The principle of the support vector regression machine（SVR） is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly...The principle of the support vector regression machine（SVR） is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filteringfrom the longitudinal direction.展开更多
Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for a...Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).展开更多
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona...In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating ...Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.展开更多
Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine ...Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.展开更多
In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using ...In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression （SVR） method than by the support vector classification method.展开更多
The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for...The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /展开更多
Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. ...Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.展开更多
As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit...As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.展开更多
OBJECTIVE:To study the development mechanism of kidney-Yang deficiency through the establishment of support vector machine models of relevant hormones of the pituitary-target gland axes in rats with kidney-Yang defici...OBJECTIVE:To study the development mechanism of kidney-Yang deficiency through the establishment of support vector machine models of relevant hormones of the pituitary-target gland axes in rats with kidney-Yang deficiency syndrome.METHODS:The kidney-Yang deficiency rat model was created by intramuscular injection of hydrocortisone,and contents of the hormones of the pituita- ry-thyroid axis:thyroid stimulating hormone(TSH),3,3',5-triiodothyronine(T_3) and thyroxine(T_4);hormones of the pituitary-adrenal gland axis:adrenocorticotropic hormone(ACTH) and Cortisol(CORT);and hormones of the pituitary-gonadal axis:luteinizing hormone(LH),follicle-stimulating hormone(FSH),and testosterone(T),were determined in the early,middle,and advanced stages.Ten support vector regression(SVR) models of the hormones were established to analyze the mutual relationships among the hormones of the three axes.RESULTS:The feedback control action of the pituitary-adrenal axis began to lose efficacy from the middle stage of kidney-Kong deficiency.The contents all hormones of the three pituitary-target gland axes decreased in the advanced stage.Relative errors of the jackknife test of the SVR models all were less than 10%.CONCLUSION:Imbalances in mutual regulation among the hormones of the pituitary-target gland axes,especially loss of effectiveness of the pituitary-adrenal axis,is one pathogenesis of kidney-Yang deficiency.The SVR model can accurately reflect the complicated non-linear relationships among pituitary-target gland axes in rats with of kidney-Yang deficiency.展开更多
The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in p...The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem.展开更多
Metal may affect maternal immune function,but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin(Ig)levels.Based on the Hangzhou Birth Cohort Study,1...Metal may affect maternal immune function,but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin(Ig)levels.Based on the Hangzhou Birth Cohort Study,1059 participants were included,and eleven metals in whole blood samples and serum IgA,IgG,IgE and IgM levels were measured.Linear regression,quantile-based g-computation(QGC),and Bayesian kernel machine regression(BKMR)models were used to evaluate the associations.Compared with the first tertile of metal levels,arsenic(As)was negatively associated with IgE(β=-0.25,95%confidence interval(CI)=-0.48 to-0.02).Moreover,significant associations of manganese(Mn)with IgA,IgG and IgM were demonstrated(β=0.10,95%CI=0.04 to 0.18;β=0.07,95%CI=0.03 to 0.12;β=0.10,95%CI=0.03 to 0.18,respectively).Cadmium(Cd)were associated with higher levels of IgM.QGC models showed the positive association of the metalmixtures with IgA and IgG,with Mn playing amajor role.Mn and Cd had positive contributions to IgM,while As had negative contributions to IgE.In the BKMR models,the latent continuous outcomes of IgA and IgG showed a significant increase when all the metals were at their 60th percentile or above compared to those at their 50th percentile.Therefore,exposure to metals was associated with maternal Igs,and mainly showed that Mn was associated with increased levels of IgA,IgG and IgM,and As was associated with low IgE levels.展开更多
Anemia is still prevalent among low and middle-income countries,posing serious family and social burdens.However,scarce studies provided evidence for real-world exposure to polycyclic aromatic hydrocarbons(PAHs)and an...Anemia is still prevalent among low and middle-income countries,posing serious family and social burdens.However,scarce studies provided evidence for real-world exposure to polycyclic aromatic hydrocarbons(PAHs)and anemia among pregnant women,as well as involved biological mechanisms.So,we conducted this study including 1717 late pregnant women fromZunyi Birth Cohort and collected urine samples for PAHs metabolites detection.Logistic regression and restricted cubic spline regression were used to examine exposuredisease risks and dose-response relationships.We conducted Bayesian kernel machine regression,weighted quantile sum regression,and quantile g-computation regression to fit the joint impacts of multiple PAHs in the real-world scenario on hypocalcemia and anemia.Results showed single exposure to 2-OHNap,2-OHFlu,9-OHFlu,1-OHPhe,2-OHPhe,3-OHPhe,and 1-OHPyr(all P-trend<0.05)increased the risks of hypocalcemia and anemia.Moreover,PAHs mixture was significantly related to higher risks of hypocalcemia and anemia,with 3-OHPhe and 1-OHPyr identified as their major drivers,respectively.Importantly,hypocalcemia served as a significant biological mechanism responsible for PAHs and anemia.Our findings suggest that individual and joint exposure to PAHs during late pregnancy elevate the anemia risk,and calcium supplementation might be a low-cost intervention target for reducing the PAHs-related impairment on anemia for pregnant women.展开更多
