• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.414-414
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• 2000

This paper presents a method for building a real-time kernel of autonomous mobile robot control systems. Until now, most of robots have their own operation softwares dedicated only for their use. Sometimes, operation softwares were developed based on MS-DOS or other real -time kernel based on UNIX. However, MS-DOS has many restrictions for use as a robot operation system. Also, mix based real-time kernel has some Limitations for use with mobile robots. So, in this paper, we focus on building a real-time kernel based on Linux. The in this paper, the software modules of Task Management, Memory Management, Intertask Communication, and Synchronization are redesigned. To show the efficiency of the paper, it was applied to run Nomad Super Scout II avoiding obstacles detected by sonar sensor array.

• Journal of IKEEE
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• v.21 no.1
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• pp.46-65
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• 2017

This paper proposes a fast patch-based de-blurring algorithm including kernel estimation based on the angle between the edge and the blur direction. For de-blurring, image patches from the most informative edges in the blurry image are used to estimate a kernel with low computational cost. Moreover, the kernels of each patch are estimated based on the correlation between the edge direction and the blur direction. This makes the final kernel more reliable and creates an accurate latent image from the blurry image. The combination of directionally oriented kernel estimation and patch-based de-blurring is faster and more accurate than existing state-of-the art methods. Experimental results using various test images show that the proposed method achieves its objectives: speed and accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.748-750
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• 2005

Isomap [1] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be positive semidefinite. In this paper we employ a constant-adding method, which leads to the Mercer kernel-based Isomap algorithm. Numerical experimental results with noisy 'Swiss roll' data, confirm the validity and high performance of our kernel Isomap algorithm.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.788-796
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• 2003

This paper proposes using dynamic momentum for squential learning method. Using The dynamic momentum improves convergence speed and performance by the variable momentum, also can identify it in the RMSE(root mean squared error). The proposed method is reflected using variable momentum according to current state. While static momentum is equally influenced on the whole, dynamic momentum algorithm can control the convergence rate and performance. According to the variable change of momentum by training. Unlike former classification and regression problems, this paper confirms both performance and regression rate of the dynamic momentum. Using RMSE(root mean square error ), which is one of the regression methods. The proposed dynamic momentum has been applied to the kernel adatron and kernel relaxation as the new sequential learning method of support vector machine presented recently. In order to show the efficiency of the proposed algorithm, SONAR data, the neural network classifier standard evaluation data, are used. The simulation result using the dynamic momentum has a better convergence rate, performance and RMSE than those using the static moment, respectively.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.6
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• pp.365-372
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• 2016

Using the computational fluid dynamics (CFD) technique, we simulated the fluid flow in a Taylor reactor considering the aggregation and breakage of particles. We calculated the population balance equation (PBE) to determine the particle-size distribution by implementing the quadrature method-of-moment (QMOM). It was used that six moments for an initial moments, the sum of Brownian kernel and turbulent kernel for aggregation kernel, and power-law kernel for breakage kernel. We predicted the final mean particle size when the particle had various initial volume fraction values. The result showed that the mean particle size and initial growth rate increased as the initial volume fraction of the particle increased.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.249-251
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• 2019

In the ongoing standardization of Versatile Video Coding (VVC), DCT-2, DST-7 and DCT-8 are designated as the vital primary transform kernels. Due to the effectiveness of DST-4 and DCT-4 in smaller resolution sequences, DST-4 and DCT-4 transform kernel can also be used as the replacement of the DST-7 and DCT-8 transform kernel respectively. While storing all of those transform kernels, ROM memory storage is considered as the major issue. So, to deal with this scenario, a unified DST-3 based transform kernel derivation method is proposed in this paper. The transform kernels used in this paper is DCT-2, DST-4 and DCT-4 transform kernels. The proposed ROM memory required to store the matrix elements is 1368 bytes each of 8-bit precision.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.267-270
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• 2005

커널 함수는 데이터를 high dimension 상의 속성 공간으로 mapping함으로써 복잡한 분포를 가지는 데이터에 대하여 기존의 선형 분류 알고리즘들의 성능을 향상시킬 수 있다. 본 논문에서는 기존의 유클리디안 거리측정방법 대신에 커널 함수에 의한 속성 공간의 거리측정방법을 fuzzy K-nearest neighbor 알고리즘에 적용한 fuzzy kernel K-nearest neighbor(FKKNN) 알고리즘을 제안한다. 제시한 알고리즘은 데이터에 대한 적절한 커널 함수의 선택으로 기존 알고리즘의 성능을 향상 시킬 수 있다. 제시한 알고리즘의 타당성을 보이기 위하여 여러 데이터 집합에 대한 실험결과를 분석한다.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.1003-1011
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• 2008

Mixed-effect Poisson regression models are widely used for analysis of correlated count data such as those found in longitudinal studies. In this paper, we consider kernel extensions with semiparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• International Journal of Reliability and Applications
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• v.6 no.1
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• pp.1-12
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• 2005

A new test for testing that a life distribution is exponential against the alternative that it is harmonic new better (worse) than used in expectation upper tail HNBUET (HNWUET), but not exponential is presented based on the highly popular 'Kernel methods' of curve fitting. This new procedure is competitive with old one in the sense of Pitman's asymptotic relative efficiency, easy to compute and does not depend on the choice of either the band width or kernel. It also enjoys good power.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.