• Clinical and Experimental Reproductive Medicine
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• v.20 no.1
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• pp.57-63
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• 1993

From September 1988 to August 1992, two different methods of preparing human sperm before intrauterine insemination(IUI) were compared using the semen samples of seventy-three infertile couples. The sperms were prepared by a swim-up after sperm washing or by a continuous percoll gradient technique. Fourteen of 35 women conceived during IUI cycles using a sperm washing and swim-up method (40%), and 12 of 38 women conceived during IUI cycles using a percoll gradient technique(31.6%). Among the group with male infertile etiologic factor only, one of 5 women conceived during sperm washing and swim-up cycles(20%); one of 4 women conceived during percoll gradient cycles(25%). On the contrary, among the group with cervical factor only, six of 10 women conceived during sperm washing and swim-up cycles (60%) ; Five of 17 women conceived during percoll gradient cycles(29.4%). It is suggested that sperm separation by sperm washing ar -up is a useful technique for intrauterine insemination in cervical infertility, and sperm separation in percoll gradient appears to be more valuable for intrauterine insemination of male subfertility.

• Journal of the Korean Magnetics Society
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• v.2 no.2
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• pp.132-139
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• 1992

We have designed a magnetic field gradient useful for cylindrical imaging in NMR-CT. The direc¬tion of the designed field is parallel to the axis and the gradient in the radial direction of cylindrical coordinate is monotonically increasing. The ratio of the gradient in the radial and axial direction is greater than 10 near the center of coordinate. This ratio depends on solenoid length, the number of reverse current turns at center, and the amount of the reverse current. We built a gradient coil based on the numerical simulation and tested the field generated by NMR-CT. The resulting image matches with the theoretical expectation within 10% error. Since the data acquisition time of 1-D imaging is significantly shorter than 2-D imaging, it becomes possible to image much more dynamic objects by the use of this gradient field.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.51-58
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• 2012

Scirtothrips Dorsalis (Thysanoptera: Thripidae) recently has been recognized as a major source of the pest damage in the citrus fruit orchards. So its arrival has been predicted periodically but it is difficult to identify adults of the pest with the naked eyes because of their size smaller than the 0.8mm. In this paper, we propose a method to detect candidate areas for automatic identification of Scirtothrips Dorsalis on forecasting traps. The proposed method uses a histogram-based template matching where the composite image synthesized with the gray-scale image and the gradient image is used. In our experiments, images are acquired by the optical microscopy with 50 magnifications. To show the usefulness of the proposed method, it is compared with the method we previously suggested. Also, the performances when the proposed method is applied to noise-reduced images and gradient images are examined. The experimental results show that the proposed method is approximately 14.42% better than our previous method, 41.63% higher than the case that the noise-reduced image is used, and 21.17% higher than the case that the gradient image is used.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.381-388
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• 2010

Naive Bayesian learning has been widely used in many data mining applications, and it performs surprisingly well on many applications. However, due to the assumption that all attributes are equally important in naive Bayesian learning, the posterior probabilities estimated by naive Bayesian are sometimes poor. In this paper, we propose more fine-grained weighting methods, called value weighting, in the context of naive Bayesian learning. While the current weighting methods assign a weight to each attribute, we assign a weight to each attribute value. We investigate how the proposed value weighting effects the performance of naive Bayesian learning. We develop new methods, using gradient descent method, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general Naive bayesian, and the value weighting method showed better in most cases.

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.61-70
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• 2001

The origin and destination(O-D) matrix is one of the most important elements in transportation planning process. Traditionally, transport planners survey the O-D movements in order to estimate the O-D matrix. Even though the cost of the O-D survey requires high amounts of resources, the accuracy is relatively low. Therefore, many researchers have studied the estimation of the O-D matrix for automobile from traffic counts. however, there is a little attention for the application on the transit O-D matrix estimation from traffic counts. The objective of this study is therefore the estimation of the transit O-D matrix from traffic counts using Gradient method. which is verified by the reliability analysis using a contrived small example network.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.98-106
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• 1998

This paper proposes an efficient learning algorithm for improving the training performance of the neural network. The proposed method improves the training performance by applying the backpropagation algorithm of a global optimization method which is a hybrid of a stochastic approximation and a conjugate gradient method. The approximate initial point for f a ~gtl obal optimization is estimated first by applying the stochastic approximation, and then the conjugate gradient method, which is the fast gradient descent method, is applied for a high speed optimization. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to those of the conventional backpropagation and the backpropagation algorithm which is a hyhrid of the stochastic approximation and steepest descent method.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.70-78
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• 1995

The identification of a fuzzy model using input-output data consists of two parts :Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures is suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.131-138
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• 2011

In this paper, we compare our proposed method with previous methods for the volumetric image segmentation using level set. In order to obtain an exact segmentation, the region and boundary information of image object are used in our proposed speed function. The boundary information is defined by the gradient vector flow obtained from the gradient images and the region information is defined by Gaussian distribution information of pixel intensity in a region-of-interest for image segmentation. Also the regular term is used to remove the noise around surface. We show various experimental results of real medical volume images to verify the superiority of proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1041-1050
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• 2011

An elasto-plastic finite element method using the theory of strain gradient plasticity is proposed to evaluate the size dependency of structural plasticity that occurs when the configuration size decreases to micron scale. For this method, we suggest a low-order plane and three-dimensional displacement-based elements, eliminating the need for a high order, many degrees of freedom, a mixed element, or super elements, which have been considered necessary in previous researches. The proposed method can be performed in the framework of nonlinear incremental analysis in which plastic strains are calculated and averaged at nodes. These strains are then interpolated and differentiated for gradient calculation. We adopted a strain-gradient-hardening constitutive equation from the Taylor dislocation model, which requires the plastic strain gradient. The developed finite elements are tested numerically on the basis of typical size-effect problems such as micro-bending, micro-torsion, and micro-voids. With respect to the strain gradient plasticity, i.e., the size effects, the results obtained by using the proposed method, which are simple in their calculation, are in good agreement with the experimental results cited in previously published papers.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.6-15
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• 2012

Purpose : The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and $T_2{^*}$ quantification in the liver. Materials and Methods: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of $T_2{^*}$and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a $B_0$ field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and $T_2{^*}$ from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. Results: After correction, in the phantom experiments, the estimated $T_2{^*}$ and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 ${\mu}T/m$ with increased homogeneity in $T_2{^*}$ and fat fractions obtained. Conclusion: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.