, the amount of change in medium viscosity of Exafine was the least and tile change of Xantopren VL was the largest. 4. In overall dimensional stability during 12 hours, medium viscosity of Exafine was the most stable and Xantopren VL and Permlastic were least stable.

• Bulletin of the Korean Chemical Society
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• 제27권7호
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• pp.1053-1055
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• 2006

• 대한기계학회논문집
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• 제19권3호
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• pp.741-750
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• 1995

The turbulent buoyancy-driven flow in 2-dimensional enclosed cavities heated from the vertical side is numerically calculated for both cases of a Rayleigh number of 5*10$^{10}$ for air and 2.5*10$^{10}$ for water. Three different turbulence models are considered : standard k-.epsilon. model of Ozoe and low-Reynolds-number model of Lam and Bremhorst, and another low-Reynolds-number model of Davidson. The results indicate that the use of low-Reynolds number models is recommended for the indoor airflow computation, and the results from Davidson model are reasonably close to the reported experimental data. A sensitivity study shows that the amounts of wall-heat transfer and the velocity profiles with the Lam and Bremhorst model largely depend on the choice of the wall function for .epsilon..

• International Journal of Fluid Machinery and Systems
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• 제4권1호
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• pp.133-139
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• 2011

In this paper, Based on Two-dimensional Flow Theory, adopting quasi-orthogonal method and point-by-point integration method to design the impeller of the low specific speed centrifugal pump by code, and using RANS (Reynolds Averaged N-S) Equation with a standard k-${\varepsilon}$ two-equation turbulence model and log-law wall function to solve 3D turbulent flow field in the impeller of the low specific speed pump. An analysis of the influences of the blade profile on velocity distributions, pressure distributions and pump performance and the investigation of the flow regulation pattern in the impeller of the centrifugal pump are presented. And the result shows that this method can be used as a new way in low speed centrifugal pump impeller design.

• 대한기계학회논문집B
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• 제27권12호
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• pp.1681-1690
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• 2003

The effect of roughness is a change in the velocity and turbulence distributions near the surface. Turbulence models with surface roughness effect are applied to the fully developed flow in a two-dimensional, rough wall channel. Modified wall function model, low-Reynolds number k-$\varepsilon$ model, and k-$\omega$ model are selected for comparison. In order to make a fair comparison, the calculation results are compared with the experimental data. The modified wall function model and the low-Reynolds number k-$\varepsilon$ model require further refinement, while the k-$\omega$ model of Wilcox performs remarkably well over a wide range of roughness values.

• 전기학회논문지P
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• 제54권1호
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• pp.34-40
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• 2005

This paper presents a frequency transfer function synthesis for simplifying a high-order model with time delay to a low-order model. A model reduction is based on minimizing the error function weighted by the numerator polynomial of reduced systems. The proposed method provides better low frequency fit and a computer aided algorithm. And in this paper, we present a design method of PID controller for achieving the desired specifications via the reduced model. The proposed method identifies the parameter vector of PID controller from a linear system that develops from rearranging the two dimensional input matrices and output vectors obtained from the frequency bounds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3458-3478
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• 2022

Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems. Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems. However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical. Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems. Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution. A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features. In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable. Finally, the network is trained with a penalized statistical entropy loss function. Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity. The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.