• Title, Summary, Keyword: SVR

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River stage forecasting models using support vector regression and optimization algorithms (Support vector regression과 최적화 알고리즘을 이용한 하천수위 예측모델)

  • Seo, Youngmin;Kim, Sungwon
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.606-609
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    • 2015
  • 본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

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Semiparametric Nu-Support Vector Regression (정해진 기저함수가 포함되는 Nu-SVR 학습방법)

  • 김영일;조원희;박주영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.81-84
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    • 2003
  • $\varepsilon$-SVR(e-Support Vector Regression)학습방법은 SV(Support Vector)들을 이용하여 함수 근사(Regression)하는 방법으로 최근 주목받고 있는 기법이다. SVM(SV machine)의 한 가지 방법으로, 신경망을 기반으로 한 다른 알고리즘들이 학습과정에서 지역적 최적해로 수렴하는 등의 문제를 한계로 갖는데 반해, 이러한 구조들을 대체할 수 있는 학습방법으로 사용될 수 있다. 일반적인 $\varepsilon$-SVR에서는 학습 데이터와 관사 함수 f사이에 허용 가능한 에러범위 $\varepsilon$값이 학습하기 전에 정해진다. 그러나 Nu-SVR(ν-version SVR)학습방법은 학습의 결과로 최적화 된 $\varepsilon$값을 얻을 수 있다. 정해진 기저함수가 포함되는 $\varepsilon$-SVR 학습방법(Sermparametric SVR)은 정해진 독립 기저함수를 사용하여 함수를 근사하는 방법으로, 일반적인 $\varepsilon$-SVR 학습방범에 비해 우수한 결과를 나타내는 것이 성공적으로 입증된 바 있다. 이에 따라, 본 논문에서는 정해진 기저함수가 포함된 ν-SVR 학습 방법을 제안하고, 이에 대한 수식을 유도하였다. 그리고, 모의 실험을 통하여 제안된 Sermparametric ν-SVR 학습 방법의 적용 가능성을 알아보았다.

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Indoor Environment Recognition of Mobile Robot Using SVR (SVR을 이용한 이동로봇의 실내환경 인식)

  • Shim, Jun-Hong;Choi, Jeong-Won
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.24 no.8
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    • pp.119-125
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    • 2010
  • This paper proposes a new solution about physical problem of autonomous mobile robots system using ultrasonic sensors. An mobile robot uses several sensors for recognition of its circumstance. However, such sensor data are not accurate all the time. A means of removing the noise that sensor data contains constantly, It is possible for simulation to estimate its circumstance based on ultrasonic sensor data by learning algorithm SVR(Support Vector Regression). To use SVR, it is being selected parameter and kernel which are the component of SVR. Selecting the component of SVR, the most accurate parameter data was selected through the tests because it is not existed determined data. In addition, choosing the kernel uses RBF(Radial Basis Function) kernel which is the most generalized. This paper proposes SVR based algorithm to compensate for the above demerits of ultrasonic sensor through the experimentation under three different environments.

Nu-SVR Learning with Predetermined Basis Functions Included (정해진 기저함수가 포함되는 Nu-SVR 학습방법)

  • Kim, Young-Il;Cho, Won-Hee;Park, Joo-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.3
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    • pp.316-321
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    • 2003
  • Recently, support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. It is well-known that among the various support vector learning methods, the so-called no-versions are particularly useful in cases that we need to control the total number of support vectors. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and a no-version support vector learning called $\nu-SVR$. After reviewing $\varepsilon-SVR$, $\nu-SVR$, and a semi-parametric approach, this paper presents an extension of the conventional $\nu-SVR$ method toward the direction that can utilize Predetermined basis functions. Moreover, the applicability of the presented method is illustrated via an example.

Flicker Estimation for Wind Turbine Systems using SVR (SVR을 이용한 풍력 발전 시스템의 플리커 추정)

  • Van, Tan Loung;Nguyen, Thanh Hai;Kim, Ki-Hong;Lee, Dong-Choon
    • The Transactions of the Korean Institute of Power Electronics
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    • v.15 no.4
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    • pp.309-318
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    • 2010
  • This paper presents a simulation model based on support vector regression (SVR) for flicker estimation emitted from the wind turbines. For the SVR training, the voltage variation and flicker level are selected as input and output, respectively. Through the off-line training, the relationship between the voltage variation and flicker level is derived. The required amount of data for the flicker measurement is decreased and its proessing time is also reduced. The simulation and experiment results have shown that the flicker estimation is performed accurately.

e-SVR using IRWLS Procedure

  • Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.1087-1094
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    • 2005
  • e-insensitive support vector regression(e-SVR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the quadratic problem of e-SVR with a modified loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of e-SVR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for e-SVR.

