• 대한수학회지
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• 제60권3호
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• pp.635-682
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• 2023

In this paper, we consider linear elliptic systems from composite materials where the coefficients depend on the shape and might have the discontinuity between the subregions. We derive a function which is related to the gradient of the weak solutions and which is not only locally piecewise Hölder continuous but locally Hölder continuous. The gradient of the weak solutions can be estimated by this derived function and we also prove the local piecewise gradient Hölder continuity which was obtained by the previous results.

• 전자공학회논문지S
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• 제34S권1호
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• pp.72-79
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• 1997

Most open magnetic resonance imaging systems have used the planar gradient coils whose inductances were minimized through the magnetic energy minimization procedure in the spatial frequency domain. Though the planar gradient coils have smaller inductance than conventional gradient coils, the planar gradient coils often suffer from their poor magnetic field linearity. Scaling the spatial frequencies of the current density function designed by the magnetic energy minimization, magnetic field linearity of the planar gradient coils can be greatly improved with small sacrifice of gradient coil inductance. We have found that the figure of merit of the planar gradient coils, defined by the gradient strength divided by the linearity error and the inductance, can be improved by proposed technique.

• 한국항공운항학회지
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• 제15권2호
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• pp.18-24
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• 2007

The high ionospheric spatial gradient during ionospheric storm is the most concern when applying GNSS(Global Navigation Satellite System) augmentation systems for aircraft precision approach. Since the ionospheric gradient level depends on geographical location as well as the storm, understanding the ionospheric gradient statistics over a specific regional area is necessary for operating the augmentation systems. This paper compares three ionosphere gradient computation methods, direct differentiation between two receivers' ionospheric delay signal for a common satellite, derivation from a grid ionosphere map, and derivation from a plate ionosphere map. The plate map method provides a good indication on the gradient variation behavior over a regional area with limited number of GNSS receivers. The residual analysis for the ionosphere storm detection is discussed as well.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.65-69
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• 2002

The main drawback of “classical”fuzzy systems is the inability to design and maintain their database. To overcome this disadvantage many types of extensions adding the adaptivity property to those systems were designed. This paper deals with one of them a new hybrid adaptation structure, called gradient-incremental adaptive fuzzy controller connecting gradient-descent methods with the so-called self-organizing fuzzy logic controller designed by Procyk and Mamdani. The aim is to incorporate the advantages of both Principles. This controller was implemented and tested on the system of LEGO robots. The results and comparison to a ‘classical’(non-adaptive) fuzzy controller designed by a human operator are also shown here.

• 제어로봇시스템학회논문지
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• 제12권9호
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• pp.898-904
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• 2006

Ionospheric storms, caused by the interaction between Solar and geomagnetic activities, may degrade the differential GNSS(Global Navigation Satellite Systems) performance significantly, and the importance of the ionospheric storm research is growing for the GBAS(Ground-Based Augmentation System) and SBAS(Satellite-Based Augmentation System) development. In order to support Korean GNSS augmentation system development, a software tool for analyzing the regional ionosphere is being developed and its preliminary results are discussed. After brief description of the ionosphere and ionospheric storm, the research topics on the GBAS applications are discussed. The need for ionospheric spatial gradient analysis is described and some results on the ionospheric spatial gradient during recent storm periods are discussed.

• 한국지능시스템학회논문지
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• 제11권6호
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• pp.454-458
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• 2001

이 논문에서는 디지털 영상의 퍼지 시스템 표현으로부터 유도된 Edge 검출 알고리듬에 대하여 기술한다. 이 알고리듬은 Gradient을 기반으로 한 것으로 Convolution Kernel이 기존의 Roberts, Prewitt 또는 Sobel등이 제안한 Gradient Kernel과 다른 새로운 것이다. 사용한 퍼지시스템은 디지털 영상을 근사적으로 표현한 Bicubic Spline 함수를 퍼지시스템 화한것으로서 2차 도함수가 연속이기 때문에 Gradient나 Laplacian 연산이 가능하다. Grid 점들에서 이 함수의 Gradient는 두 개의 축 방향으로 각각 한개의 3$\times$3행렬과 영상과의 Covolution에 의하여 산출됨을 보였으며 이를 이용하여 검출된 Edge들은 기존의 다른 방법을 사용하여 검출된 Edge 영상보다 훨씬 선명함을 확인하였다. 이 알고리듬 적용사례 2개에 대한 기술에 포함되어 있다.

• 제어로봇시스템학회논문지
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• 제17권9호
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• pp.875-881
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• 2011

This paper describes a new method of estimating the gradient of a function with perturbation and correlation. We impose a known periodic perturbation to the input variable and observe the output of the function in order to obtain much richer and more reliable information. By taking the correlation between the input perturbation and the resultant function outputs, we can determine the gradient of the function. The computation of the correlation does not require derivatives; therefore the gradient can be estimated reliably. Robust estimation of the gradient using perturbation/correlation, which is very effective when an analytical solution is not available, is described. To verify the effectiveness of perturbation/correlation based estimation, the results of gradient estimation are compared with the analytical solutions of an example function. The effects of amplitude of the perturbation and number of samplings in a period are investigated. A minimization of a function with the gradient estimation method is performed.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.444-449
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• 2006

Path planning is a key element in navigation of a mobile robot. Several algorithms such as a gradient method have been successfully implemented so for. Although the gradient method can provide the global optimal path, it computes the navigation function over the whole environment at all times, which result in high computational cost. This paper proposes a high-speed path planning scheme, called a gradient method with topological information, in which the search space for computation of a navigation function can be remarkably reduced by exploiting the characteristics of the topological information reflecting the topology of the navigation path. The computing time of the gradient method with topological information can therefore be significantly decreased without losing the global optimality. This reduced path update period allows the mobile robot to find a collision-free path even in the dynamic environment.

• ETRI Journal
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• 제28권1호
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• pp.59-66
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• 2006

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF-SVD-SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine-tuning of centers and widths still shows ill-behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center-gradient variance of the RBFN-SVD-SG algorithm. We found analytically that the steadystate weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center-gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady-state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.740-749
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• 1998

This paper proposes compression of image data using neural networks based on conjugate gradient method and dynamic tunneling system. The conjugate gradient method is applied for high speed optimization .The dynamic tunneling algorithms, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Converging to the local minima by using the conjugate gradient method, the new initial point for escaping the local minima is estimated by dynamic tunneling system. The proposed method has been applied the image data compression of 12 ${\times}$12 pixels. The simulation results shows the proposed networks has better learning performance , in comparison with that using the conventional BP as learning algorithm.