Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts ...Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.展开更多
To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector mac...To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector machine(SVM) and traditional response surface method(RSM), and utilizes experimental samples to construct a suitable response surface function(RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method(MCM); while SRSM(17.296 s) needs far less running time than MCM(10958 s) and RSM(9840 s). Therefore,under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.展开更多
Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sour...Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sources in astronomy. Therefore, efficient machine learning and classification algorithms are required to automate the classifying process. We propose to apply the Support Vector Machine (SVM) algorithm to classify galaxy morphologies and Krylov iterative methods to improve runtime of the classification. The accuracy of the classification is measured on various categories of galaxies from the survey. A three-class algorithm is presented that makes use of multiple SVMs. This algorithm is used to assign the categories of spiral, elliptical, and irregular galaxies. A selection of Krylov iterative solvers are compared based on their efficiency and accuracy of the resulting classification. The experimental results demonstrate that runtime can be significantly improved by utilizing Krylov iterative methods without impacting classification accuracy. The generalized minimal residual method (GMRES) is shown to be the most efficient solver to classify galaxy morphologies.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
A method which integrates expert evaluation method and support vector machine(SVM) method is introduced for failure mode criticality analysis(FMCA) about the gearbox device. An expert evaluation standard is built by u...A method which integrates expert evaluation method and support vector machine(SVM) method is introduced for failure mode criticality analysis(FMCA) about the gearbox device. An expert evaluation standard is built by using expert evaluation method. The experts make scores about the gearbox failure mode. In order to overcome the subjectivity of expert evaluation method, we use SVM method to make a comprehensive prediction about the scores. According to the comprehensive prediction evaluation results, the FMCA of the gearbox device can be obtained. The analysis shows that the method used in this paper not only can effectively solve the problem which is unable to get specific failure rate in the qualitative analysis, but also can solve the problem which needs lots of data in the quantitative analysis.展开更多
Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support v...Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.展开更多
A novel data-driven, soft sensor based on support vector regression （SVR） integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization （HDS） process. A wid...A novel data-driven, soft sensor based on support vector regression （SVR） integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization （HDS） process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm （GA） and sequential quadratic programming （SQP） methods （GA-SQP） was developed. Performance of different optimization algorithms including GA-SQP, GA, pattern search （PS）, and grid search （GS） indicated that the best average absolute relative error （AARE）, squared correlation coefficient （R2）, and computation time （CT） （AARE = 0.0745, R2 = 0.997 and CT = 56 s） was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization （VQ） technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.展开更多
The purpose of this paper is to present a novel way to building quantitative structure-property relationship(QSPR) models for predicting the gas-to-benzene solvation enthalpy(ΔHSolv) of 158 organic compounds based on...The purpose of this paper is to present a novel way to building quantitative structure-property relationship(QSPR) models for predicting the gas-to-benzene solvation enthalpy(ΔHSolv) of 158 organic compounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptors were calculated for each compounds using dragon package. The variable selection technique of enhanced replacement method(ERM) was employed to select optimal subset of descriptors. Our investigation reveals that the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact and that ERM method is unable to model the solvation enthalpy accurately. The standard error value of prediction set for support vector machine(SVM) is 1.681 kJ ? mol^(-1) while it is 4.624 kJ ? mol^(-1) for ERM. The results established that the calculated ΔHSolvvalues by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by ERM one. This indicates that SVM can be used as an alternative modeling tool for QSPR studies.展开更多
The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FS...The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.展开更多
Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel...Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.展开更多
Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructe...Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.展开更多
