scholarly journals Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression

2020 ◽  
Vol 10 (18) ◽  
pp. 6555
Author(s):  
Qiyang Wang ◽  
Feng Han ◽  
Zidan Wu ◽  
Tianyi Lan ◽  
Wenfu Wu
Keyword(s):  
Support Vector Regression ◽  
Grain Quality ◽  
Back Propagation ◽  
Storage Period ◽  
Percentage Error ◽  
Support Vector ◽  
Grain Storage ◽  
Stored Grain ◽  
Support Vector Regression Model ◽  
Multiple Kernel

Grain quality changes during the storage period, and an important grain quality indictor is the free fatty acid (FFA) content. Understanding real-time change of FFA content in stored grain is significant for grain storage safety. However, the FFA content requires manual detection with time-consuming and complex procedures. Thus, this paper is dedicated to developing a method to estimate FFA content in stored grain accurately. We proposed a machine learning approach—multiple-kernel support vector regression—to complete this goal, which improved the accuracy and robustness of the FFA estimation. The effectiveness of the proposed approach was validated by the grain storage data collected from northeast China. To show the merits of the proposed method, several prevailing prediction methods, such as single-kernel support vector regression, multiple linear regression, and back propagation neural network, were introduced for comparative purposes, and several quantitative statistical indexes were adopted to evaluate the performance of different models. The results showed that the proposed approach can achieve a high accuracy with mean absolute error of 0.341 mg KOH/100 g, root mean square error of 0.442 mg KOH/100 g, and mean absolute percentage error of 2.026%. Among the four models tested, the multiple-kernel support vector regression model performed best and made the most robust forecasts of FFA content in stored grain.

scholarly journals ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Xigao Shao ◽  
Kun Wu ◽  
Bifeng Liao
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Market Price ◽  
Stock Index ◽  
Support Vector ◽  
Optimization Strategy ◽  
Support Vector Regression Model ◽  
Stock Price Prediction ◽  
Multiple Kernel ◽  
Multiple Support

Linear multiple kernel learning model has been used for predicting financial time series. However,ℓ1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adoptℓp-norm multiple kernel support vector regression (1≤p<∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better thanℓ1-norm multiple support vector regression model.

scholarly journals Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: a Taiwan case study

2020 ◽  
Author(s):  
Min-Yuan Cheng ◽  
Minh-Tu Cao ◽  
Po-Kun Tsai
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Influential Factors ◽  
Percentage Error ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Symbiotic Organisms Search ◽  
Proper Actions ◽  
Ground Anchors ◽  
Ground Anchor

Abstract Failure of ground anchor is a major cause of landslides and severe natural hazards, especially in the highly developed mountainous areas such as New Taipei City. Accurately estimating load on ground anchors is thus essential for evaluating the stability status of slope to prevent landslide from happening. This study first employed correlation analyses to identify possible influential factors of load on ground anchors. Second, various artificial intelligence models were used to map the relationship of the found influencing factors with the current load on ground anchors. The results indicated that the symbiotic organisms search-optimized least squares support vector regression (SOS-LSSVR) model had the optimal accuracy by earning the smallest value of mean absolute percentage error (9.10%) and the most outstanding value of correlation coefficient (R = 0.988). The study applied the established inference model for the real case of estimating load on un-monitoring ground anchors. The analyzed results strongly advised administrators to conduct site surveying and patrolling more frequently to take early proper actions. In summary, the obtained results have demonstrated SOS-LSSVR as an effective alternative for the conventional subjective evaluation methods, which is able to rapidly provide accurate values of load on un-monitoring ground anchors.

