Based on vectorial Debye theory, tight focusing of radially and azimuthally polarized vortex beams passing through a dielectric interface are studied. The intensity distribution in the focal region is illustrated by n...Based on vectorial Debye theory, tight focusing of radially and azimuthally polarized vortex beams passing through a dielectric interface are studied. The intensity distribution in the focal region is illustrated by numerical calculations. We show the influence of numerical-aperture （NA） on the full-width at half maximum （FWHM） of the focal spot or the focal hole. It has been found that compared with the azimuthally polarized Besse^Gaussian （BG） beams, the longitudinal component in the z direction of the radially polarized BG beams has no influence on the FWHM of the focal spot and hole, but enhances the total light intensity.展开更多
Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis t...Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.展开更多
In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single tria...In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.展开更多
In order to restore the degraded ultrasonic C-scan image for testing surfacing inteoface, a method based on support vector regression （SVR） network is proposed. By using the image of a simulating defect, the network...In order to restore the degraded ultrasonic C-scan image for testing surfacing inteoface, a method based on support vector regression （SVR） network is proposed. By using the image of a simulating defect, the network is trained and a mapping relationship between the degraded and restored image is founded. The degraded C-scan image of Cu-Steel surfacing inteoface is processed by the trained network and improved image is obtained. The result shows that the method can effectively suppress the noise and deblur the defect edge in the image, and provide technique support for quality and reliability evaluation of the surfacing weld.展开更多
An analytical solution of the guided wave propagation in a multilayered twodimensional decagonal quasicrystal plate with imperfect interfaces is derived.According to the elastodynamic equations of quasicrystals(QCs),t...An analytical solution of the guided wave propagation in a multilayered twodimensional decagonal quasicrystal plate with imperfect interfaces is derived.According to the elastodynamic equations of quasicrystals(QCs),the wave propagating problem in the plate is converted into a linear control system by employing the state-vector approach,from which the general solutions of the extended displacements and stresses can be obtained,These solutions along the thickness direction are utilized to derive the propagator matrix which connects the physical variables on the lower and upper interfaces of each layer.The special spring model,which describes the discontinuity of the physical quantities across the interface,is introduced into the propagator relationship of the multilayered structure.The total propagator matrix can be used to propagate the solutions in each interface and each layer about the multilayered plate.In addition,the traction-free boundary condition on the top and bottom surfaces of the laminate is considered to obtain the dispersion equation of wave propagation,Finally,typical numerical examples are presented to illustrate the marked influences of stacking sequence and interface coeficients on the dispersion curves and displacement mode shapes of the QC laminates.展开更多
User adaptation is a critical and important problem. For users' specialization, such as Handwriting, Voice,Drawing Styles, the system is hard to adapt to all users. SVM-based incremental learning can find the most...User adaptation is a critical and important problem. For users' specialization, such as Handwriting, Voice,Drawing Styles, the system is hard to adapt to all users. SVM-based incremental learning can find the most basic fea-ture of different users and cast away the special user's character, so this method can adapt the different users withoutover fitting. In this paper, the repetitive learning strategy and other two incremental learning algorithms are presentedfor comparison. Based on theoretical analysis and experimental results, we draw the conclusion that SVM-based incre-mental learning can solve the user conflict problem.展开更多
Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery （MI） brain-computer interface （BCI） system. In this pa...Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery （MI） brain-computer interface （BCI） system. In this paper, we propose a common spatial pattern （CSP） and chaotic particle swarm optimization （CPSO） twin support vector machine （TWSVM） scheme for classification of MI electroencephalography （EEG）. The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine （SVM）, particle swarm optimization （PSO） TWSVM, linear discriminant analysis （LDA）, back propagation （BP） and probabilistic neural network （PNN）, respectively. Furthermore, it achieved a faster or comparable central processing unit （CPU） running time over the traditional SVM methods.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No 60477041, the Key Project of Science and Technology of Fujian Province under Grant No 2007H0027, and the Foundation of Science and Technology Development of Southwest Jiaotong University of China under Grant No 2006B01.
文摘Based on vectorial Debye theory, tight focusing of radially and azimuthally polarized vortex beams passing through a dielectric interface are studied. The intensity distribution in the focal region is illustrated by numerical calculations. We show the influence of numerical-aperture （NA） on the full-width at half maximum （FWHM） of the focal spot or the focal hole. It has been found that compared with the azimuthally polarized Besse^Gaussian （BG） beams, the longitudinal component in the z direction of the radially polarized BG beams has no influence on the FWHM of the focal spot and hole, but enhances the total light intensity.
基金This work was supported by Taif University Researchers Supporting Project(TURSP)under number(TURSP-2020/73),Taif University,Taif,Saudi Arabia.
文摘Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.
基金Natural Science Foundation of Shandong Provincegrant number:Y2007G31
文摘In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.
文摘In order to restore the degraded ultrasonic C-scan image for testing surfacing inteoface, a method based on support vector regression （SVR） network is proposed. By using the image of a simulating defect, the network is trained and a mapping relationship between the degraded and restored image is founded. The degraded C-scan image of Cu-Steel surfacing inteoface is processed by the trained network and improved image is obtained. The result shows that the method can effectively suppress the noise and deblur the defect edge in the image, and provide technique support for quality and reliability evaluation of the surfacing weld.
基金supported by the National Natural Science Foundation of China(Grant Nos.11972365,12102458,and 11972354)China Agricultural University Education Foundation(No.1101-2412001).
文摘An analytical solution of the guided wave propagation in a multilayered twodimensional decagonal quasicrystal plate with imperfect interfaces is derived.According to the elastodynamic equations of quasicrystals(QCs),the wave propagating problem in the plate is converted into a linear control system by employing the state-vector approach,from which the general solutions of the extended displacements and stresses can be obtained,These solutions along the thickness direction are utilized to derive the propagator matrix which connects the physical variables on the lower and upper interfaces of each layer.The special spring model,which describes the discontinuity of the physical quantities across the interface,is introduced into the propagator relationship of the multilayered structure.The total propagator matrix can be used to propagate the solutions in each interface and each layer about the multilayered plate.In addition,the traction-free boundary condition on the top and bottom surfaces of the laminate is considered to obtain the dispersion equation of wave propagation,Finally,typical numerical examples are presented to illustrate the marked influences of stacking sequence and interface coeficients on the dispersion curves and displacement mode shapes of the QC laminates.
文摘User adaptation is a critical and important problem. For users' specialization, such as Handwriting, Voice,Drawing Styles, the system is hard to adapt to all users. SVM-based incremental learning can find the most basic fea-ture of different users and cast away the special user's character, so this method can adapt the different users withoutover fitting. In this paper, the repetitive learning strategy and other two incremental learning algorithms are presentedfor comparison. Based on theoretical analysis and experimental results, we draw the conclusion that SVM-based incre-mental learning can solve the user conflict problem.
基金supported by the National Natural Science Foundation of China (61571063)
文摘Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery （MI） brain-computer interface （BCI） system. In this paper, we propose a common spatial pattern （CSP） and chaotic particle swarm optimization （CPSO） twin support vector machine （TWSVM） scheme for classification of MI electroencephalography （EEG）. The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine （SVM）, particle swarm optimization （PSO） TWSVM, linear discriminant analysis （LDA）, back propagation （BP） and probabilistic neural network （PNN）, respectively. Furthermore, it achieved a faster or comparable central processing unit （CPU） running time over the traditional SVM methods.
