• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• Food Science and Biotechnology
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• v.16 no.6
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• pp.918-923
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• 2007

To investigate the relationship of wheat single kernel characteristics with end-use properties, 183 soft wheat cultivars and lines were evaluated for milling quality characteristics (kernel hardness, kernel and flour protein, flour ash), and end-use properties (i.e., as ingredients in sugar-snap cookies, sponge cake). Significant positive correlations occurred among wheat hardness parameters including near-infrared reflectance (NIR) score and single kernel characterization system (SKCS). The SKCS characteristics were also significantly correlated with conventional wheat quality parameters such as kernel size, wheat protein content, and straight-grade flour yield. The cookie diameter and cake volume were negatively correlated with NIR and SKCS hardness, and there was an inverse relationship between flour protein contents and kernel weights or sizes. Sugar-snap cookie diameter was positively correlated with sponge cake volume.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1521-1521
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• 2001

The objective of the research being presented was to construct and calibrate a spectrometer for the analysis of single kernels of corn. In light of the difficulties associated with capturing the spatial variability in composition of corn kernels by single-beam spectrometry, a hyperspectral imaging spectrometer was constructed with the intention that it would be used to analyze single kernels of corn for the prediction of moisture and oil content. The spectrometer operated in the range of 750- 1090 nanometers. After evaluating four methods of standardizing the output from the spectrometer, calibrations were made to predict whole-kernel moisture and oil content from the hyperspectral image data. A genetic algorithm was employed to reduce the number of wavelengths imaged and to optimize the calibrations. The final standard errors of prediction during cross-validation (SEPCV) were 1.22% and 1.25% for moisture and oil content, respectively. It was determined, by analysis of variance, that the accuracy and precision of single-kernel corn analysis by hyperspectral imaging is superior to the single kernel reference chemistry method (as tested).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1616-1616
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• 2001

To select kernels for breeding that have required constituent content from either naturally distributed samples or artificially mutated ones, it is necessary to process batch samples in a short time. The constituent content of single-kernel grains such as wheat and rice has been determined using conventional bench type NIR instruments; however, it takes a lot of time and effort. Shizuoka Seiki (Fukuroi-city, Japan) and NFRI (National Food Research Institute) of MAFF (Ministry of Agriculture, forestry and Fisheries of Japan) have jointly developed a continuous high-speed single-kernel brown rice sorting machine based on rice protein content. It consists of several sections such as a feeding mechanism, measuring unit, sorting mechanism and controlling PC. The feeding mechanism picks up single-kernel brown rice from the hopper (maximum of 5kg storage capacity) and sends it to the measuring unit. A spectrum of the brown rice is obtained in the measuring unit, which consists of a near-infrared array sensor. The brown rice is then sorted in the sorting mechanism based on its protein content estimated by the controlling PC. In the present study, measuring speed was approximately 500ms for the full spectrum range and overall sorting speed was approximately 2.8s for one kernel. Accuracy of estimation was approximately SEP=0.5% of dry matter protein content for nonglutinous rice.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.44 no.1
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• pp.78-85
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• 1999

To investigate kernel hardness, a compression test which is widely used to measure the hardness of individual kernels as a physical testing method was made simultaneously with the measurement of friabilin (15KDa) which is strongly associated with kernel hardness and was recently developed as a biochemical marker for evaluating kernel hardness in 79 Korean wheat varieties and experimental lines. With the scattered diagram based on the principal component analysis from the parameters of the compression test, 79 Korean wheat varieties were classified into three groups based on the principal component analysis. Since conventional methods required large amount of flour samples for analysis of friabilin due to the relatively small amount of friabilin in wheat kernels, those methods had limitations for quality prediction in wheat breeding programs. An extraction of friabilin from the starch of a single kernel through cesium chloride gradient centrifugation was successful in this experiment. Among 79 Korean wheat varieties and experimental lines 50 lines (63.3%) exhibited a friabilin band and 29 lines (36.7%) did not show a friabilin band. In this study, lines that contained high maximum force and the lower ratio of minimum force to maximum force showed the absence of the friabilin band. Identification of friabilin, which is the product of a major gene, could be applied in the screening procedures of kernel hardness. The single kernel analysis system for friabilin was found to be an easy, simple and effective screening method for early generation materials in a wheat breeding program for quality improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1463-1480
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• 2014

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn't make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.187-194
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• 2018

The operating system for IoT should have a small memory footprint and provide low power state, real-time, multitasking, various network protocols, and security. Although the Zephyr kernel, an operating system for IoT, released by the Linux Foundation in February 2016, has these features but errors generated by the user code can generate fatal problems in the system because the Zephyr kernel adopts a single-space method that both the user code and kernel code execute in the same space. In this research, we propose a space separation method, which separates kernel space and user space, to solve this problem. The space separation that we propose consists of three modifications in Zephyr kernel. The first is the code separation that kernel code and user code execute in each space while using different stacks. The second is the kernel space protection that generates an exception by using the MPU (Memory Protection Unit) when the user code accesses the kernel space. The third is the SVC based system call that executes the system call using the SVC instruction that generates the exception. In this research, we implemented the space separation in Zephyr v1.8.0 and evaluated safety through abnormal execution of the user code. As the result, the kernel was not crashed by the errors generated by the user code and was normally executed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.232-248
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• 2014

A series of kernel regression (KR) algorithms, such as the classic kernel regression (CKR), the 2- and 3-D steering kernel regression (SKR), have been proposed for image and video super-resolution. In existing KR frameworks, a single algorithm is usually adopted and applied for a whole image/video, regardless of region characteristics. However, their performances and computational efficiencies can differ in regions of different characteristics. To take full advantage of the KR algorithms and avoid their disadvantage, this paper proposes a kernel regression framework for video super-resolution. In this framework, each video frame is first analyzed and divided into three types of regions: flat, non-flat-stationary, and non-flat-moving regions. Then different KR algorithm is selected according to the region type. The CKR and 2-D SKR algorithms are applied to flat and non-flat-stationary regions, respectively. For non-flat-moving regions, this paper proposes a similarity-assisted steering kernel regression (SASKR) algorithm, which can give better performance and higher computational efficiency than the 3-D SKR algorithm. Experimental results demonstrate that the computational efficiency of the proposed framework is greatly improved without apparent degradation in performance.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.641-648
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• 1997

Mutual information is useful for analyzing nonlinear dependence in time series in much the same way as correlation is used to characterize linear dependence. We use multivariate kernel density estimators for the estimation of mutual information at different time lags for single and multiple time series. This approach is tested on a variety of hydrologic data sets, and suggested an appropriate delay time $ au$ at which the mutual information is almost zerothen multi-dimensional phase portraits could be constructed from measurements of a single scalar time series.