• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.2
• /
• pp.237-242
• /
• 2003

In this paper, we propose a new adaptive intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based IDS but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.05a
• /
• pp.109-113
• /
• 2002

In this paper, we propose a new intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based 105 but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• Journal of the Chungcheong Mathematical Society
• /
• v.23 no.1
• /
• pp.59-63
• /
• 2010

In this paper, we introduce the concept of weakly-ultra-${\alpha}{\psi}$-separation of two sets in a topological space using ${\alpha}{\psi}$-open sets. The ${\alpha}{\psi}$-closure and the ${\alpha}{\psi}$-kernel are defined in terms of this weakly ultra-${\alpha}{\psi}$-separation. We also investigate some of the properties of the ${\alpha}{\psi}$-kernel and the ${\alpha}{\psi}$-closure.

• Honam Mathematical Journal
• /
• v.43 no.4
• /
• pp.587-596
• /
• 2021

In this article, using the concept proposed reciently by the author, of a Generalized k-Proportional Fractional Integral Operators with General Kernel, new integral inequalities are obtained for convex functions. It is shown that several known results are particular cases of the proposed inequalities and in the end new directions of work are provided.

• The Journal of Information Technology
• /
• v.4 no.2
• /
• pp.25-36
• /
• 2001

In this paper, we present the development of distributed dual real-time kernel system. This paper proposed that real-time applications should be split into small and simple parts with real-time constraints, Following this concept we have designed to preserve the properties of both hard real-time kernel and general kernel. 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 un. In general kernel, non real-time tasks or general tasks are run. We compared the results of this study for performance of the proposal real-time kernel with both RT Linux 0.5a and QNX 4.23A, that is, of interrupt latency scheduling precision and message passing.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.7C
• /
• pp.428-434
• /
• 2011

Recently, subspace analysis has raised its performance to a higher level through the adoption of kernel-based nonlinearity. Especially, the radial basis function, based on its nonparametric nature, has shown promising results in face recognition. However, due to the endemic small sample size problem of face data, the conventional kernel-based feature extraction methods have difficulty in data representation. In this paper, we introduce a novel variant of the RBF kernel to alleviate this problem. By adopting the concept of the nearest feature line classifier, we show both effectiveness and generalizability of the proposed method, particularly regarding the small sample size issue.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.489-492
• /
• 2000

Because of the great variety of demands on real-time scheduling, real-time kernel should be small, fast and predictable. In this paper, we present that Real-time applications should be split into small and simple parts with hard real-time constraints. Following this concept, we have designed and implemented to have the properties of both hard real-time kernel and general kernel. And, to prove be useful the proposal kernel, we compare and analyze the performance with RT-Linux 0.5a

• Communications of Mathematical Education
• /
• v.16
• /
• pp.139-147
• /
• 2003

While the statistical concept 'order statistics' has a great number of applications in our society ranging from industry to military analysis, it is not necessarily an easy concept to understand for many people. Adding some interesting simulation activities of this concept to the probability or statistics curriculum, however, can enhance the learning curve greatly. A hands-on and a graphic calculator based activities of a missile simulation were introduced by Kim(2003) in the context of order statistics. This article revisits the two activities in his paper and point out a dilemma that occurs from the violation of an assumption on two deviation parameters associated with the missile simulation. A third activity is introduced to resolve the dilemma in the terms of a kernel density estimation which is a nonparametric approach.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.8 no.3
• /
• pp.21-26
• /
• 2009

The finite element analysis of metal forming processes often fails because of severe mesh distortion at large deformation. As the concept of meshless methods, only nodal point data are used for modeling and solving. As the main feature of these methods, the domain of the problem is represented by a set of nodes, and a finite element mesh is unnecessary. This computational methods reduces time-consuming model generation and refinement effort. It provides a higher rate of convergence than the conventional finite element methods. The displacement shape functions are constructed by the reproducing kernel approximation that satisfies consistency conditions. In this research, A meshless method approach based on the reproducing kernel particle method (RKPM) is applied with metal forming analysis. Numerical examples are analyzed to verify the performance of meshless method for metal forming analysis.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.3
• /
• pp.705-712
• /
• 2005

Entropy is the basic concept of information theory. It is well defined for random varibles with known probability density function(pdf). For given data with unknown pdf, entropy should be estimated. Usually, estimation of entropy is based on the approximations. In this paper, we consider a kernel based approximation and compare it to the cumulant approximation method for several distributions. Monte carlo simulation for various sample size is conducted.