In this work a Support Vector Machine Regression（SVMR） algorithm is used to calculate local magnitude（MI） using only five seconds of signal after the P wave onset of one three component seismic station. This algor...In this work a Support Vector Machine Regression（SVMR） algorithm is used to calculate local magnitude（MI） using only five seconds of signal after the P wave onset of one three component seismic station. This algorithm was trained with 863 records of historical earthquakes, where the input regression parameters were an exponential function of the waveform envelope estimated by least squares and the maximum value of the observed waveform for each component in a single station. Ten-fold cross validation was applied for a normalized polynomial kernel obtaining the mean absolute error for different exponents and complexity parameters. The local magnitude（MI） could be estimated with 0.19 units of mean absolute error. The proposed algorithm is easy to implement in hardware and may be used directly after the field seismological sensor to generate fast decisions at seismological control centers, increasing the possibility of having an effective reaction.展开更多
This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a question...This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents.In other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new pandemic.To focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)algorithms.According to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and anxiety.The health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age factors.The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period.The results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.展开更多
Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear re...Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.展开更多
X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image...X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.展开更多
Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidificat...Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.展开更多
In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support ve...In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.展开更多
基金Supported by National Natural Science Foundation of China（ No. 50705030）.
文摘The principle of the support vector regression machine（SVR） is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filteringfrom the longitudinal direction.
基金supported by the Natural Science Foundation of Shanghai(23ZR1463600)Shanghai Pudong New Area Health Commission Research Project(PW2021A-69)Research Project of Clinical Research Center of Shanghai Health Medical University(22MC2022002)。
文摘Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).
基金National High-tech Research and Development Pro-gram (2006AA04Z405)
文摘In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金This research was funded by the National Natural Science Fund of China[grant number 41701415]Science fund project of Wuhan Institute of Technology[grant number K201724]Science and Technology Development Funds Project of Department of Transportation of Hubei Province[grant number 201900001].
文摘Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.
基金Project supported by the National Natural Science Foundation of China （Grant No 60602034） and the Natural Science Foundation of Jiangxi Province, China （Grant No 0611031）.
文摘Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10674172 and 10874229)
文摘In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression （SVR） method than by the support vector classification method.
文摘The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /
文摘Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.
基金supported by the National Natural Science Foundation of China (61074127)
文摘As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.
基金Supported by National Natural Science Foundation for Young Scholars of China(Study on the Mechanism of Kidney-Yang Deficiency Regulation with Yougui Pill Base on Support Vector Regression Machine,No.81403153)National Natural Science Foundation of China(General Program,Study on the Mechanisms of the Premature Ovarian Failure Regulation by Herbs Couples of Cuscuta Chinessis-Radix Bupleuri in Dingjing Decoction based on RBF Artificial Neural Networks,No.81073073) and National Natural Science Foundation of China(General Program,Study on the law of compatibility of Categorized Formula for Tonifying Kidney Yang based on Rough Set,No.30973977)
文摘OBJECTIVE:To study the development mechanism of kidney-Yang deficiency through the establishment of support vector machine models of relevant hormones of the pituitary-target gland axes in rats with kidney-Yang deficiency syndrome.METHODS:The kidney-Yang deficiency rat model was created by intramuscular injection of hydrocortisone,and contents of the hormones of the pituita- ry-thyroid axis:thyroid stimulating hormone(TSH),3,3',5-triiodothyronine(T_3) and thyroxine(T_4);hormones of the pituitary-adrenal gland axis:adrenocorticotropic hormone(ACTH) and Cortisol(CORT);and hormones of the pituitary-gonadal axis:luteinizing hormone(LH),follicle-stimulating hormone(FSH),and testosterone(T),were determined in the early,middle,and advanced stages.Ten support vector regression(SVR) models of the hormones were established to analyze the mutual relationships among the hormones of the three axes.RESULTS:The feedback control action of the pituitary-adrenal axis began to lose efficacy from the middle stage of kidney-Kong deficiency.The contents all hormones of the three pituitary-target gland axes decreased in the advanced stage.Relative errors of the jackknife test of the SVR models all were less than 10%.CONCLUSION:Imbalances in mutual regulation among the hormones of the pituitary-target gland axes,especially loss of effectiveness of the pituitary-adrenal axis,is one pathogenesis of kidney-Yang deficiency.The SVR model can accurately reflect the complicated non-linear relationships among pituitary-target gland axes in rats with of kidney-Yang deficiency.
基金supported by the National Natural Science Foundation of China(50576033)
文摘The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem.
