To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellit...To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.展开更多
Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf colo...Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.展开更多
Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study,...Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study, we develop an image algorithm for preserved eggshell＇s crack detection by using natural light and polarized image. Four features including crack length, crack state coefficient, maximum projection and angular point are extracted from the natural light image by morphology calculus algorithms. The support vector machines（SVM） model with radial basis kernel function is established using the four features with an accuracy of about 92%. The detection accuracy is improved to 94% by using a new characteristic parameter of crack length on polarization image. The Multi-information fusion analysis indicates the potential for cracks detection by a real-time synthesis imaging system.展开更多
We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM ...We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.展开更多
This study proposes an optimized method for estimating atomic nucleus masses by combining the finiterangedroplet model (FRDM) with the support vector machine algorithm. The optimization process significantly improvest...This study proposes an optimized method for estimating atomic nucleus masses by combining the finiterangedroplet model (FRDM) with the support vector machine algorithm. The optimization process significantly improvesthe accuracy of the FRDM by reducing the root mean square error from 0.606 to 0.253 MeV. The optimizedmass data obtained from this method are then used to calculate the evaporation residue cross-sections (ERCSs) forfusion-evaporation reactions, employing the di-nuclear system model. The experimental results for the 48Ca+238U reactionare relatively well reproduced using these optimized mass data. Additionally, the study investigates the impactof mass uncertainties on fusion and survival probabilities. By considering the mass uncertainties, the ERCSs fornew elements 119 and 120 are predicted based on the obtained optimized mass data.展开更多
基金The National Key Research and Development Plan of China(No.2018YFB0505103)the National Natural Science Foundation of China(No.61873064)。
文摘To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.XDJK2019D041)the Research Innovation Programs for graduate student of Chongqing,China(Grant No.CYS19123)the National Undergraduate Innovation and Entrepreneurship Training Programs(Grant No.201810635015).
文摘Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.
基金Supported by the Fundamental Funds for Central University(2662014BQ062)
文摘Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study, we develop an image algorithm for preserved eggshell＇s crack detection by using natural light and polarized image. Four features including crack length, crack state coefficient, maximum projection and angular point are extracted from the natural light image by morphology calculus algorithms. The support vector machines（SVM） model with radial basis kernel function is established using the four features with an accuracy of about 92%. The detection accuracy is improved to 94% by using a new characteristic parameter of crack length on polarization image. The Multi-information fusion analysis indicates the potential for cracks detection by a real-time synthesis imaging system.
基金supported by the National Natural Science Foundation of China(Grant Nos.10778724,11178021 and 11033001)the Natural Science Foundation of Education Department of Hebei Province (Grant No.ZD2010127)the Young Researcher Grant of National Astronomical Observatories,Chinese Academy of Sciences
文摘We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.
基金the National Natural Science Foundation of China(12175170,11675066)。
文摘This study proposes an optimized method for estimating atomic nucleus masses by combining the finiterangedroplet model (FRDM) with the support vector machine algorithm. The optimization process significantly improvesthe accuracy of the FRDM by reducing the root mean square error from 0.606 to 0.253 MeV. The optimizedmass data obtained from this method are then used to calculate the evaporation residue cross-sections (ERCSs) forfusion-evaporation reactions, employing the di-nuclear system model. The experimental results for the 48Ca+238U reactionare relatively well reproduced using these optimized mass data. Additionally, the study investigates the impactof mass uncertainties on fusion and survival probabilities. By considering the mass uncertainties, the ERCSs fornew elements 119 and 120 are predicted based on the obtained optimized mass data.
