• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.961-972
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• 2008

In this paper, we consider the global exponential stability of cellular neural networks with time-varying delays. Based on the Lyapunov function method and convex optimization approach, a novel delay-dependent criterion of the system is derived in terms of LMI (linear matrix inequality). In order to solve effectively the LMI convex optimization problem, the interior point algorithm is utilized in this work. Two numerical examples are given to show the effectiveness of our results.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.13-14
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• 2003

Two different ${\kappa}-{\varepsilon}-\overline{{\nu}{\nu}}-f$ turbulence models together with the two-layer model are evaluated for natural convection in a rectangular cavity. The numerical problem and accuracy of the turbulence models are discussed. The original $\overline{{\nu}{\nu}}-f$ model suffers from the numerical stiffness problem when used with the segregate solution procedure like the SIMPLE algorithm, and a remedy for this problem is proposed. It is shown that original $\overline{{\nu}{\nu}}-f$ model best predicts the mean velocity, Reynolds stresses and the turbulent heat flux while the modified $\overline{{\nu}{\nu}}-f$ model (N=6) overpredicts the turbulent quantities.

• Journal of computational fluids engineering
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• v.2 no.2
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• pp.9-17
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• 1997

The performance of several turbulence models in computing an axisymmetric supersonic base flow is investigated. A compressible Navier-Stokes code, which incorporates k-ε, k-ω model and Reynolds stress closure with three kinds of pressure-strain correlation model, has been developed using implicit LU-SGS algorithm with second-order upwind TVD scheme. Numerical computations have been carried out for Herrin and Dutton's base flow. It is observed that the two-equation models give large backward axial velocity approaching to the base and somewhat larger variation of base pressure distribution than the Reynolds stress model. It is also found that the Reynolds stress model with third order pressure-strain model in the anisotropy tensor predicts most accurate mean flow field.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.11a
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• pp.211-214
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• 2005

This research studied robust design of composite structure under combined loading of bending and torsion. DOE (Design of Experiment) technique was used to find important design factors. The results show that the beam height, beam width, layer thickness and stack angle of outer-layer are important design parameter. The $2^{nd}$ DOE and RSM (Response Surface Model) were conducted to obtain optimum design. Multi-island genetic algorithm was used to optimum design. An approximate value of 6.65 mm in deflection was expected under optimum condition. Six sigma robust design was conducted to find out guideline for control range of design parameter. To acquire six sigma level reliability, the sigma level reliability, the standard deviation of design parameter should be controlled within 2.5 % of average design value.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.5
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• pp.102-106
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• 2015

In this paper, we study the optimal design for improved rotation latch system performance. The factors affecting the Torque generated in the armature were chosen as design variables. Utilizing the vertical matrix, the orthogonal array table was created to predict the results through minimal analysis. To confirm the Torque generation amount, by utilizing the commercial electromagnetic analysis software MAXWELL, finite element analysis was performed. The approximation method and experimental design through the commercial PIDO tool PIAnO for optimal design and calculations were utilized to perform experiments using an optimization method with evolutionary algorithms. Using the approximation model, design factors were determined that can maximize the torque generated in the armature, and the simulation was performed.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.553-556
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• 2002

A numerical study on the use of the momentum interpolation mettled for flows with a large body force is presented. The inherent problems of the momentum interpolation method are discussed first. Numerical experiments are performed for a typical flow involving a large body force. The tact that the momentum interpolation method may result in physically unrealistic solutions is demonstrated. Numerical experiments changing the numerical grid have shown that a simple way of removing the physically unrealistic solution is a proper grid refinement where there is a large pressure gradient. An effective way of specifying the pressure and pressure correction at the boundary by a local mass conservation near the boundary is proposed, and it is shown that this method can effectively remove the inherent problem of the specification of pressure and pressure correction at the boundary when one uses the momentum interpolation method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.683-691
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• 1997

This paper presents a new robot trajectory generation method based on the curvatre theory of ruled surfacees. robot trajectory is represented as a ruled surface generated by the TCP (Tool center point ) and any one unit vector among the tool frame (usually denoted O, A,N). The curvature theory of ruled surfaces provides the robot control algorithm with the motion property oarameters. The proposed method eliminates the necessity of approximation technic of either joint or cartesian interpolation. This technic may give new methodology of precision robot control. Especially this is very efficient when the robot traces an analytical or form surface if the surface is geometrically modelled.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2258-2274
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• 2020

In this paper we first propose a new cryptosystem based on our data hiding scheme (2,9,8) introduced in 2019 with high security, where encrypting and hiding are done at once, the ciphertext does not depend on the input image size as existing hybrid techniques of cryptography and steganography. We then exploit our automata approach presented in 2019 to design two algorithms for exact and approximate pattern matching on secret data encrypted by our cryptosystem. Theoretical analyses remark that these algorithms both have O(n) time complexity in the worst case, where for the approximate algorithm, we assume that it uses ⌈(1-ε)m)⌉ processors, where ε, m and n are the error of our string similarity measure and lengths of the pattern and secret data, respectively. In searchable encryption, our cryptosystem is used by users and our pattern matching algorithms are performed by cloud providers.

• Steel and Composite Structures
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• v.7 no.3
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• pp.201-217
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• 2007

Two combinatorial optimization algorithms, tabu search and simulated annealing, are presented for the minimum-weight design of geometrically non-linear steel plane frames. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) specification, maximum and interstorey drift constraints and size constraints for columns were imposed on frames. The stress constraints of AISC Allowable Stress Design (ASD) were also mounted in the two algorithms. The comparisons between AISC-LRFD and AISC-ASD specifications were also made while tabu search and simulated annealing were used separately. The algorithms were applied to the optimum design of three frame structures. The designs obtained using tabu search were compared to those where simulated annealing was considered. The comparisons showed that the tabu search algorithm yielded better designs with AISC-LRFD code specification.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.