• Journal of the Korean Data and Information Science Society
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• 제21권2호
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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.

• 산업경영시스템학회지
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• 제43권4호
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• pp.107-115
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• 2020

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.155-163
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• 2005

This paper applies Support Vector Regression (SVR) to estimate and forecast nonlinear autoregressive integrated (ARI) model of the daily exchange rates of four currencies (Swiss Francs, Indian Rupees, South Korean Won and Philippines Pesos) against U.S. dollar. The forecasting abilities of SVR are compared with linear ARI model which is estimated by OLS. Sensitivity of SVR results are also examined to kernel type and other free parameters. Empirical findings are in favor of SVR. SVR method forecasts exchange rate level better than linear ARI model and also has superior ability in forecasting the exchange rates direction in short test phase but has similar performance with OLS when forecasting the turning points in long test phase.

• 한국전자파학회논문지
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• 제29권11호
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• pp.868-877
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• 2018

본 논문에서는 SVR(Support Vector Regression)을 이용한 컨포멀 배열 안테나의 빔 형성 알고리즘을 제안한다. 기존의 최소자승법 기반 알고리즘은 모든 샘플의 오차를 고려하는 반면에, SVR은 정해진 오차 한계를 벗어나는 샘플들을 통해 가중치를 결정하여 희소(sparse)한 해를 가지며 과적합(over-fitting) 문제를 최소화하는 장점을 갖고 있다. 제안된 알고리즘의 성능을 검증하기 위해 실험적으로 측정된 컨포멀 배열 안테나 능동 소자 패턴을 SVR에 적용하여 목적 빔 배턴으로 근사시키는 가중치를 구하였으며, SVR로 얻은 가중치와 최소자승법을 통해 얻은 가중치를 실측한 소자패턴에 적용하여 빔 형성 성능을 비교하였다.

• 한국지능시스템학회논문지
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• 제20권4호
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• pp.483-488
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• 2010

센서시스템을 이용한 상시감시는 발전소의 효율적인 운전과 안전을 담보하는 데 필수적이다. 상시감시기술을 구현하기 위해서는 우선 센서로부터 전송된 신호로부터 발전소 운전파라미터의 참값을 예측하는 모델 즉 Auto-association (AA) 모델을 확보하는 것이 중요하다. 이를 위해 본 논문에서는 Support Vector Regression (SVR)과 Partial Least Square Regression (PLSR)을 이용하는 방안을 각각 제시한다. 이렇게 해서 구축된 모델은 모니터해야 할 파라미터가 많을 때에도 쉽게 적용할 수 있다. 실제 발전소에서 수집된 데이터셋을 이용하여 AA 모델링의 정확도 및 민감도를 비교한 결과, 정확도 면에서는 SVR이 우수한 반면 민감도 면에서는 PLSR이 다소 나은 것으로 나타났다.

• 한국컴퓨터정보학회논문지
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• 제14권7호
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• pp.17-24
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• 2009

정보시스템에 대한 이용이 늘어남에 따라 소프트웨어 개발 요구와 개발 비용이 증가하게 되었다. 기존에는 통계적 알고리즘 기반의 회귀분석을 이용하여 소프트웨어 개발비용을 산정하였으나 오늘날은 기계학습 방법들이 많이 연구되고 있다. 본 논문에서는 기계학습 기술의 하나인 SVR를 사용하여 소프트웨어 비용을 산정하였고, 이 때 SVR에서 사용하는 파라미터들의 최적 조합을 면역계의 동작원리를 적용한 면역 알고리즘을 적용하여 최적 조합을 찾았다. 소프트웨어 비용산정을 위해 세대수, 기억세포수, 대립유전자수를 변경해 가면서 면역 알고리즘 기반의 SVR을 적용하였고, 그 실험 결과를 기존 연구된 다른 기계학습 방법과 비교 분석하였다.

• Structural Engineering and Mechanics
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• 제63권1호
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• pp.115-124
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• 2017

In this paper, a different technique to predict the effects of soil-structure interaction (SSI) on seismic response of building systems is investigated. The technique use a machine learning algorithm called Support Vector Regression (SVR) with technical and analytical results as input features. Normally, the effects of SSI on seismic response of existing building systems can be identified by different types of large data sets. Therefore, predicting and estimating the seismic response of building is a difficult task. It is possible to approximate a real valued function of the seismic response and make accurate investing choices regarding the design of building system and reduce the risk involved, by giving the right experimental and/or numerical data to a machine learning regression, such as SVR. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The results show that the performance of the technique can be predicted by reducing the number of real data input features. Further, performance enhancement was achieved by optimizing the RBF kernel and SVR parameters through grid search.

• 한국해양공학회지
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• 제37권5호
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• pp.198-204
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• 2023

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

• Journal of the Korean Data and Information Science Society
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• 제24권2호
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• pp.391-399
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• 2013

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

• 전력전자학회논문지
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• 제15권4호
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• pp.309-318
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• 2010

본 논문은 풍력 터빈에서 발생되는 플리커를 추정하기 위하여 SVR(Support Vector Regression)을 이용한 시뮬레이션 모델을 제시한다. SVR 훈련을 위한 입력은 전압 변동이고 출력은 플리커로 설정한다. 오프라인 훈련을 통해 출력전압과 플리커의 관계가 도출된다. 제안된 기법은 플리커를 추정하는데 필요한 데이터 양을 줄이고 또 연산시간을 감소시킨다. 제안된 알고리즘은 시뮬레이션과 실험을 통해 그 타당성이 검증된다.