Modeling and optimization of spherical motor based on support vector machine and chaos

Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier, the parameters setting is very crucial to the learning results and generalization ability of SVM, and many irrelevant and redundant features degrade the performance of classification, furthermore, the choice of SVM parameters is influenced by the feature subset taken into account and vice versa. So a joint optimization method for selecting features and SVM parameters is proposed. Genetic algorithms, which have a lot of improvements about fitness, crossover and mutation in comparison with simple genetic algorithms, is to achieve a balance between the classification accuracy and the size of the feature subsets selected and have a fast convergence rate. Furthermore, ‘one-against-one’ approach is used to solve multi-class classification problems and a comparison of the model performances is made among the results of the joint optimization and the single optimization of features or SVM parameters in the experiments. The simulation result shows that our approach can find subsets of features for 4-class skin symptoms classification with higher average cross validation accuracy and a higher rate of convergence compared with single optimization of features or SVM parameters.

Application of robust principal component analysis–multivariate adaptive regression splines for the determination of °API gravity in crude oil samples using ATR-FTIR spectroscopy

Modeling the behavior of groundwater levels is necessary to implement sustainable groundwater resource management. Groundwater is a non-linear and complex system, which can be modeled by data-driven models. This study evaluates the performances of data-driven models, support vector machine regression (SVR) and artificial neural network (ANN), for forecasting groundwater levels of confined and unconfined systems at 1-, 2-, and 3-month ahead. This is the first time that confined and unconfined aquifers have been compared using data-driven models. In addition, to identify the optimal input combination, a hybrid gamma test (GT) and genetic algorithm (GA) was used. The coefficient of correlation (R), Mean Absolute Error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and developed discrepancy ratio (DDR) were applied to evaluate the SVR and ANN models. Results showed that the SVR and ANN models were more accurate for the unconfined system than the confined system for forecasts up to 3-month ahead. In both hydrogeological systems for 1-month ahead, the models performed better than for 2- and 3-month ahead forecasts, and the accuracy of the models decreased as the months ahead increased. The SVR model performed better than the ANN model for 1-, 2-, and 3-month ahead groundwater-level forecasting. The SVR model could be successfully used in predicting monthly groundwater in confined and unconfined systems.

This research work presents a method of smokestack emission predicting model based on Latin hypercube sampling (LHS) and support vector machines (SVM). To solve the problem of predicting the concentration of smokestack emission, the Latin hypercube sampling method is adopted in a circular area above the smokestack to choose sample points, while the support vector machines regression analysis is carried out on the small data set and is evaluated by the mean square error. At the same time, an efficient parameter optimization method is proposed to reduce the mean square error and improve the prediction precision. With LIBSVM toolbox of MATLAB achieving the SVM regression analysis, SVM regression analysis and parameter optimization method were verified by a mathematical way. It turns out that the optimal parameters C and σ is respectively 2.8482 and 16, and the mean square error is 0.023011 which is acceptable. This study provides a reference sampling method and prediction model with parameters optimization for the detection of smokestack emission concentration.

Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines

Gas production during the electrochemical treatment of waste liquids has potential safety risks. Herein, hydrogen (H2) production in such treatment was taken as the control target. Electrode combinations with relatively low H2 production were selected for electrochemical treatment during a fracturing flowback fluid experiment, in which mesh-like titanium–based ordinary ruthenium–iridium–palladium-coated electrode was the anode and plate–like titanium–based ordinary ruthenium–iridium–palladium-coated electrode was the cathode. The effects of electrolysis time (t), electrolytic current (I), electrode spacing (D), and other factors on H2 production in the electrolytic process were investigated. On the basis of the experimental data, a model was established using the support vector machine (SVM) method. Firstly, the three parameters of the radial basis function kernel of the model were simultaneously optimized using the quantum–particle swarm optimization algorithm as follows: penalty parameter = 256, kernel parameter = 0.0097039, and loss parameter = 0.014928. Then, an SVM regression model was established according to the three optimal parameters. The correlation coefficient was 0.98291 (r = 0.9914), and the mean–square error was 1.1883. The regression model was used to predict the technological conditions (t, I, and D) for the maximum/minimum H2 production in the electrochemical treatment of fracturing flowback fluid. The values were t = 50 min, I = 1.5 A, and D = 2.0 cm for the maximum H2 production, and t = 30 min, I = 0.5 A, and D = 6.0 cm for the minimum H2 production. Under these process conditions, the predicted maximum/minimum H2 production volumes were 145.04 and 20.47 mL, respectively.

Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

Air permeability is an important index of textiles and has a significant effect on the quality of the fabric. Thus, the air permeability measured by the air permeability tester plays an important role in the textile industry. However, the accuracy of the tester is determined by prediction model precision. A new prediction model for air permeability based on the support vector machine (SVM) was presented in the paper, which can improve the measurement precision and stability of the tester. Three groups of measured open data were used to verify the validity of the model. For each group, 27 samples were used as training data to create the support vector machine regression (SVR) model. The mean square error (MSE) and the correlation coefficient (R) were introduced to evaluate the model. For the selected 3#, 4#, and 6# nozzles, the R values of the SVM regression model (Model 1) were 0.9988, 0.9992, and 0.9995, respectively, whereas for the traditional power function (Model 2), those were 0.9938, 0.9951, and 0.9898, respectively, indicating Model 1 has better correlation and agreement. In addition, the MSE values of Model 1 were 2.3978, 1.5186, and 0.9314, respectively, whereas for Model 2, those were 11.2558, 9.2485, and 36.5991, respectively. As a result, the performance of Model 1 is superior to Model 2, and Model 1 has higher stability and generalization ability, meanwhile demonstrating that Model 1 is practical.

