Least squares support vector machine （LS-SVM） is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern sear...Least squares support vector machine （LS-SVM） is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern search method. Finally, by decoding aircraft communication addressing and reporting system （ACARS） report, a real-time cruise data set is acquired, and the diagnosis model is adopted to process data. In contrast to the radial basis function （RBF） neutral network, LS-SVM is more suitable for real-time diagnosis of gas turbine engine.展开更多
In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal f...In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal firing table is the ballistic matching for two types of projectiles.Therefore,a method is proposed in the process of designing new type of projectile.The least squares support vector machine is utilized to build the ballistic trajectory model of the original projectile,thus it is viable to compare the two trajectories.Then the particle swarm optimization is applied to find the combination of trajectory parameters which meet the criterion of ballistic matching best.Finally,examples show the proposed method is valid and feasible.展开更多
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines ...Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.展开更多
Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the i...Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.展开更多
A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improv...A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.展开更多
This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration...This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370–1042 nm hyperspectral imager that included visible and near infrared light,germination was tested to confirm estimates.The Savitzky–Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),principal component analysis,and local linear embedding algorithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabili s based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81%.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.展开更多
Mentha haplocalyx(mint)is a significant traditional Chinese medicine(TCM)listed in the Catalogue of’Medicinal and Food Homology’,therefore,its geographical origins(GOs)are critical to the medicinal and food value.La...Mentha haplocalyx(mint)is a significant traditional Chinese medicine(TCM)listed in the Catalogue of’Medicinal and Food Homology’,therefore,its geographical origins(GOs)are critical to the medicinal and food value.Laser-induced breakdown spectroscopy(LIBS)is an advanced analytical technique for GOs certification,due to the fast multi-elemental analysis requiring minimal sample pretreatment.In this study,LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyzes.The spectral data was analyzed by two chemometric algorithms,i.e.principal component analysis(PCA)and least squares support vector machines(LS-SVM).Specifically,the performance of LS-SVM with least kernel and radial basis function(RBF)kernel was explored in sensitivity and robustness tests.Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance(a little inferior)in robustness test.Generally,LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel.The result indicated the potential for future applications in herbs and food,especially for in situ GOs applications of TCM authenticity rapidly.展开更多
Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough ...Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.展开更多
基金The National High Technology Research and Development Program of China (No.2006AA12A108)
文摘Least squares support vector machine （LS-SVM） is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern search method. Finally, by decoding aircraft communication addressing and reporting system （ACARS） report, a real-time cruise data set is acquired, and the diagnosis model is adopted to process data. In contrast to the radial basis function （RBF） neutral network, LS-SVM is more suitable for real-time diagnosis of gas turbine engine.
基金supported by the National Natural Science Foundation of China(No.51006052)
文摘In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal firing table is the ballistic matching for two types of projectiles.Therefore,a method is proposed in the process of designing new type of projectile.The least squares support vector machine is utilized to build the ballistic trajectory model of the original projectile,thus it is viable to compare the two trajectories.Then the particle swarm optimization is applied to find the combination of trajectory parameters which meet the criterion of ballistic matching best.Finally,examples show the proposed method is valid and feasible.
基金supported by the Program for New Century Excellent Talents in University (NoNCET- 08-0836)the National Natural Science Foundation of China (Nos60804022, 60974050 and 61072094)+1 种基金the Fok Ying-Tung Education Foundation for Young Teachers (No121066)by the Natural Science Foundation of Jiangsu Province (No.BK2008126)
文摘Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.
基金funded by the Natural Science Foundation of Shandong Province (ZR2021MD061ZR2023QD025)+3 种基金China Postdoctoral Science Foundation (2022M721972)National Natural Science Foundation of China (41174098)Young Talents Foundation of Inner Mongolia University (10000-23112101/055)Qingdao Postdoctoral Science Foundation (QDBSH20230102094)。
文摘Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.
基金Projects 50774080 supported by the National Natural Science Foundation of China200348 by the Foundation for the National Excellent Doctoral Dis-sertation of China
文摘A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.
基金funded by the National Natural Science Foundation of China(Grant No.31770769)the National Key Research and Development Program of China(No.2017YFC0504403)the Fundamental Research Funds for the Central Universities(No.2015ZCQ-GX-03).
文摘This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370–1042 nm hyperspectral imager that included visible and near infrared light,germination was tested to confirm estimates.The Savitzky–Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),principal component analysis,and local linear embedding algorithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabili s based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81%.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.
基金supported by National Natural Science Foundation of China(Nos.81903796,81603396 and 31870338)the National Key Research and Development Program of China(No.2019YFC1711200)+2 种基金Major new drug innovation project of the ministry of science and technology(2018ZX09201011)Scientific and Technological Planning Projects of Colleges and Universities of Shandong Province(No.J18KA287)Binzhou Medical University Research Startup Fund Project(No.BY2016KYQD02)。
文摘Mentha haplocalyx(mint)is a significant traditional Chinese medicine(TCM)listed in the Catalogue of’Medicinal and Food Homology’,therefore,its geographical origins(GOs)are critical to the medicinal and food value.Laser-induced breakdown spectroscopy(LIBS)is an advanced analytical technique for GOs certification,due to the fast multi-elemental analysis requiring minimal sample pretreatment.In this study,LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyzes.The spectral data was analyzed by two chemometric algorithms,i.e.principal component analysis(PCA)and least squares support vector machines(LS-SVM).Specifically,the performance of LS-SVM with least kernel and radial basis function(RBF)kernel was explored in sensitivity and robustness tests.Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance(a little inferior)in robustness test.Generally,LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel.The result indicated the potential for future applications in herbs and food,especially for in situ GOs applications of TCM authenticity rapidly.
文摘Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.
