• The Mathematical Education
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• v.31 no.2
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• pp.133-140
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• 1992

The problem of estimating a smooth distribution function F at a point $\tau$ based on randomly right censored data is treated under certain smoothness conditions on F . The asymptotic performance of a certain class of kernel estimators is compared to that of the Kap lan-Meier estimator of F($\tau$). It is shown that the .elative deficiency of the Kaplan-Meier estimate. of F($\tau$) with respect to the appropriately chosen kernel type estimate. tends to infinity as the sample size n increases to infinity. Strong uniform consistency and the weak convergence of the normalized process are also proved.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.607-617
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• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.693-703
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• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.164-173
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• 2001

In this paper, we have designed and implemented a new hard embedded real-time system to satisfy hard real-time constraints in moving independently. Real-time kernel should be small size, fast and predictable. Because of the great variety of demands on real time scheduling, a real time kernel should also include a flexible and re-programmable task scheduling discipline. In this paper, we present that real-time applications should be split into small and simple parts with hard real-time constraints. To satisfy these properties, we designed real-time kernel and general kernel, that have their different properties. In real-time tasks, interrupt processing should be run. In general kernel, non real time tasks or general tasks are run. The efficiency of the proposed hard embedded real-time system is shown by comparison results for performance of the proposal real time kernel with both RT-Linux and QNX.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.1
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• pp.70-73
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• 1988

The changes of kernel size, contents of moisture and total sugar, and rates of flavor and stickiness of a waxy corn (cv. Hongcheon native) were observed from 15 days after silking (DAS) to 40 DAS at Suwon and Cheongju in 1984, nespectively. Fresh kernel length and width greatly increased from 15 DAS to 30 DAS and slightly increased thereafter. Fresh 100-kernel weight was markedly increased during 15 DAS to 30 DAS, and slightly and continuously increased thereafter while dry 100-kernel weight almost linearly increased with maturity. Total sugar content of fresh kernel was the highest at 20 DAS and then continuously decreased with maturity. Flavor and stickiness rates of boiled waxy corn were significantly increased during 15 DAS to 30 DAS and flavor rate decreased thereafter. However, stickiness rate maintained the level until 35 DAS and then decreased slightly. The optimum harvest date for fresh waxy corn seems to be about 30 DAS considering kernel size, and flavor and stickiness rates of boiled corn.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.31 no.1
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• pp.49-55
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• 1986

To obtain basic information required for improving grain yield of the two modified opaque-2 synthetics, which have been developed at College of Agr., Chungnam National Univ. in 1980 and named as Puyo No.2 and No.3, physical kernel characteristics of the two synthetics were fully investigated and results obtained are as follows: Puyo No.2 synthetics had a smaller kernel size with lighter weight than the Puyo No.3. The Puyo No.2 synthetics had higher kernel density than the Puyo No.3 with large Kernel size. The Puyo No.2 had kernels with heterogenous endosperm phenotypes. Some kernels had mottled patches on endosperm, while other kernels 1/2 and 1/2 phenotypes. All the modified opaque-2 synthetics had somewhat lighter endosperm weight than the normal check hybrid. The Puyo No.2 synthetics with smaller kernel size had more germ portion compared with large kernel, Puyo No.3. The Puyo No.2 had shown also typical endosperm texture when observed under microscope after cutting by glass knife. The lysine content of the Puyo No.2 was higher than those of other varieties studied. Breeding schemes to improve the yield capacity of the two modified opaue-2 synthetics were discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Journal of the Korean Society of Food Science and Nutrition
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• v.20 no.6
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• pp.653-662
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• 1991

aThe terms of hard and soft as applied to wheats are descriptions of the texture of the kernel. A hard wheat kernel required greater force to cause it to disintegrate than those a soft wheat kernel. Factors than can affect the measurement of hardness outnumber those that affect hardness itself. Kernel texture is the most important single characteristic that affects the functionality of a common wheat. It affect the way in which must be tempered for milling ; the yield and the particle size, and density of flour particles ; and the end use properties in milling, breadmaking, production of soft wheat products, and noodle-making. Papers are reviewed from various sources not only hardness but flour functionality.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.195-203
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• 2022

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.705-719
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• 2010

We investigated the speckle filtering effect in supervised classification of the C-band polarimetric Ground Based SAR image data. Wishart classification method was used for the supervised classification of the polarimetric GB-SAR image data and total of 6 kinds of speckle filters were applied before supervised classification, which are boxcar, Gaussian, Lopez, IDAN, the refined Lee, and the refined Lee sigma filters. For each filters, we changed the filtering kernel size from $3{\times}3$ to $9{\times}9$ to investigate the filtering size effect also. The refined Lee filter with the kernel size of bigger than $5{\times}5$ showed the best result for the Wishart supervised classification of polarimetric GB-SAR image data. The result also showed that the type of trees could be discriminated by Wishart supervised classification of polarimetric GB-SAR image data.