• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.325-328
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• 2006

고분자 전해질 연료전지의 전극을 gradient catalyst coating 방법을 이용하여 제조하였다. 촉매 잉크제조 시 나피온 이오노머의 함침 구성비를 다르게 하여 조성 비율이 다른 gradient 구조를 갖도록 하여 전극을 제조하였다. Anode Cathode의 두 전극을 각각 나피온 함량비가 다른 두 개의 gradient 층구조의 촉매층으로 9:1, 8:2, 7:3, 6:4 비율의 조성비로 성능을 측정하였으며, 전극의 전기화학적 반응 면적을 알아보기 위해 순위전위법을 그리고 분극 저항(Polarization resistance) 변화를 알아보기 위해서는 0.7V에서 임피던스 측정법의 전기화학분석법으로 전극 제조법에 따른 성능변화를 확인하였다. 특히 Gradient catalyst coating 방법을 이용하여 제조한 MEA는 종래 방식의 MEA보다 high current $density(1000mA/cm^2)$이상에서 향상된 성능을 보였다.

• Clinical and Experimental Reproductive Medicine
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• v.28 no.1
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• pp.25-31
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• 2001

연구목적: $Isolate^{(R)}$ gradient와 swim-up 방법이 정자의 형상 및 정밀정자형태 (strict morphology)에 미치는 영창을 비교분석하고, 이러한 정자처리방법이 정자의 동결-융해과정에 미치는 영향을 비교하고자 하였다. 연구재료 및 방법: 20명의 정상 정자를 대상으로 하였으며 각각의 정자는 두 가지 정자처리방법으로 나누어 정자의 형상과 정밀정자형태를 컴퓨터를 이용한 정자자동분석기를 통하여 측정하였고, 동결보호제의는 TYB 용액을 사용하였으며, 동결 및 융해는 cryo Magic사의 기계를 사용하였다. 통계는 SPSS PC+(version7.0)를 이용하였으며 통계학적인 유의성은 p<0.05로 하였다. 결과: 정자의 농도는 $Isolate^{(R)}$ gradient 처리군이 swim-up 처리군보다 유의성 있게 높았으나 ($51.2{\pm}40.1,\;156.6{\pm}64.3$), 운동성 VCL, VSL, VAP, Linearity, 및 ALH는 swim-up 처리군에서 유의성 있게 높았다. 정밀 정자형태는 swim-up 처리군과 $Isolate^{(R)}$ gradient 처리군에서 차이가 없었다 ($53.7{\pm}6.8$ vs $50.3{\pm}9.1%$). 동결-융해과정 중 두 가지 정자 처리군에서 정자의 형상들은 swim-up 처리군에서 전반적으로 높은 양상을 보였으나, 정밀정자형태는 $Isolate^{(R)}$ gradient 처리군이 swim-up 처리군 보다 감소율이 컸지만 두 군간에 유의한 차이는 없었다 ($12.8{\pm}8.5$ vs $8.6{\pm}6.6$). 결론: 정상 정자에서 swim-up 방법이 $Isolate^{(R)}$ gradient 방법보다 정자 회수율은 우수하였으나, 동결-융해과정 중 정밀정자형태에는 차이가 없어 두 방법을 상호보완적으로 사용할 수 있을 것으로 사료된다.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.305-312
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• 2022

In this paper, we focused on training to create and optimize a basic data prediction model. And we proposed a gradient descent training method of machine learning that is widely used to optimize data prediction models. It visually shows the entire operation process of gradient descent used in the process of optimizing parameter values required for data prediction models by applying the differential method and teaches the effective use of mathematical differentiation in machine learning. In order to visually explain the entire operation process of gradient descent, we implement gradient descent SW in a spreadsheet. In this paper, first, a two-variable gradient descent training method is presented, and the accuracy of the two-variable data prediction model is verified by comparison with the error least squares method. Second, a three-variable gradient descent training method is presented and the accuracy of a three-variable data prediction model is verified. Afterwards, the direction of the optimization practice for gradient descent was presented, and the educational effect of the proposed gradient descent method was analyzed through the results of satisfaction with education for non-majors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.120-127
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• 2010

