• Journal of the Chungcheong Mathematical Society
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• v.33 no.4
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• pp.497-504
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• 2020

A weight ω on the positive half real line [0, ∞) is a positive continuous function such that ω(s + t) ≤ ω(s)ω(t), for all s, t ∈ [0, ∞), and ω(0) = 1. The weighted convolution Banach algebra L1(ω) is the algebra of all equivalence classes of Lebesgue measurable functions f such that ‖f‖ = ∫0∞|f(t)|ω(t)dt < ∞, under pointwise addition, scalar multiplication of functions, and the convolution product (f ⁎ g)(t) = ∫0t f(t - s)g(s)ds. We give a sufficient condition on a weight function ω(t) in order that L1(ω) has a bounded approximate identity.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.123-128
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• 2000

A cracked-plate with a patch bonded on one side is treated with a crack-bridging model: assuming continuous distribution of springs acting between crack surfaces. the approximate weight function was introduced to obtain the stress intensity factor of patched crack subjected to residual stress or non-uniform stress. The stress intensity factors for the partially patched crack within finite plate or the patched crack initiated from a notch were successfully obtained by numerical calculation.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1659-1664
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• 2003

A cracked plate with a patch bonded on one side was treated with a crack-bridging model using weight function: assuming continuous distribution of springs acting between th crack surfaces, the stress intensity factor of the patched crack was numerically obtained. Especially in the case of a patched crack subjected to residual non-uniform stress, the stress intensity factor was easily with the corresponding approximate weight function. This paper presented the stress intensity factors for a crack partially patched within a finite plate or a patched crack initiated from a notch.

• Structural Engineering and Mechanics
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• v.38 no.5
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• pp.651-674
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• 2011

This paper presents a novel method for predicting the failure probability of structural or mechanical systems subjected to random loads and material properties involving multiple design points. The method involves Multicut High Dimensional Model Representation (Multicut-HDMR) technique in conjunction with moving least squares to approximate the original implicit limit state/performance function with an explicit function. Depending on the order chosen sometimes truncated Cut-HDMR expansion is unable to approximate the original implicit limit state/performance function when multiple design points exist on the limit state/performance function or when the problem domain is large. Multicut-HDMR addresses this problem by using multiple reference points to improve accuracy of the approximate limit state/performance function. Numerical examples show the accuracy and efficiency of the proposed approach in estimating the failure probability.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.142-147
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• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.41-49
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• 1997

In this paper, we describe the algorithm which controls an unknown nonlinear system with disturbance a using multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate an unknown nonlinear system by using of multilayer neural netowrk. WE include a disturbance among the modelling error, and the weight-update rule of multilayer neural network is derived to satisfy Laypunov stability. The whole control system constitutes controller using the feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.7
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• pp.2167-2173
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• 1996

An approximate weight function technique using the indirect boundary integral equation has been presented for the analysis of stress intensity foactors(SIFs) of a thick pipe. One-term boundary integral was introduced to represent the crack surface displacement field for the displacement based weight function technique. An explicit closed-form SIF solution applicable to symmetric cracked pipes without any modification of the solution including both circumferential and radial cracks has been derived. The necessary information in the analysis is two or three reference SIFs. In most cases the SIF solution were in good agreement with those available in the literature.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.10
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• pp.847-854
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• 1991

This paper presents a new output phase assignment algorithm which determines the phases of all the nodes in a given boolean network. An estimation function is defined, which is represented by the relation between the literals in the given function expression. A weight function, WT (fi, fj) is defined, which is represented by approximate amount of common subexpression between function fi and fj. Common Subexpression Graph(CSG) is generated for phase selection by the weight function between all given functions. We propose a heuristic algorithm finding subgraph of which sum of weights has maximum by assigning phases into the given functions. The experiments with MCNC benchmarks show the efficiency of the proposed method.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.187-196
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• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.