Geophysical data sets are growing at an ever-increasing rate,requiring computationally efficient data selection （thinning） methods to preserve essential information.Satellites,such as WindSat,provide large data sets...Geophysical data sets are growing at an ever-increasing rate,requiring computationally efficient data selection （thinning） methods to preserve essential information.Satellites,such as WindSat,provide large data sets for assessing the accuracy and computational efficiency of data selection techniques.A new data thinning technique,based on support vector regression （SVR）,is developed and tested.To manage large on-line satellite data streams,observations from WindSat are formed into subsets by Voronoi tessellation and then each is thinned by SVR （TSVR）.Three experiments are performed.The first confirms the viability of TSVR for a relatively small sample,comparing it to several commonly used data thinning methods （random selection,averaging and Barnes filtering）,producing a 10％ thinning rate （90％ data reduction）,low mean absolute errors （MAE） and large correlations with the original data.A second experiment,using a larger dataset,shows TSVR retrievals with MAE ＜ 1 m s-1 and correlations ≥ 0.98.TSVR was an order of magnitude faster than the commonly used thinning methods.A third experiment applies a two-stage pipeline to TSVR,to accommodate online data.The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment,is an order of magnitude faster than the nonpipeline TSVR.Therefore,pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set.This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.展开更多
As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing disc...As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle.展开更多
To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identific...To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.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
The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then t...The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset. The time spent in training SVM with the training dataset compressed by the method is shortened obviously. The result of the experiment shows that the algorithm is effective.展开更多
A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the r...A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.展开更多
This paper proposes a new facial beautification method using facial rejuvenation based on the age evolution. Traditional facial beautification methods only focus on the color of skin and deformation and do the transfo...This paper proposes a new facial beautification method using facial rejuvenation based on the age evolution. Traditional facial beautification methods only focus on the color of skin and deformation and do the transformation based on an experimental standard of beauty. Our method achieves the beauty effect by making facial image looks younger, which is different from traditional methods and is more reasonable than them. Firstly, we decompose the image into different layers and get a detail layer. Secondly, we get an age-related parameter： the standard deviation of the Gaussian distribution that the detail layer follows, and the support vector machine （SVM） regression is used to fit a function about the age and the standard deviation. Thirdly, we use this function to estimate the age of input image and generate a new detail layer with a new standard deviation which is calculated by decreasing the age. Lastly, we combine the original layers and the new detail layer to get a new face image. Experimental results show that this algo- rithm can make facial image become more beautiful by facial rejuvenation. The proposed method opens up a new way about facial beautification, and there are great potentials for applications.展开更多
Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
基金support from "973 Project" (Contract No. 2010CB226706)
文摘Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.
基金Project(51335003)supported by the National Natural Science Foundation of ChinaProject(20111102110011)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector machine(SVM) and traditional response surface method(RSM), and utilizes experimental samples to construct a suitable response surface function(RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method(MCM); while SRSM(17.296 s) needs far less running time than MCM(10958 s) and RSM(9840 s). Therefore,under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.
文摘Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sources in astronomy. Therefore, efficient machine learning and classification algorithms are required to automate the classifying process. We propose to apply the Support Vector Machine (SVM) algorithm to classify galaxy morphologies and Krylov iterative methods to improve runtime of the classification. The accuracy of the classification is measured on various categories of galaxies from the survey. A three-class algorithm is presented that makes use of multiple SVMs. This algorithm is used to assign the categories of spiral, elliptical, and irregular galaxies. A selection of Krylov iterative solvers are compared based on their efficiency and accuracy of the resulting classification. The experimental results demonstrate that runtime can be significantly improved by utilizing Krylov iterative methods without impacting classification accuracy. The generalized minimal residual method (GMRES) is shown to be the most efficient solver to classify galaxy morphologies.
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the National Natural Science Foundation of China(No.11272070)the Natural Science Foundation of Liaoning Province(No.2014028020)+1 种基金the Dalian Science and Technology Project(No.2015A11GX026)the Educational Commission Project of Liaoning Province(No.L2013182)
文摘A method which integrates expert evaluation method and support vector machine(SVM) method is introduced for failure mode criticality analysis(FMCA) about the gearbox device. An expert evaluation standard is built by using expert evaluation method. The experts make scores about the gearbox failure mode. In order to overcome the subjectivity of expert evaluation method, we use SVM method to make a comprehensive prediction about the scores. According to the comprehensive prediction evaluation results, the FMCA of the gearbox device can be obtained. The analysis shows that the method used in this paper not only can effectively solve the problem which is unable to get specific failure rate in the qualitative analysis, but also can solve the problem which needs lots of data in the quantitative analysis.