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Mixed-effects LS-SVR for longitudinal dat

  • Cho, Dae-Hyeon
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.2
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    • pp.363-369
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    • 2010
  • In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

Performance Analysis for SVR-MMSE Detection of Constant Modulus Signals in MIMO-OFDM Systems (MIMO-OFDM 시스템에서 Constant Modulus 신호의 SVR-MMSE 검출 성능 분석)

  • Shin, Chul-Min;Seo, Myoung-Seok;Yang, Qing-Hai;Kwak, Kyung-Sup
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.12A
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    • pp.1198-1204
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    • 2006
  • In this paper, we extend SVR-MMSE detection scheme which is proposed in MIMO system to MIMO-OFDM system, and evaluate performance of the system in frequency selective fading channel. First of all, we explain about typical MIMO-OFDM system and detection scheme of constant modulus signals in this system. And compare proposed SVR-MMSE with Zero Forcing, Minimum Mean Square Error which is conventional detection scheme. we identify that the performance of the proposed system is shown different by varying doppler frequency in frequency selective fading channel using jakes channel model. The result of detection performance by the proposed SVR-MMSE in this simulation, it shows that proposed algorithm have a good performance in MIMO-OFDM systems.

Protective effect of Salviae-radix extraction in $H_2O_2$ induced renal cell injury ($H_2O_2$에 의한 신장(腎臟) 세포 손상에 대한 단삼(丹參) 추출물의 방지 효과)

  • Kim, Sang-Beum;Jeong, Ji-Cheon
    • The Journal of Korean Medicine
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    • v.19 no.1
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    • pp.38-48
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    • 1998
  • This study was undertaken to determine whether Salviae-radix (SVR) extraction prevents the oxidant-induced cell injury and thereby exerts protective effect against oxidant-induced inhibition of tetraethylammonium uptake (TEA) in renal corticaJ sices. SVR (5%) attenuated $H_2O_2-induced$ inhibition of TEA uptake. $H_2O_2$ increased LDH release and lipid peroxidation in a dose-dependent manner. These changes were prevented by SVR extraction. The protective effect of SVR on LDH release was dose-dependent over the concentration range of 0.1-0.5%, and that on lipid peroxidation over the concentration ranges of 0.05-2%. SVR significantly prevented Hg-induced lipid peroxidation. SVR extraction (0.5%) increased cellular GSH content in normal and $H_2O_2-treated$ tissues. When slices were treated with 100 mM $H_2O_2$, catalase activity was decreased, which was prevented by 0.5% SVR extraction. The activity of glutathione peroxidase but not superoxide dismutase was significantly increased by 0.5% SVR extraction in $H_2O_2-treated$ tissuces. These results suggest that SVR has an antioxidant action and thereby exerts benefical effect against oxidant-induced impairment of membrane transport function. This effect of SVR is attributed to an increase in endogenous antioxidants such as GSH, catalase and glutathione peroxidase.

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A Study on the Cooperation Program between ULTC and SVR for the Optimal Voltage Regulation (최적전압조정을 위한 ULTC와 SVR의 협조방안에 관한 연구)

  • Kim Mi Young;Rho Dae Seok
    • Proceedings of the KIEE Conference
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    • pp.94-97
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    • 2004
  • 최근 생활수준의 향상과 정보통신 산업의 급속한 발전으로 인하여 전세계적으로 전력품질에 대한 관심이 높아져, 각종 전력품질 개선장치로부터 최적 공급시스템에 이르기까지 다양한 각도에서 전력품질을 향상하기 위한 연구개발이 수행되고 있으며, 특히 정보통신기기 및 정밀제어기기 등의 보급증가로 전압품질에 대한 관심이 높아져가고 있다. 지금까지 배전계통의 전압관리는 배전용 변전소 ULTC(Under Load Tap Changer)와 고압배전선로의 주상변압기에 의해 조정되어 왔으며 최근에 고압배전선로의 전압강하가 $5\%$를 초과하는 선로에 대한 효율적 전압관리를 위해 선로전압조정장치(SVR- Step Voltage Regulator)가 도입되고있다. 그러나 배전용 변전소 ULTC와 고압배전선로의 SVR이 서로 독립적으로 운용되고 있기 때문에 SVR의 효율성이 멀어지고 있는 실정이다. 그러므로 본 논문에서는 변전소 ULTC와 SVR 송출전압의 최적 전압조정을 위한 협조운용 방안을 제시하고자 한다.

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