• The KIPS Transactions:PartA
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• v.13A no.3 s.100
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• pp.199-204
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• 2006

The kernel hardening function is necessary in terms of kernel stability to reduce the system error or panic due to the kernel code error that is made by program developer. But, the traditional kernel hardening method is difficult to implement and consuming high cost. The suggested kernel hardening function that makes high availability system by changing the panic() function of inside kernel code guarantees normal system operation by recovering the incorrect address of the kernel stack frame. We experimented the kernel hardening function at the network module of the Linux by forcing panic code and confirmed the proposed design mechanism of kernel hardening is working well by this experiment.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.983-986
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• 2002

Support Vector Machine (SVM) is one of the methods of pattern recognition that separate input data using hyperplane. This method has high capability of pattern recognition by using the technique, which says kernel trick, and the Radial basis function (RBF) kernel is usually used as a kernel function in kernel trick. In this paper we propose using the q-normal distribution to the kernel function, instead of conventional RBF, and compare two types of the kernel function.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.405-407
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• 2009

SVM(Support Vector Machine) is a classification method which is recently watched in mechanical learning system. Vapnik, Osuna, Platt etc. had suggested methodology in order to solve needed QP(Quadratic Programming) to realize SVM so that have extended application field. SVM find hyperplane which classify into 2 class by converting from input space converter vector to characteristic space vector using Kernel Function. This is very systematic and theoretical more than neural network which is experiential study method. Although SVM has superior generalization characteristic, it depends on Kernel Function. There are three category in the Kernel Function as Polynomial Kernel, RBF(Radial Basis Function) Kernel, Sigmoid Kernel. This paper has analyzed performance of SVM against kernel using virtual data.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.7-10
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• 2003

In this paper, we present a new echo kernel, which is a modification of polar echo kernel. to improve the detection performance and robustness against attacks. Polar echo kernel may take advantage of large detection margin from the polarity of inserted echo signal, but its poor frequency response in low frequency band degrades sound quality. To solve this problem, we applied bipolar echo pulses to the polar echo kernel. Using the proposed echo kernel the distributions of autocepstrum peaks fur data ‘0’ and ‘1’ are located more distant and improvement of detection performance is achieved. It also makes the low frequency band flat so that the timbre difference in the polar echo kernel can be removed to reproduce the imperceptible sound qualify. Informal listening tests as well as robustness test against attacks were performed to evaluate the proposed echo kernel. Experimental results demonstrated the superiority of the proposed echo kernel to both conventional unipolar and polar echo kernels

• 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.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.12
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• pp.618-629
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• 2007

In this paper, we propose an effective memory test environment, called a virtualized kernel, for 64bit multi-core computing environments. The term of effectiveness means that we can test all of the physical memory space, even the memory space occupied by the kernel itself, without rebooting. To obtain this capability, our virtualized kernel provides four mechanisms. The first is direct accessing to physical memory both in kernel and user mode, which allows applying various test patterns to any place of physical memory. The second is making kernel virtualized so that we can run two or more kernel image at the different location of physical memory. The third is isolating memory space used by different instances of virtualized kernel. The final is kernel hibernation, which enables the context switch between kernels. We have implemented the proposed virtualized kernel by modifying the latest Linux kernel 2.6.18 running on Intel Xeon system that has two 64bit dual-core CPUs with hyper-threading technology and 2GB main memory. Experimental results have shown that the two instances of virtualized kernel run at the different location of physical memory and the kernel hibernation works well as we have designed. As the results, the every place of physical memory can be tested without rebooting.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.71-77
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• 2006

Support Vector Machine (SVM) which has roots in a statistical learning theory is a training algorithm based on structural risk minimization. Generally, SVM algorithm uses the kernel for determining a linearly non-separable boundary and classifying the data. But, classical kernels can not apply to effectively the hyperspectral image classification because it measures similarity using vector's dot-product or euclidian distance. So, This paper proposes the spectral similarity kernel to solve this problem. The spectral similariy kernel that calculate both vector's euclidian and angle distance is a local kernel, it can effectively consider a reflectance property of hyperspectral image. For validating our algorithm, SVM which used polynomial kernel, RBF kernel and proposed kernel was applied to land cover classification in Hyperion image. It appears that SVM classifier using spectral similarity kernel has the most outstanding result in qualitative and spatial estimation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-6
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• 2009

One of the most important problems in bioinformatics is how to extract the useful information from a huge amount of data, and make a decision in diagnosis, prognosis, and medical treatment applications. This paper proposes a weighted kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a learning method based on genetic algorithm. The weights of basis kernel functions in proposed kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.