基金Projects(2013BAB02B01,2013BAB02B03)supported by the National Key Technologies R&D Program of ChinaProjects(41072224,41272347)supported by the National Natural Science Foundation of China
文摘Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.
文摘A novel data-driven, soft sensor based on support vector regression （SVR） integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization （HDS） process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm （GA） and sequential quadratic programming （SQP） methods （GA-SQP） was developed. Performance of different optimization algorithms including GA-SQP, GA, pattern search （PS）, and grid search （GS） indicated that the best average absolute relative error （AARE）, squared correlation coefficient （R2）, and computation time （CT） （AARE = 0.0745, R2 = 0.997 and CT = 56 s） was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization （VQ） technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.
文摘The purpose of this paper is to present a novel way to building quantitative structure-property relationship(QSPR) models for predicting the gas-to-benzene solvation enthalpy(ΔHSolv) of 158 organic compounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptors were calculated for each compounds using dragon package. The variable selection technique of enhanced replacement method(ERM) was employed to select optimal subset of descriptors. Our investigation reveals that the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact and that ERM method is unable to model the solvation enthalpy accurately. The standard error value of prediction set for support vector machine(SVM) is 1.681 kJ ? mol^(-1) while it is 4.624 kJ ? mol^(-1) for ERM. The results established that the calculated ΔHSolvvalues by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by ERM one. This indicates that SVM can be used as an alternative modeling tool for QSPR studies.
基金Supported by the National Natural Science Foundation of China(51006052)
文摘The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.
基金Sponsored by the National Natural Science Foundation of China(51006052)
文摘Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.
文摘Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.
基金NOAA Grant NA17RJ1227 and NSF Grant EIA-0205628 for providing financial support for this worksupported by RSF Grant 14-41-00039
文摘Geophysical data sets are growing at an ever-increasing rate,requiring computationally efficient data selection （thinning） methods to preserve essential information.Satellites,such as WindSat,provide large data sets for assessing the accuracy and computational efficiency of data selection techniques.A new data thinning technique,based on support vector regression （SVR）,is developed and tested.To manage large on-line satellite data streams,observations from WindSat are formed into subsets by Voronoi tessellation and then each is thinned by SVR （TSVR）.Three experiments are performed.The first confirms the viability of TSVR for a relatively small sample,comparing it to several commonly used data thinning methods （random selection,averaging and Barnes filtering）,producing a 10％ thinning rate （90％ data reduction）,low mean absolute errors （MAE） and large correlations with the original data.A second experiment,using a larger dataset,shows TSVR retrievals with MAE ＜ 1 m s-1 and correlations ≥ 0.98.TSVR was an order of magnitude faster than the commonly used thinning methods.A third experiment applies a two-stage pipeline to TSVR,to accommodate online data.The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment,is an order of magnitude faster than the nonpipeline TSVR.Therefore,pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set.This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.
文摘As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle.
基金Sponsored by the National Natural Science Foundation of China(60472110)
文摘To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.
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.
基金the National Natural Science Foundation of China (60503024, 50634010)
文摘The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset. The time spent in training SVM with the training dataset compressed by the method is shortened obviously. The result of the experiment shows that the algorithm is effective.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.
文摘This paper proposes a new facial beautification method using facial rejuvenation based on the age evolution. Traditional facial beautification methods only focus on the color of skin and deformation and do the transformation based on an experimental standard of beauty. Our method achieves the beauty effect by making facial image looks younger, which is different from traditional methods and is more reasonable than them. Firstly, we decompose the image into different layers and get a detail layer. Secondly, we get an age-related parameter： the standard deviation of the Gaussian distribution that the detail layer follows, and the support vector machine （SVM） regression is used to fit a function about the age and the standard deviation. Thirdly, we use this function to estimate the age of input image and generate a new detail layer with a new standard deviation which is calculated by decreasing the age. Lastly, we combine the original layers and the new detail layer to get a new face image. Experimental results show that this algo- rithm can make facial image become more beautiful by facial rejuvenation. The proposed method opens up a new way about facial beautification, and there are great potentials for applications.
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
