• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.451-459
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• 2003

The present paper suggests a method to estimate confidence interval using SV(Support Vector) in LS-SVM(Least-Squares Support Vector Machine). To get the proposed method we used the fact that the values of the hessian matrix obtained by full data set and SV are not different significantly. Since the suggested method implement only SV, a part of full data, we can save computing time and memory space. Through simulation study we justified the proposed method.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.421-422
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• 2020

Hardware In-the-Loop simulation(HIL)은 실제 하드웨어 시스템을 실시간 모사할 수 있는 시뮬레이션 장비로 연구 및 개발 기간의 단축, 비용저감 등의 장점을 앞세워 다양한 전력전자 분야에 사용되고 있다. 실제 하드웨어를 그대로 모사하는 것이 HIL의 목적이기 때문에 HIL 장비는 검증의 실시간성과 출력된 결과의 정확성이 무엇보다도 중요하다고 할 수 있다. 하지만 코어간의 데이터를 주고받는 과정에서 HIL의 연산 속도 및 정확성을 저해하는 요인들이 발생하게 된다. 본 연구에서는 HIL 장비를 이용해 복잡한 시스템을 구현함에 있어서 연산속도 및 정확성을 저해하는 요인들을 찾아내고 이를 해결하기 위한 방법을 제안한다. 제안된 연산속도 개선 및 정확성 개선 방법의 타당성은 프로세서의 연산 속도 변화량, HIL 및 시험 결과 파형의 비교 분석을 통해 검증되었다.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.84-90
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• 2009

In this paper, the new channel estimation was proposed that have the low complexity and high performance using Fuzzy inference method uses recently from various field for estimation about uncertainty in channel estimation of OFDM. Proposed method is channel estimation performance improve, calculation and interpolation for statistics character of channel using the pilot before LS channel estimation by Fuzzy inference method. Simulation result in QPSK proposed channel estimation method shows the enhancement of 5.5dB compared to the LS channel estimation and the deterioration of 1.3dB compared to the MMSE channel estimation in mean square error point $10^{-3}$. symbol error rate shows similarity performance the MMSE $10^{-1.96}$, proposed channel estimation $10^{-1.93}$ and enhancement of $10^{-0.35}$ compared to the LS channel estimation in signal to noise ratio point 20dB.

• Journal of Plant Biotechnology
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• v.40 no.4
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• pp.192-197
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• 2013

This study examined the phenotypic and molecular characteristics of the $2^{nd}$ clone ($T_0V_2$) plants of LeLs-antisense gene-transgenic chrysanthemum line (LeLs80) that exhibited non-branching, proving the relevance of these characteristics as a factor for use in environmental risk assessment. Results of the Southern blot analysis showed that three copies of the LeLs-antisense gene were introduced into the transgenic line, and northern analysis showed that the transcripted gene was normally expressed in the transgenic line. A flanking T-DNA sequencing method was used to determine that sequences of 184 and 464 bps flanked the LeLs-antisense gene in the transgenic line. These sequences, respectively, matched the 35S promoter for expression of the npt II gene and the NOS terminator for expression of the LeLs-antisense gene within the pCAMBIA 2300 vector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12C
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• pp.1208-1215
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• 2009

In OFDM systems, mobile channel degrades the system performance seriously. Therefore, channel estimation technique is required to compensate for the degradation from the channel effects. However, conventional channel estimations in frequency domain induce ICI which is induced from Doppler frequency. In addition, a linear interpolation method causes inaccurate channel estimation. In order to minimize the effect of the interference and interpolation error, the proposed method combines LS method and Kalman filtering algorithm. Channel impulse response is adaptively tracked by Kalman filtering based on the information from LS estimator. Simulation results are presented to verify the performance of the proposed channel estimation over mobile channel environment. Simulation results show that the proposed method can effectively compensate for channel degradation.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1557-1563
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• 2015

Multiclass classification is typically performed using the voting scheme method based on combining binary classifications. In this paper we propose multiclass classification method for large data, which can be regarded as the revised one-vs-all method. The multiclass classification is performed by using the hat matrix of least squares support vector machine (LS-SVM) ensemble, which is obtained by aggregating individual LS-SVM trained on each subset of whole large data. The cross validation function is defined to select the optimal values of hyperparameters which affect the performance of multiclass LS-SVM proposed. We obtain the generalized cross validation function to reduce computational burden of cross validation function. Experimental results are then presented which indicate the performance of the proposed method.

• Design & Manufacturing
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• v.10 no.2
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• pp.1-5
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• 2016

High-speed and high-torque performance is required in the ultra-high speed electric injection molding machine field. To implement this performance, the big-size inverter is needed and the corresponding converter should be used. In this case, the whole cost for configuring the system will be increased. In this paper, we introduce a method which is able to reduce the energy and the cost for configuring the system using the energy regeneration. The energy regeneration method is based on reusing the regeneration power generated at the electric motor during decelerating the injection motion. In this paper, we demonstrate the effectiveness of the method by using the ultra-high speed injection motion.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.481-488
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• 2006

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.137-141
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• 2003

LS-SVM(least squares support vector machine) is a widely applicable and useful machine learning technique for classification and regression analysis. LS-SVM can be a good substitute for statistical method but computational difficulties are still remained to operate the inversion of matrix of huge data set. In modern information society, we can easily get huge data sets by on line or batch mode. For these kind of huge data sets, we suggest an on line pruning regression method by LS-SVM. With relatively small number of pruned support vectors, we can have almost same performance as regression with full data set.