This paper presents a SR method using adaptive interpolation based on local gradient features to obtain a high quality SR image. In this method, the distance between the interpolated pixel and the neighboring valid pixel is considered by using local gradient properties. The interpolation coefficients take the local gradient of the LR images into account. The smaller the local gradient of a pixel is, the more influence it should have on the interpolated pixel. And the median filter is finally applied to reduce the blurring and noise of the interpolated HR image. Experiment results show the effectiveness of the proposed method in comparison with other methods, especially in the edge areas of the images.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.454-458
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• 2001

Based on a fuzzy system representation of gray scale images, we derive and edge detection algorithm whose convolution kernel is different from the known kernels such as those of Robert's Prewitt's or Sobel's gradient. Our fuzzy system representation is an exact representation of the bicubic spline function which represents the gray scale image approximately. Hence the fuzzy system is a continuous function and it provides a natural way to define the gradient and the Laplacian operator. We show that the gradient at grid points can be evaluated by taking the convolution of the image with a 3$\times$3 kernel. We also that our gradient coupled with the approximate value of the continuous function generates an edge detection method which creates edge images clearer than those by other methods. A few examples of applying our methods are included.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.3
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• pp.94-102
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• 1996

An interpretation for the additive and multiplicative decomposition theory of the deformation gradient tensor in finite deformation problems is presented. the conventional methods have not provided the additive deformation velocity gradient. Moreover the plastic deformation velocity gradients are not free from elastic deformations. In this paper, a modified multiplicative decomposition is introduced with the assumption of coaxial plastic deformation velocity gradient. This strategy well gives the additive deformation velocity gradient in which the plastic deformation velocity gradient is not affect4d by the elastic deformation.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2769-2779
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• 1997

This Paper Proposes an efficient method for improving the training performance of the neural networks using a hybrid of conjugate gradient backpropagation algorithm and dynamic tunneling backpropagation algorithm The conjugate gradient backpropagation algorithm, which is the fast gradient algorithm, is applied for high speed optimization. The dynamic tunneling backpropagation algorithm, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Conversing to the local minima by using the conjugate gradient backpropagation algorithm, the new initial point for escaping the local minima is estimated by dynamic tunneling backpropagation algorithm. The proposed method has been applied to the parity check and the pattern classification. The simulation results show that the performance of proposed method is superior to those of gradient descent backpropagtion algorithm and a hybrid of gradient descent and dynamic tunneling backpropagation algorithm, and the new algorithm converges more often to the global minima than gradient descent backpropagation algorithm.

• Journal of Korean Society of Transportation
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• v.18 no.2
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• pp.41-52
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• 2000

In the developing country, the transportation situation is changed very quickly and the transportation environment is not stable. So the transportation planning should be frequently made in considering the limited cost and time. And the traditional large-scale survey(household survey, roadside interview, etc.) has many Problem like the difficulty for doing it and getting mood results. Therefore the study about the method of evaluation on the traffic count based O/D matrix is Processing actively recently. Though the many study for the network in the realistic size are enacted, the study for comparing with the advantage and disadvantage of each method are few. Therefore this study mainly deals with the static method among the existing models of evaluation on the traffic count based O/D matrix(in terms of the transportation plan). Bi-level(GU) and gradient method are selected as main alternative model and analyzed their capability and validity. For testing the reliability of the models, Bi-level(GLS) and gradient method are adapted to toy network. Then we analyze the result of testing, and study the way for large network.

• Journal of Korea Multimedia Society
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• v.2 no.2
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• pp.129-136
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• 1999

A linear feature often provide sufficient information for image understanding and coding. An objective of the research reported in this paper is to develop and analyze the reliable methods of extracting lines in gray scale images. The Hough Transform is known as one of the optimal paradigms to detect or identify the linear features by transforming edges in images into peaks in parameter space. The scheme proposed here uses the fuzzy gradient direction model and weights the gradient magnitudes for deciding the voting values to be accumulated in parameter space. This leads to significant computational savings by restricting the transform to within some support region of the observed gradient direction which can be considered as a fuzzy variable and produces robust results.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.