• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.742-751
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• 2011

This paper presents a robust 3D model hashing dependent on key and parameter by using heat kernel signature (HKS), which is special shape feature descriptor, In the proposed hashing, we calculate HKS coefficients of local and global time scales from eigenvalue and eigenvector of Mesh Laplace operator and cluster pairs of HKS coefficients to 2D square cells and calculate feature coefficients by the distance weights of pairs of HKS coefficients on each cell. Then we generate the binary hash through binarizing the intermediate hash that is the combination of the feature coefficients and the random coefficients. In our experiment, we evaluated the robustness against geometrical and topological attacks and the uniqueness of key and model and also evaluated the model space by estimating the attack intensity that can authenticate 3D model. Experimental results verified that the proposed scheme has more the improved performance than the conventional hashing on the robustness, uniqueness, model space.

• Journal of KIISE:Information Networking
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• v.34 no.1
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• pp.16-26
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• 2007

It is well known that, in the near future, the lifetime of the IPv4 address space will be limited and available 32-bit IP network addresses will not be left my more. In order to solve such IPv4 address space problem in an effective way, the transition to the new version using IPv6 architecture is inevitably required. This paper presents the design and implementation of IPv4/IPv6 dual stack at the GSM Phone based on Linux Kernel 2.4 IPv6 Protocol Stack. It designs appropriately in GSM Phone environment and it is tested by a network of Linux IPv4/IPv6 dual stack on PPP. The test was processed with a test scenario and it was found that the results were successful.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5375-5400
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• 2016

IOMMU is a hardware unit that is indispensable for DMA. Besides address translation and remapping, it also provides I/O virtual address space isolation among devices and memory access control on DMA transactions. However, currently commodity virtualization platforms lack of IOMMU virtualization, so that the virtual machines are vulnerable to DMA security threats. Previous works focus only on DMA security problem of directly assigned devices. Moreover, these solutions either introduce significant overhead or require modifications on the guest OS to optimize performance, and none can achieve high I/O efficiency and good compatibility with the guest OS simultaneously, which are both necessary for production environments. However, for simulated virtual devices the DMA security problem also exists, and previous works cannot solve this problem. The reason behind that is IOMMU circuits on the host do not work for this kind of devices as DMA operations of which are simulated by memory copy of CPU. Motivated by the above observations, we propose an IOMMU para-virtualization solution called PVIOMMU, which provides general functionalities especially DMA security guarantees for both directly assigned devices and simulated devices. The prototype of PVIOMMU is implemented in Qemu/KVM based on the virtio framework and can be dynamically loaded into guest kernel as a module, As a result, modifying and rebuilding guest kernel are not required. In addition, the device model of Qemu is revised to implement DMA access control by separating the device simulator from the address space of the guest virtual machine. Experimental evaluations on three kinds of network devices including Intel I210 (1Gbps), simulated E1000 (1Gbps) and IB ConnectX-3 (40Gbps) show that, PVIOMMU introduces little overhead on DMA transactions, and in general the network I/O performance is close to that in the native KVM implementation without IOMMU virtualization.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.266-274
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• 2003

This paper considers an incremental support vector learning for the abnormality detection problems. One of the most well-known support vector learning methods for abnormality detection is the so-called SVDD(support vector data description), which seeks the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to modify the SVDD into the direction of utilizing the relation between the optimal solution and incrementally given training data. After a thorough review about the original SVDD method, this paper establishes an incremental method for finding the optimal solution based on certain observations on the Lagrange dual problems. The applicability of the presented incremental method is illustrated via a design example.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.205-207
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• 2002

컴퓨터의 이동성을 지원하기 위해 1996년 IETF (Internet Engineering Task Force)에서 mobile IPv4 를 제정하였고 이에 따른 구현 사례가 등장하였다. 그러나 현재까지 국내외 대학에서 발표된 구현 사례는 주로 유닉스 기반의 플랫폼(리눅스, BSD)에서 개발되었으며 가장 보편적으로 사용되고 있는 운영체제인 윈도우에서, 라우팅 정보가 변경되면 재부팅을 해야 하는 결함때문에 개발에 어려움이 있었다. NT 기반의 윈도우즈가 출시됨에따라 윈도우 플랫폼에서 Mobile IP의 구현이 가능하게 되었으며 본 연구실에서는 윈도우즈 2000 플랫폼에서 Mobile IPv4를 위한 mobile node를 설계하여 POSTECH MIP mobile node를 개발하였다. FOSTECH MIP mobile node는 확장성과 효율성을 목표로 설계되어 커널 스페이스(kernel space)와 유저 스페이스 (user space)에서 동작하도록 구현되었다.

• Bulletin of the Korean Mathematical Society
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• v.36 no.3
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• pp.589-597
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• 1999

Let G be a closed subspace of a Banach space X and let (S,$\Omega$,$\mu$) be a $\sigma$-finite measure space. It was known that $L_1$(S,G) is proximinal in $L_1$(S,X) if and only if $L_p$(S,G) is proximinal in $L_p$(S,X) for 1$\infty$. In this article we show that this result remains true when "proximinal" is replaced by "Chebyshev". In addition, it is shown that if G is a proximinal subspace of X such that either G or the kernel of the metric projection $P_G$ is separable then, for 0 < p $\leq$ $\infty$. $L_p$(S,G) is proximinal in $L_p$(S,X)

• Journal of the Korean Mathematical Society
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• v.53 no.4
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• pp.929-967
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• 2016

Let p(t, x) be the fundamental solution to the problem $${\partial}^{\alpha}_tu=-(-{\Delta})^{\beta}u,\;{\alpha}{\in}(0,2),\;{\beta}{\in}(0,{\infty})$$. If ${\alpha},{\beta}{\in}(0,1)$, then the kernel p(t, x) becomes the transition density of a Levy process delayed by an inverse subordinator. In this paper we provide the asymptotic behaviors and sharp upper bounds of p(t, x) and its space and time fractional derivatives $$D^n_x(-{\Delta}_x)^{\gamma}D^{\sigma}_tI^{\delta}_tp(t,x),\;{\forall}n{\in}{\mathbb{Z}}_+,\;{\gamma}{\in}[0,{\beta}],\;{\sigma},{\delta}{\in}[0,{\infty})$$, where $D^n_x$ x is a partial derivative of order n with respect to x, $(-{\Delta}_x)^{\gamma}$ is a fractional Laplace operator and $D^{\sigma}_t$ and $I^{\delta}_t$ are Riemann-Liouville fractional derivative and integral respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.