• The KIPS Transactions:PartC
• /
• v.10C no.2
• /
• pp.145-154
• /
• 2003

Current security efforts provided in such as firewall or IDS (intrusion detection system) of the network level suffer from many vulnerabilities in internal computing servers. Thus the necessity of secure OS is especially crucial in today's computing environment. This paper identifies secure OS requirements, analyzes tile research trends for secure Linux in terms of security kernel, and provides the descriptions of the multi-level security(MLS) Linux kernel which we have implemented. This security kernel-based Linux meets the minimum requirements for TCSEC Bl class as well providing anti-hacking, real-time audit trailing, restricting of root privileges, and enterprise suity management functions.

• Journal of KIISE:Computing Practices and Letters
• /
• v.9 no.3
• /
• pp.289-300
• /
• 2003

Traditional kernel-level communication systems for clusters are dependent upon computing platforms. Futhermore, they are not easy to use and do not provide various functions for clusters. This paper presents an architecture and various implementation issues of a kernel-level communication system, KCCM(Kernel level Cluster Communication Module), for linux cluster. The KCCM provides asynchronous communication services as well as standard synchronous communication services using send and receive. The KCCM also automatically detects and recovers connection failures at runtime. This allows programmers to use KCCM when they build mission critical applications over TCP-based connection-oriented communication environments. Having developed using standard socket interfaces, it can be easily ported to various platforms. The experimental results show that the KCCM provides good performance for asynchronous communication patterns.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.1
• /
• pp.109-118
• /
• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

• Journal of Communications and Networks
• /
• v.14 no.5
• /
• pp.540-545
• /
• 2012

In this paper, the lagged cross-correlation of two probability density functions constructed by kernel density estimation is proposed, and by maximizing the proposed function, adaptive filtering algorithms for supervised and unsupervised training are also introduced. From the results of simulation for blind equalization applications in multipath channels with impulsive and slowly varying direct current (DC) bias noise, it is observed that Gaussian kernel of the proposed algorithm cuts out the large errors due to impulsive noise, and the output affected by the DC bias noise can be effectively controlled by the lag ${\tau}$ intrinsically embedded in the proposed function.

• Bulletin of the Korean Mathematical Society
• /
• v.59 no.5
• /
• pp.1215-1235
• /
• 2022

In this paper we study Szegö kernel projections for Hardy spaces in quaternionic Clifford analysis. At first we introduce the matrix Szegö projection operator for the Hardy space of quaternionic Hermitean monogenic functions by the characterization of the matrix Hilbert transform in the quaternionic Clifford analysis. Then we establish the Kerzman-Stein formula which closely connects the matrix Szegö projection operator with the Hardy projection operator onto the Hardy space, and we get the matrix Szegö projection operator in terms of the Hardy projection operator and its adjoint. At last, we construct the explicit matrix Szegö kernel function for the Hardy space on the sphere as an example, and get the solution to a Diriclet boundary value problem for matrix functions.

• Honam Mathematical Journal
• /
• v.30 no.1
• /
• pp.185-195
• /
• 2008

We consider a system of trigonometric functional equations in the spaces of generalized functions such as Schwartz distributions and Gelfand generalized functions. As a consequence we find locally integrable solutions of the n-dimensional trigonometric functional equation.

• Communications of the Korean Mathematical Society
• /
• v.9 no.3
• /
• pp.607-616
• /
• 1994

I. M. Gelfand and G. E. Shilov [GS] introduced the Gelfand-Shilov spaces of type S, generalized type S and type W of test functions to investigate the uniqueness of the solutions of the Cauchy problems of partial differential equations. Using the heat kernel method Matsuzawa gave structure theorems for distributions, hyperfunctions and generalized functions in the dual space $(S^s_r)'$ of the Gelfand-Shilov space of type S in [M1, M2 and DM], respectively. Also, we gave structure theorems for ultradistributions, Fourier hyperfunctions in [CK, KCK], respectively.

• Korean Journal of Mathematics
• /
• v.17 no.4
• /
• pp.375-383
• /
• 2009

In this note, we determine the properties of the coefficients of Bell domains in the plane and find some coefficients to consist of Bell domain.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.8 no.4
• /
• pp.75-88
• /
• 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 Korean Journal of Applied Statistics
• /
• v.33 no.5
• /
• pp.569-578
• /
• 2020

By estimating conditional quantile functions of the response, quantile regression (QR) can provide comprehensive information of the relationship between the response and the predictors. In addition, kernel quantile regression (KQR) estimates a nonlinear conditional quantile function in reproducing kernel Hilbert spaces generated by a positive definite kernel function. However, it is infeasible to use the KQR in analysing a massive data due to the limitations of computer primary memory. We propose a divide and conquer based KQR (DC-KQR) method to overcome such a limitation. The proposed DC-KQR divides the entire data into a few subsets, then applies the KQR onto each subsets and derives a final estimator by aggregating all results from subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.