• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.4
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• pp.470-474
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• 2015

This study was carried out to compare botanical and amylogram characteristics including table quality in waxy corn kernel. The used materials in this experiment were producted and evulated at Corn Breeding Laboratory, Coll. of Agri. & Life Sci., in CNU. In botanical characteristics CNU H09-26 among used hybrids was highest in stem height as 228.5 cm, but that of CNU H09-30 was lowest. Ear height was highest in CNU H09-23 as 78.2 cm, but that of CNU H09-30 was lowest. Ear length among hybrids were also variable as 21.2 cm to 10.8 cm. in amylogram analysis CNU H09-23 hybrid was lowest in pasting temperature, while break down of this hybrid was highest These results appeared highly in table quality. Accordingly we thought that this hybrid will be adapted as a leading variety for edible waxy corn.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.10-18
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• 2011

Medical asset protection is important in each medical institution especially because of the law on private medical record protection and face recognition for this protection is one of the most interesting and challenging problems. In recognizing human faces, the distortion of face images can be caused by the change of pose, illumination, expressions and scale. It is difficult to recognize faces due to the locations of lights and the directions of lights. In order to overcome those problems, this paper presents an analysis of coefficients of Gabor wavelets, kernel decision, feature point, size of kernel, for face recognition in CCTV surveillance. The proposed method consists of analyses. The first analysis is to select of the kernel from images, the second is an coefficient analysis for kernel sizes and the last is the measure of changes in garbo kernel sizes according to the change of image sizes. Face recognitions are processed using the coefficients of experiment results and success rate is 97.3%. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the satisfaction and the quality of services will be improved in the face recognition area.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.27-34
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• 2008

The purpose of this paper is an adaptive image interpolation using parametric cubic convolution. Proposed method derive parameter of adapting the frequency from adjacent values. The parameter optimize the interpolation kernel of cubic convolution. Simulation results show that the proposed method is superior to the conventional method in terms of the subjective and objective image quality.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.233-236
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• 1996

A new method of blocking effects reduction is proposed in this paper for use in low bitrate image coding. We use 28 DCT kernel functions of which boundary values are linearly independent, and Gram-Schmidt process is applied to the boundary values in order to obtain 28 boundary-orthonormal basis images. Then we use these basis images to obtain the correction terms for blocking artifacts reduction. A threshold of block discontinuity is introduced for improvement of visual quality by reducing image blurring. We also investigate the number of basis images needed for efficient blocking artifacts reduction when the compression ratio changes.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1157-1165
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• 2017

2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2-D complex Gabor filter bank consisting of the 2-D complex Gabor filtering outputs at multiple orientations and frequencies. Although several approaches for fast 2-D complex Gabor filtering have been proposed, they primarily focus on reducing the runtime of performing the 2-D complex Gabor filtering once at specific orientation and frequency. To obtain the 2-D complex Gabor filter bank output, existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2-D complex Gabor filter bank by reducing the computational redundancy that arises when performing the Gabor filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor basis kernels to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the 2-D complex Gabor filter bank by reusing intermediate results of the 2-D complex Gabor filtering computed at a specific orientation. Experimental results demonstrate that our method runs faster than state-of-the-arts methods for fast 2-D complex Gabor filtering, while maintaining similar filtering quality.

• Journal of Biosystems Engineering
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• v.15 no.3
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• pp.207-218
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• 1990

Grains display characteristics of both elastic bodies and viscous fluids when they are subjected to mechanical treatments in harvesting, handling, and processing. This viscoelastic behavior of grains when mechanically stressed must be fully understood to establish maximum machine efficiency and have a minimum degree of grain damage and the highest quality of the final product. The studies were conducted to examine the effect of the moisture content, the loading rate and the initial deformation on the stress relaxation behavior of whole kernel of rough rice, and develop the rheological model to represent its stress relaxation behavior. The following results were obtained from the study. 1. Moisture content had the greatest influence on the initial portion of the relaxation curve. With elapsing time the lower moisture content resulted in the lower residual stress for the Japonica-type rough rice and vice versa for the Indica-type rough rice. But within the ranges of moisture content tested, the degree of stress relaxation per unit strain on the Indica-type rough rice was a little higher than those on the Japonica-type rough rice. 2. The slower loading rate resulted in less initial stress. The decreasing trend of residual stress for all the samples tested with increasing loading rate was shown. 3. The higher initial deformation for all the samples resulted in less initial stress. The increasing of amount of stress relaxation per unit strain with increase of initial stress indicated that viscoelastic properties of rough rice depended not only upon duration of load applied but also initial stress applied. This means that rough rice is nonlinear viscoelastic material. 4. The compression stress relaxation properties of rough rice kernel can be described by a generalized Maxwell model representing by the Maxwell elements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6B
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• pp.533-543
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• 2003

Recently, according to the rapid increase of Web users, various kinds of Web applications have been being developed. Hence, Web QoS(Quality of Service) becomes a critical issue in the Web services, such as e-commerce, Web hosting, etc. Nevertheless, most Web servers currently process various requests from Web users on a FIFO basis, which can not provide differentiated QoS. This paper presents two approaches to provide differentiated Web QoS. The first is the kernel-level approach, which is adding a real-time scheduling processor to the operating system kernel to maintain the priority of user requests determined by the scheduling processor of Web server. The second is the load-balancing approach, which uses If-level masquerading and tunneling technology to improve reliability and response speed upon user requests.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.