• Journal of Ship and Ocean Technology
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• 제10권2호
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• pp.24-36
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• 2006

Ship hullform is usually designed with a curve network, and smooth hullform surfaces are supposed to be generated by filling in (or interpolating) the curve network with appropriate surface patches. Tensor-product surfaces such as B-spline and $B\'{e}zier$ patches are typical representations to this interpolating problem. However, they have difficulties in representing the surfaces of irregular topological type which are frequently appeared in the fore- and after-body of ship hullform curve network. In this paper, we proposed a method that can automatically generate discrete $G^1$ continuous B-spline surfaces interpolating given curve network of ship hullform. This method consists of three steps. In the first step, given curve network is reorganized to be of two types: boundary curves and reference curves of surface patches. Especially, the boundary curves are specified for their surface patches to be rectangular or triangular topological type that can be represented with tensor-product (or degenerate) B-spline surface patches. In the second step, surface fitting points and cross boundary derivatives are estimated by constructing virtual iso-parametric curves at discrete parameters. In the last step, discrete $G^1$ continuous B-spline surfaces are generated by surface fitting algorithm. Finally, several examples of resulting smooth hullform surfaces generated from the curve network data of actual ship hullform are included to demonstrate the quality of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2528-2530
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• 2004

Neural network minimization problems are often conditioned and in this contribution way to handle this will be discussed. It is shown that a better conditioned minimization problem can be obtained if the problem is separated with respect to the linear parameters. This will increase the convergence speed of the minimization. One of the most powerful uses of neural networks is in function approximation(curve fitting)[1]. A main characteristic of this solution is that function (f) to be approximated is given not explicitly but implicitly through a set of input-output pairs, named as training set, that can be easily obtained from calibration data of the measurement system. In this context, the usage of Neural Network(NN) techniques for modeling the systems behavior can provide lower interpolation errors when compared with classical methods like polynomial interpolation. This paper solve of single-variable minimization using neural network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권4호
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• pp.264-269
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• 2008

In this paper, two neural networks as a material model, which are based on the state-space method, have been proposed. One outputs the rates of inelastic strain and material internal variables whereas the outputs of the other are the next state of the inelastic strain and material internal variables. Both the neural networks were trained using input-output data generated from Chaboche's model and successfully converged. The former neural network could reproduce the original stress-strain curve. The neural network also demonstrated its ability of interpolation by generating untrained curve. It was also found that the neural network can extrapolate in close proximity to the training data.

• 한국정보통신학회논문지
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• 제17권12호
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• pp.2775-2784
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• 2013

Brachistochrone 문제는 중력장내의 수직평면에 존재하는 임의의 두점을 연결하는 곡선경로를 따라 bead가 움직일 때에 가장 빠른 곡선경로를 구하는 것이며, calculus of variation에 의해 최단시간제어량을 구할 수 있지만 매우 복잡한 비선형방정식의 역관계를 테이블 형태로 구해야 하므로 그 정확도가 높지 않다. 본 논문에서는 이러한 근사해의 정확도를 높이기 위해 신경회로망을 이용하여 비선형방정식의 역관계식을 표현하였고, 신경회로망의 보간 기능으로 인해 높은 정확도의 최단시간제어가 가능하였다. 여러 가지 최종목표점에 대한 컴퓨터 시뮬레이션을 통해서 본 논문에서 제안한 방법이 기존의 방법보다 우수함을 확인할 수 있었다.