Ensemble of illumination estimation methods using support vector regression

Abstract In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.

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Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

Information ◽

10.3390/info10060217 ◽

2019 ◽

Vol 10 (6) ◽

pp. 217 ◽

Cited By ~ 1

Author(s):

Izzat Aulia Akbar ◽

Tomohiko Igasaki

Keyword(s):

Support Vector Regression ◽

Estimation Method ◽

Electrode Site ◽

Estimation Methods ◽

Support Vector ◽

Physical Damage ◽

Prediction Ability ◽

Estimation Performance ◽

Eeg Data ◽

Electroencephalogram Eeg

As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

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Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

Remote Sensing ◽

10.3390/rs12111903 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1903

Author(s):

Cheng Hu ◽

Shaoyang Kong ◽

Rui Wang ◽

Fan Zhang ◽

Lianjun Wang

Keyword(s):

Support Vector Regression ◽

Cross Section ◽

Radar Cross Section ◽

Estimation Methods ◽

Estimation Accuracy ◽

Support Vector ◽

Feature Subset ◽

Mass Estimation ◽

Optimal Feature Subset ◽

Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

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