• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.107-110
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• 2001

In this paper, we propose a multiplication-free DFT kernel computation technique, whose input sequences are approximated into a ring of Algebraic Integers. This paper also gives computational examples for DFT and IDFT. And we proposes an architecture of the DFT using barrel shifts and adds. When the radix is greater than 4, the proposed method has a high Precision property without scaling errors due to twiddle factor multiplication. A possibility of higher radix system assumes that higher performance can be achievable for reducing the DFT stages in FFT.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.221-224
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• 2005

The authors have recently developed a method for identification of Volterra kernels of nonlinear systems by using M-sequence and correlation technique. In this paper, we apply the proposed method to identification of a robot manipulator which has two degrees of freedom. From the results of the experiment, the nonlinear characteristics of the robot manipulator can be identified by the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.103-105
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• 2004

본 논문에서는 프로세스 모니터링 기법에 기반한 DVS 시스템을 제안한다. 이상적인 DVS 시스템은 응용프로그램의 수정 없이 자동으로 수행되어야 하며 프로세스의 QoS를 고려해야 한다. 본 논문은 이를 위해 본 연구의 이전 논문에서 제시한 Kernel Control Path를 모니터링하여 주기적 프로세스의 QoS관련 정보를 추출할 수 있는 기법을 기반으로 DVS 시스템을 제안한다. 제안한 DVS 시스템은 리눅스 운영체제상에서 실제 구현하였으며 관련 연구와의 비교를 위해 관련연구도 구현하여 실험하였다. 이를 통해 제안한 DVS 시스템이 주기적 프로세스의 QoS를 보장하면서 전력소비를 최소화할 수 있음을 밝힌다.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.843-848
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• 1999

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

• Kyungpook Mathematical Journal
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• v.45 no.4
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• pp.471-480
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• 2005

This paper deals with some useful methods of solving the one-dimensional integral equation of Fredholm type. Application of the reduction techniques with a view to inverting a class of integral equation with Lauricella function in the kernel, Riemann-Liouville fractional integral operators as well as Weyl operators have been made to reduce to this class to generalized Stieltjes transform and inversion of which yields solution of the integral equation. Use of Mellin transform technique has also been made to solve the Fredholm integral equation pertaining to certain polynomials and H-functions.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.4
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• pp.277-282
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• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.7
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• pp.1070-1074
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• 1999

Growth and feeding studies were conducted to determine effects of hazelnut kernel oil meal (HKOM) on growth performance (as protein efficiency), and milk production and composition. In the growth study, 24 individually fed Karayaka lambs (4 mo. and 25.55 kg LW) were used to determine protein efficiency calculated using the Slope Ratio Technique. In the feeding trial, 4 Jersey cows were arranged in $4{\times}4$ Latin squares experiment to measure effects of diets containing HKOM, soybean meal (SBM) corn gluten meal (CGU) and urea (U) on milk production and composition. Protein efficiencies for HKOM, SBM and CGM were found as $1.342{\pm}0.499$, $0.879{\pm}0.488$ and $1.833{\pm}0.893$, respectively. Milk production for the cows consuming concentrates, containing HKOM, SBM, CGM and U, were $13.97{\pm}0.99$, $13.20{\pm}1.09$, $14.86{\pm}0.68 $ and $13.06{\pm}1.23kg/d$ (p<0.01), respectively. There were no differences (p>0.05) among diets for milk protein content were statistically different (p<0.05), although milk DM and fat percentage as well as milk solids-not-fat and lactose percentage (p<0.01). The highest DM intake was associated with the U diet, intake was intermediate with the SBM and HKOM diets, and the lowest with CGM diet (p<0.05). In conclusion, there data may indicate that the HKOM is useful in diets as a protein source for growing ruminants and lactating cows.

• Journal of the Korea Society of Computer and Information
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• v.18 no.3
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• pp.1-10
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• 2013

In this paper, we propose a method of embedded linux light-weight to improve efficiency of application running on embedded systems. Three methods including fast booting scheme applying the Hibernation technique, JFFS2 file system optimization applying the Symbolic Link and virtual address mapping, kernel light-weight that guarantees the general purpose was applied. Since then check the system dependency and generate kernel image according to the target embedded kit. And embedded system performance of existing linux and linux which the method proposed in this paper was compared. In experimental result, the kernel size was 9.6% improved and the system booting time was 18% improved. And application processing speed on target embedded kit was improved 11% in the best case, 66% in the worst case. This result show that the light-weight method proposed in this paper is guarantee fast booting time and securing resources and it is good for the application processing speed improvement.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.25-33
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• 2015

As Data mining is a method of extracting the information based on the large data, the technique has been used in many application areas to deal with data in particular. However, the status of the algorithm that can deal with the healthcare data are not fully developed. In this paper, One of clustering algorithm, the EM and DBSCAN are used for performance comparison. It could be analyzed using by the same data. To do this, EM and DBSACN algorithm are changing performance according to the variables in Health expenditure database. Based on the results of the experimental data, We analyze more precise and accurate results using by Kernel Filtering. In this study, we tried comparison of the performance for the algorithm as well as attempt to improve the performance. Through this work, we were analyzed the comparison result of the application of the experimental data and of performance change according to expansion algorithm. Especially, Collects data from the various cluster using the medical record, it could be recommended the effective spending on medical services.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.1-11
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• 2012

Recently, we have seen several implementations that virtualize the ARM architecture. Since the current ARM architecture is not possible to be virtualized using the traditional technique called "trap-and-emulation", we usually detect all virtualization sensitive instructions during the run-time of a guest kernel and emulate them virtually rather than executing them directly. The emulation for virtualization is usually implemented either by binary translation or interpretation. Our research is about how to improve the performance of emulation for virtualization based on interpretation. The interpretation usually requires a few steps: instruction fetching, instruction decoding and instruction executing. In this paper, we propose a method that decodes all virtualization sensitive instructions during the compilation time of a guest kernel and reduces the time required for interpretation during the run time of the guest kernel. Our method provides both implementation simplicity and performance improvement of emulation for virtualization based on interpretation.