• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1998.10a
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• pp.487-490
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• 1998

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.195-203
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• 2022

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.347-351
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• 1991

We propose a real time kernel chimera implemented under AT&T UNIX motorola versoin, Carnegie Mellon Univ. in U.S first developed chimera using SUN Worstation with Berkley UNIX based on VMEbus. The major differences between Canegie Mellon's and ours are downloading program and communication method between host and target. Original chimera used device driver but we used UNIX system call corresponding to shared memory when user downloads program and communicates. We modified kernel itself because the two different UNIX have different link editor.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.300-305
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• 2002

In this paper, we use the modified gaussian kernel function as clustering distance measure and recast the given hyper-ellipsoidal clustering problem as the optimization problem that minimizes the volume of hyper-ellipsoidal clusters, respectively and solve this using EVP (eigen value problem) that is one of the LMI (linear matrix inequality) techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.67-75
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• 2022

When acquiring a product having an OS, it is very important to identify the exact kernel version of the OS. This is because the product's administrator needs to keep checking whether a new vulnerability is found in the kernel version. Also, if there is an acquisition requirement for exclusion or inclusion of a specific kernel version, the kernel identification becomes critical to the acquisition decision. In the case of the Linux kernel used in various equipment, sometimes it becomes difficult to pinpoint the device's exact version. The reason is that many manufacturers often modify the kernel to produce their own firmware optimized for their device. Furthermore, if a kernel patch is applied to the modified kernel, it will be very different from its base kernel. Therefore, it is hard to identify the Linux kernel accurately by simple methods such as a specific file existence test. In this paper, we propose a static method to classify a specific kernel version by analyzing function names stored in the symbol table. In an experiment with 100 Linux devices, we correctly identified the Linux kernel version with 99% accuracy.

• Honam Mathematical Journal
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• v.41 no.1
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• pp.163-174
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• 2019

The veritable pursuit of this exegesis is to exhibit integrals affined with the Bessel-Struve kernel function, which are explicitly inscribed in terms of generalized (Wright) hypergeometric function and also the product of generalized (Wright) hypergeometric function with sum of two confluent hypergeometric functions. Somewhat integrals involving exponential functions, modified Bessel functions and Struve functions of order zero and one are also obtained as special cases of our chief results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.381-389
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• 2017

The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.507-519
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• 2000

n this paper, we implement the real-time scheduler which performs extended schedulability testing, to reconfigure Mach kernel in which Real-Time scheduling is possible. for this purpose, first, we propose the configuration factors according to requirements of Real-Time operation systems and we analyze a Real-time scheduling algorithm. Second, for the reconfiguration of Mach kernel, we propose the modified data structure through the analysis of Mach kernel environments and scheduling. Third, we suggest the extended scheduling method by analyzing conventional Real-Time scheduling policies. Fourth, we implement the scheduler which executes tasks according to the Earliest-Deadline-First scheduling and the Rate Monotonic scheduling.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.243-248
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• 2003

An incremental kernel principal component analysis (IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis (KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the feature extraction and classification problem on nonlinear data set.

• Bulletin of the Korean Mathematical Society
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• v.42 no.2
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• pp.387-396
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• 2005

In the setting of the half-plane of the complex plane, we introduce a modified reproducing kernel and we show that for $r>-1/2,\;B^{1,r}-cancellation$ property holds and the Bloch space is the dual space of $B^{1,r}$.