Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and exter...Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and external disturbance make the resolution of image still limited in a certain level.The goal of this paper is to use a single image super-resolution(SISR) method to predict a high-resolution(HR) MODIS image from a single low-resolution(LR) input.Recently,although the method based on sparse representation has tackled the ill-posed problem effectively,two fatal issues have been ignored.First,many methods ignore the relationships among patches,resulting in some unfaithful output.Second,the high computational complexity of sparse coding using l_1 norm is needed in reconstruction stage.In this work,we discover the semantic relationships among LR patches and the corresponding HR patches and group the documents with similar semantic into topics by probabilistic Latent Semantic Analysis(p LSA).Then,we can learn dual dictionaries for each topic in the low-resolution(LR) patch space and high-resolution(HR) patch space and also pre-compute corresponding regression matrices for dictionary pairs.Finally,for the test image,we infer locally which topic it corresponds to and adaptive to select the regression matrix to reconstruct HR image by semantic relationships.Our method discovered the relationships among patches and pre-computed the regression matrices for topics.Therefore,our method can greatly reduce the artifacts and get some speed-up in the reconstruction phase.Experiment manifests that our method performs MODIS image super-resolution effectively,results in higher PSNR,reconstructs faster,and gets better visual quality than some current state-of-art methods.展开更多
Support vector machine(SVM)is easily affected by noises and outliers,and its training time dramatically increases with the growing in number of training samples.Satellite cloud image may easily be deteriorated by nois...Support vector machine(SVM)is easily affected by noises and outliers,and its training time dramatically increases with the growing in number of training samples.Satellite cloud image may easily be deteriorated by noises and intensity non-uniformity with a huge amount of data needs to be processed regularly,so it is hard to detect convective clouds in satellite image using traditional SVM.To deal with this problem,a novel method for detection of convective clouds was proposed based on fast fuzzy support vector machine(FFSVM).FFSVM was constructed by eliminating feeble samples and designing new membership function as two aspects.Firstly,according to the distribution characteristics of fuzzy inseparable sample set and the fact that the classification hyper-plane is only determined by support vectors,this paper uses SVDD,Gaussian model and border vector extraction model comprehensively to design a sample selection method in three steps,which can eliminate most of redundant samples and keep possible support vectors.Then,by defining adaptive parameters related to attenuation rate and critical membership on the basis of the distribution characteristics of training set,an adaptive membership function is designed.Finally,the FFSVM is trained by the remaining samples using adaptive membership function to detect convective clouds.The experiments on FY-2D satellite images show that the proposed method,compared with traditional FSVM,not only remarkably reduces training time,but also further improves the accuracy of convective clouds detection.展开更多
In the applications of joint control and robot movement,the joint torque estimation has been treated as an effective technique and widely used.Researches are made to analyze the kinematic and compliance model of the r...In the applications of joint control and robot movement,the joint torque estimation has been treated as an effective technique and widely used.Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output.Through analyzing the structures of the harmonic drive and experiment apparatus,a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter(UKF)is designed and built.Based on research and scheme,torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique.Finally,a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed,and simulation results compared with the measurements of a commercial torque sensor,have verified the effectiveness of the proposed method.展开更多
基金partially supported by the National Natural Science Foundation of China (61471212)Natural Science Foundation of Zhejiang Province (LY16F010001)Natural Science Foundation of Ningbo (2016A610091, 2017A610297)
文摘Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and external disturbance make the resolution of image still limited in a certain level.The goal of this paper is to use a single image super-resolution(SISR) method to predict a high-resolution(HR) MODIS image from a single low-resolution(LR) input.Recently,although the method based on sparse representation has tackled the ill-posed problem effectively,two fatal issues have been ignored.First,many methods ignore the relationships among patches,resulting in some unfaithful output.Second,the high computational complexity of sparse coding using l_1 norm is needed in reconstruction stage.In this work,we discover the semantic relationships among LR patches and the corresponding HR patches and group the documents with similar semantic into topics by probabilistic Latent Semantic Analysis(p LSA).Then,we can learn dual dictionaries for each topic in the low-resolution(LR) patch space and high-resolution(HR) patch space and also pre-compute corresponding regression matrices for dictionary pairs.Finally,for the test image,we infer locally which topic it corresponds to and adaptive to select the regression matrix to reconstruct HR image by semantic relationships.Our method discovered the relationships among patches and pre-computed the regression matrices for topics.Therefore,our method can greatly reduce the artifacts and get some speed-up in the reconstruction phase.Experiment manifests that our method performs MODIS image super-resolution effectively,results in higher PSNR,reconstructs faster,and gets better visual quality than some current state-of-art methods.
基金supported in part by the National Natural Science Foundation of China under Grants (61471212)Natural Science Foundation of Zhejiang Province under Grants (LY16F010001)+1 种基金Science and Technology Program of Zhejiang Meteorological Bureau under Grants (2016YB01)Natural Science Foundation of Ningbo under Grants(2016A610091,2017A610297)
文摘Support vector machine(SVM)is easily affected by noises and outliers,and its training time dramatically increases with the growing in number of training samples.Satellite cloud image may easily be deteriorated by noises and intensity non-uniformity with a huge amount of data needs to be processed regularly,so it is hard to detect convective clouds in satellite image using traditional SVM.To deal with this problem,a novel method for detection of convective clouds was proposed based on fast fuzzy support vector machine(FFSVM).FFSVM was constructed by eliminating feeble samples and designing new membership function as two aspects.Firstly,according to the distribution characteristics of fuzzy inseparable sample set and the fact that the classification hyper-plane is only determined by support vectors,this paper uses SVDD,Gaussian model and border vector extraction model comprehensively to design a sample selection method in three steps,which can eliminate most of redundant samples and keep possible support vectors.Then,by defining adaptive parameters related to attenuation rate and critical membership on the basis of the distribution characteristics of training set,an adaptive membership function is designed.Finally,the FFSVM is trained by the remaining samples using adaptive membership function to detect convective clouds.The experiments on FY-2D satellite images show that the proposed method,compared with traditional FSVM,not only remarkably reduces training time,but also further improves the accuracy of convective clouds detection.
基金supported by the National Natural Science Foundation of China(51879055)。
文摘In the applications of joint control and robot movement,the joint torque estimation has been treated as an effective technique and widely used.Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output.Through analyzing the structures of the harmonic drive and experiment apparatus,a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter(UKF)is designed and built.Based on research and scheme,torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique.Finally,a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed,and simulation results compared with the measurements of a commercial torque sensor,have verified the effectiveness of the proposed method.