基金supported by the National Natural Science Foundation of China(No.U22A20358)Zhejiang Provincial Program for the Cultivation of High-Level Innovative Health Talents(No.2020-18).
文摘Metal may affect maternal immune function,but few epidemiological studies have reported the associations between multiple-metal exposure and maternal immunoglobulin(Ig)levels.Based on the Hangzhou Birth Cohort Study,1059 participants were included,and eleven metals in whole blood samples and serum IgA,IgG,IgE and IgM levels were measured.Linear regression,quantile-based g-computation(QGC),and Bayesian kernel machine regression(BKMR)models were used to evaluate the associations.Compared with the first tertile of metal levels,arsenic(As)was negatively associated with IgE(β=-0.25,95%confidence interval(CI)=-0.48 to-0.02).Moreover,significant associations of manganese(Mn)with IgA,IgG and IgM were demonstrated(β=0.10,95%CI=0.04 to 0.18;β=0.07,95%CI=0.03 to 0.12;β=0.10,95%CI=0.03 to 0.18,respectively).Cadmium(Cd)were associated with higher levels of IgM.QGC models showed the positive association of the metalmixtures with IgA and IgG,with Mn playing amajor role.Mn and Cd had positive contributions to IgM,while As had negative contributions to IgE.In the BKMR models,the latent continuous outcomes of IgA and IgG showed a significant increase when all the metals were at their 60th percentile or above compared to those at their 50th percentile.Therefore,exposure to metals was associated with maternal Igs,and mainly showed that Mn was associated with increased levels of IgA,IgG and IgM,and As was associated with low IgE levels.
基金supported by the Science&Technology Program of Guizhou Province(Nos.QKHPTRC-GCC[2022]039-1,QKHPTRC-CXTD[2022]014,and QKHHBZ[2020]3002)the Scientific Research Programof Guizhou Provincial Department of Education(No.QJJ[2023]019).
文摘Anemia is still prevalent among low and middle-income countries,posing serious family and social burdens.However,scarce studies provided evidence for real-world exposure to polycyclic aromatic hydrocarbons(PAHs)and anemia among pregnant women,as well as involved biological mechanisms.So,we conducted this study including 1717 late pregnant women fromZunyi Birth Cohort and collected urine samples for PAHs metabolites detection.Logistic regression and restricted cubic spline regression were used to examine exposuredisease risks and dose-response relationships.We conducted Bayesian kernel machine regression,weighted quantile sum regression,and quantile g-computation regression to fit the joint impacts of multiple PAHs in the real-world scenario on hypocalcemia and anemia.Results showed single exposure to 2-OHNap,2-OHFlu,9-OHFlu,1-OHPhe,2-OHPhe,3-OHPhe,and 1-OHPyr(all P-trend<0.05)increased the risks of hypocalcemia and anemia.Moreover,PAHs mixture was significantly related to higher risks of hypocalcemia and anemia,with 3-OHPhe and 1-OHPyr identified as their major drivers,respectively.Importantly,hypocalcemia served as a significant biological mechanism responsible for PAHs and anemia.Our findings suggest that individual and joint exposure to PAHs during late pregnancy elevate the anemia risk,and calcium supplementation might be a low-cost intervention target for reducing the PAHs-related impairment on anemia for pregnant women.
文摘In this work a Support Vector Machine Regression（SVMR） algorithm is used to calculate local magnitude（MI） using only five seconds of signal after the P wave onset of one three component seismic station. This algorithm was trained with 863 records of historical earthquakes, where the input regression parameters were an exponential function of the waveform envelope estimated by least squares and the maximum value of the observed waveform for each component in a single station. Ten-fold cross validation was applied for a normalized polynomial kernel obtaining the mean absolute error for different exponents and complexity parameters. The local magnitude（MI） could be estimated with 0.19 units of mean absolute error. The proposed algorithm is easy to implement in hardware and may be used directly after the field seismological sensor to generate fast decisions at seismological control centers, increasing the possibility of having an effective reaction.
文摘This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents.In other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new pandemic.To focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)algorithms.According to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and anxiety.The health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age factors.The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period.The results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.
基金supported by the National Natural Science Foundation of China[rant Nos.81960583,81760577,81560523 and 82260629]Major Science and Technology Projects in Guangxi[GKAA22399 and AA22096026]+3 种基金the Guangxi Science and Technology Development Project[Grant Nos.AD 17129003 and 18050005]the Guangxi Natural Science Foundation for Innovation Research Team[2019GXNSFGA245002]the Innovation Platform and Talent Plan in Guilin[20220120-2]the Guangxi Scholarship Fund of Guangxi Education Department of China。
文摘Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.
基金National Natural Science Foundation of China(No.61305118)
文摘X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.
基金Project (No. 2003AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.
基金co-supported by Aeronautical Science Foundation of China (No. 2010ZB52011)Funding of Jiangsu Innovation Program for Graduate Education (No.CXLX11_0213)
文摘In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.