ELECTRICAL POWER PREDICTION OF PROTON EXCHANGE MEMBRANE FUEL CELL BY USING SUPPORT VECTOR REGRESSION

2012 ◽  
Vol 26 (19) ◽  
pp. 1250121 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
S. ZHAO
Keyword(s):  
Fuel Cell ◽  
Regression Model ◽  
Support Vector Regression ◽  
Proton Exchange Membrane ◽  
Electrical Power ◽  
Proton Exchange ◽  
Percentage Error ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Exchange Membrane

Studies have shown that numerous operating parameters affecting the proton exchange membrane fuel cell (PEMFC) performance, such as fuel cell operating temperature, operating pressure, anode/cathode humidification temperatures, anode/cathode stoichiometric flow ratios. In order to improve performance of fuel cell systems, it is advantageous to have an accurate model with which one can predict fuel cell behavior at different operating conditions. In this paper, a model using support vector regression (SVR) approach combining with particle swarm optimization (PSO) algorithm for its parameter optimization was developed to modeling and predicting the electrical power of proton exchange membrane fuel cell. The accuracy and reliability of the constructed support vector regression model are validated by leave-one-out cross-validation. Prediction results show that the maximum absolute percentage error does not exceed 5%, the mean absolute percentage error (MAPE) reached 0.68% and the correlation coefficient (R2) as high as 0.998. This implies that one can estimate an available combination of controller parameters by using support vector regression model to get suitable electrical power of proton exchange membrane fuel cell system.

An efficient multiple kernel support vector regression model for assessing dry weight of hemodialysis patients

2020 ◽  
Vol 15 ◽  
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Bin Shi ◽  
Xiaohua Wang ◽  
Aiyan Du ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Support Vector Regression ◽  
Demographic Data ◽  
Dry Weight ◽  
Hemodialysis Patients ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Multiple Kernel ◽  
Proposed Model ◽  
Average Similarity

Background: Dry Weight (DW) is the lowest weight after dialysis, and patients with lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches have been presented to assess dry weight of hemodialysis patients. However, these traditional methods all depend on special instruments and professional technicians. Objective: In order to avoid this limitation, we want to find a machine-independent way to assess dry weight, so we collected some clinical influencing characteristic data and constructed a Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients. Methods: In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements, and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were closely related to their dry weight. All these relevant data were used to enter the regression equation. Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS) model was proposed to predict the dry weight of hemodialysis patients. Result: The experimental results show that dry weight is positively correlated with BMI and HR. And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negative correlated with dry weight. What's more, the Root Mean Square Error (RMSE) of our model is 1.3817. Conclusion: Our proposed model could serve as a viable alternative of dry weight estimation for hemodialysis patients, thus providing a new way for the clinic.

scholarly journals Prediction and Simulation Spatio-Temporal Support Vector Regression for Nonlinear Data

2020 ◽  
Vol 6 (1) ◽  
pp. 31
Author(s):  
Khalilur Rahman ◽  
Margaretha Ari Anggorowati ◽  
Agung Andiojaya
Keyword(s):  
Support Vector Regression ◽  
Simulation Models ◽  
Percentage Error ◽  
Average Error ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Average Percentage ◽  
Spatio Temporal ◽  
Average Percentage Error ◽  
Prediction And Simulation

<p>Spatio-temporal model forecasting method is a forecasting model that combines forecasting with a function of time and space.  This method is expected to be able to answer the challenge to produce more accurate and representative forecasting. Using the ability of method Support Vector Regression in dealing with data that is mostly patterned non-linear premises n adding a spatial element in the model of forecasting in the form of a model forecasting Spatio- Temporal. Some simulations have done with generating data that follows the Threshold Autoregressive model. The models are correlated into spatial points generated by several sampling methods. Simulation models are generated to comparing the accuracy between model Spatio-Temporal Support Vector Regression and model  ARIMA  based on  Mean  Error,  Mean Average Error, Root Mean Square Error, and Mean Average Percentage Error. Based on the evaluation results, it is shown that forecasting with the Spatio-Temporal Support Vector Regression model has better accuracy than forecasting ARIMA.</p>

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

scholarly journals Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1320
Author(s):  
Yuanyuan Sun ◽  
Gongde Xu ◽  
Na Li ◽  
Kejun Li ◽  
Yongliang Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Heat Dissipation ◽  
Hot Spot ◽  
Load Capacity ◽  
Regression Method ◽  
Support Vector ◽  
Temperature Prediction ◽  
Support Vector Regression Model ◽  
Load Rate

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

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