<abstract> <bold><sc>Abstract.</sc></bold> This study evaluated four optical sensor systems in an effort to characterize a robust nondestructive technique to estimate tree-specific oil palm leaflet nutrient deficiency and aid in determining the required fertilizer dosage rates. The four sensor systems used were a handheld reflectance sensor (GreenSeeker 505), a chlorophyll meter (SPAD 502 Plus), a fluorescence sensor (Multiplex 3), and a spectroradiometer (FieldSpec 3). Spectral responses were collected using the above sensors for leaf samples from 164 randomly selected trees from three commercial farms in Malaysia. The spectral data were processed using either stepwise multiple linear regression (SMLR) or rank features techniques for feature extraction. The feature datasets were analyzed using support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and artificial neural network (ANN) classifiers. The SVM and ANN classifiers performed better compared to SIMCA for most nutrient prediction cases. Overall, with SMLR as a feature extraction technique and SVM as a classifier, a spectroradiometer can be used to effectively predict N, P, K, Mg, Ca, and B nutrient levels in oil palm leaflets. The average overall prediction accuracies for the above conditions were in the range of 59% to 85%. The spectroradiometer dataset was further analyzed using SVM regression models for estimating nutrient levels. Either SMLR-selected features or partial least square (PLS) latent variables were used for establishing the above regression models. The models fitted on the SMLR features performed better for N, Mg, Ca, and B, with correlation coefficients (r) for test datasets in the range of 0.45 to 0.78. The best fitted SVM regression models were for boron estimation, with a correlation coefficient of 0.78 and root mean squared error of prediction (RMSEP) of 2.82.

Gas–solid fluidized beds are complex particle systems, and the electrostatic behavior of particles in fluidized beds is even more complex, which is influenced by numerous factors such as particle properties and operating conditions. Current studies focus on the effect of a certain factor on particle charging without a global picture. Furthermore, there is no mathematical model that can describe the interaction of multiple factors on particle charging because it is difficult to build a model for such a complex system. Therefore, a new approach is needed. In this study, a model capable of accurately predicting the surface charge density of particles in monodispersed gas–solid fluidized beds within a certain range was developed based on the literature and experimental data through several machine learning methods including kernel ridge regression (KRR), support vector machine regression (SVR), and multilayer perceptron (MLP). SVR and MLP models gave the best results with R2 equal to 0.980 and 0.979, respectively. However, the sensitivity analysis showed that the MLP model was more reliable than the SVR model. In conclusion, the feasibility of using machine learning to analyze the charging behavior of particles in fluidized beds is demonstrated, and the proposed MLP model can serve as an accurate correlative tool for fast and effective estimation of particle surface charge density in gas–solid fluidized beds.

The changes in China’s stock market are inseparable from the country’s economic development and macroeconomic regulation and control and have far-reaching significance in promoting China’s national economic growth. Compared with the Western developed capital market, China’s current stock market’s main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan.

This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (trn.1); genotypes of PB sires and average of CB records per PB sire (trn.2); and individual CB genotypes and phenotypes (trn.3). The average of CB offspring records was the trait to be predicted from PB sire’s genotype using cross-validation. Single nucleotide polymorphisms (SNPs) were ranked based on the Spearman Rank correlation with the trait. Subsets with an increasing number (from 50 to 2,000) of the most informative SNPs were used as predictor variables in SVM. Prediction performance was the median of the Spearman correlation (SC, interquartile range in brackets) between observed and predicted phenotypes in the testing set. The best predictive performances were obtained when sire phenotypic information was included in trn.1 (0.22 [0.03] for RFI with SVM and 250 SNPs, and 0.12 [0.05] for ADG with SVM and 500–1,000 SNPs) or when trn.3 was used (0.29 [0.16] with Genomic best linear unbiased prediction (GBLUP) for RFI, and 0.15 [0.09] for ADG with just 50 SNPs). Animals from the last two generations were assigned to the testing set and remaining animals to the training set. Individual’s PB own phenotype and genotype improved the prediction ability of CB offspring of young animals for ADG but not for RFI. The highest SC was 0.34 [0.21] and 0.36 [0.22] for RFI and ADG, respectively, with SVM and 50 SNPs. Predictive performance using CB data for training leads to a SC of 0.34 [0.19] with GBLUP and 0.28 [0.18] with SVM and 250 SNPs for RFI and 0.34 [0.15] with SVM and 500 SNPs for ADG. Results suggest that PB candidates could be evaluated for CB performance with SVM and low-density SNP chip panels after collecting their own RFI or ADG performances or even earlier, after being genotyped using a reference population of CB animals.

Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression

Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter &gt;100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter &gt;0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management.

The application of near infrared spectroscopy for quantitative analysis of cotton-polyester textile was investigated in the present work. A total of 214 cotton-polyester fabric samples, covering the range from 0% to 100% cotton were measured and analyzed. Partial least squares and least-squares support vector machine models with all variables as input data were established. Furthermore, successive projection algorithm was used to select effective wavelengths and establish the successive projection algorithm-least-squares support vector machine models, with the comparison of two other effective wavelength selection methods: loading weights analysis and regression coefficient analysis. The calibration and validation results show that the successive projection algorithm-least-squares support vector machine model outperformed not only the partial least squares and least-squares support vector machine models with all variables as inputs, but also the least-squares support vector machine models with loading weights analysis and regression coefficient analysis effective wavelength selection. The root mean squared error of calibration and root mean squared error of prediction values of the successive projection algorithm-least-squares support vector machine regression model with the optimal performance were 0.77% and 1.17%, respectively. The overall results demonstrated that near infrared spectroscopy combined with least-squares support vector machine and successive projection algorithm could provide a simple, rapid, economical and non-destructive method for determining the composition of cotton-polyester textiles.