Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems,...Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems, kernel discriminant analysis for face recognition is presented. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are given.展开更多
Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust min...Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.展开更多
A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate it...A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.展开更多
Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on ker...Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis(kernel GDA,KGDA)was proposed.Through KGDA,the data were mapped from the original space to the high-dimensional feature space.Then the statistic distance between normal data and test data was constructed to detect whether a fault was occurring.If a fault had occurred,similar analysis was used to identify the type of faults.The effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.展开更多
Free spanning pipelines are suspended between two points on an uneven seaffoor. The variations of structural conditions, such as the changes in soil property, flow velocity, axial force and span length etc., directly ...Free spanning pipelines are suspended between two points on an uneven seaffoor. The variations of structural conditions, such as the changes in soil property, flow velocity, axial force and span length etc., directly affect working performance of the whole submarine pipeline system. But until now few researches have focused on condition identification for free span （CIFS）. A method to identify the operational conditions of free spanning submarine pipelines based on vibration measurements is proposed in this paper. Firstly, the ill-posedness of CIFS is analyzed in detail. Secondly, the framework for CIFS based on the nonlinear kernel discriminant analysis （KDA） is established. Thirdly, the internal structural characteristics of natural frequencies, normalized frequencies and frequency change ratios are studied. And then the condition feature vector for CIFS is extracted by use of the vibration measurements. Finally, the validity of the proposed approach is evaluated by a case study. The results demonstrate that the proposed approach can effectively identify each condition of free span when condition variation occurs even if under measurement noise. It is concluded that the proposed method is a promising tool for CIFS in real applications.展开更多
An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for fre...An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.展开更多
Data-driven process monitoring methods are widely used in industrial tasks,with visual monitoring enabling operators to intuitively understand operational status,which is vital for maximizing industrial safety and pro...Data-driven process monitoring methods are widely used in industrial tasks,with visual monitoring enabling operators to intuitively understand operational status,which is vital for maximizing industrial safety and production efficiency.However,high-dimensional industrial data often exhibit complex structures,making the traditional 2D visualization methods ineffective at distinguishing different fault types.Thus,a visual process monitoring method that combines supervised uniform manifold approximation and projection with a label assignment strategy is proposed herein.First,the proposed supervised projection method enhances the visualization step by incorporating label information to guide the nonlinear dimensionality reduction process,improving the degrees of class separation and intraclass compactness.Then,to address the lack of label information for online samples,a label assignment strategy is designed.This strategy integrates kernel Fisher discriminant analysis and Bayesian inference,assigning different label types to online samples based on their confidence levels.Finally,upon integrating the label assignment strategy with the proposed supervised projection method,the assigned labels enhance the separability of online projections and enable the visualization of unknown data to some extent.The proposed method is validated on the Tennessee Eastman process and a real continuous catalytic reforming process,demonstrating superior visual fault monitoring and diagnosis performance to that of the state-of-the-art methods,especially in real industrial applications.展开更多
To improve the classification accuracy and reduce the training time, an intrusion detection technology is proposed, which combines feature extraction technology and multiclass support vector machine （SVM） classifica...To improve the classification accuracy and reduce the training time, an intrusion detection technology is proposed, which combines feature extraction technology and multiclass support vector machine （SVM） classification algorithm. The intrusion detection model setup has two phases. The first phase is to project the original training data into kernel fisher discriminant analysis （KFDA） space. The second phase is to use fuzzy clustering technology to cluster the projected data and construct the decision tree, based on the clustering results. The overall detection model is set up based on the decision tree. Results of the experiment using knowledge discovery and data mining （KDD） from 99 datasets demonstrate that the proposed technology can be an an effective way for intrusion detection.展开更多
文摘Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems, kernel discriminant analysis for face recognition is presented. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are given.
基金Project(51874353)supported by the National Natural Science Foundation of ChinaProject(GCX20190898Y)supported by Mittal Student Innovation Project,China。
文摘Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.
文摘A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.
基金National Natural Science Foundation of China(No.60504033)
文摘Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis(kernel GDA,KGDA)was proposed.Through KGDA,the data were mapped from the original space to the high-dimensional feature space.Then the statistic distance between normal data and test data was constructed to detect whether a fault was occurring.If a fault had occurred,similar analysis was used to identify the type of faults.The effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.
基金supported by the Key Program of National Natural Science Foundation of China(GrantNo.50439010)the Main Program of the Ministry of Education of China(Grant No.305003)
文摘Free spanning pipelines are suspended between two points on an uneven seaffoor. The variations of structural conditions, such as the changes in soil property, flow velocity, axial force and span length etc., directly affect working performance of the whole submarine pipeline system. But until now few researches have focused on condition identification for free span （CIFS）. A method to identify the operational conditions of free spanning submarine pipelines based on vibration measurements is proposed in this paper. Firstly, the ill-posedness of CIFS is analyzed in detail. Secondly, the framework for CIFS based on the nonlinear kernel discriminant analysis （KDA） is established. Thirdly, the internal structural characteristics of natural frequencies, normalized frequencies and frequency change ratios are studied. And then the condition feature vector for CIFS is extracted by use of the vibration measurements. Finally, the validity of the proposed approach is evaluated by a case study. The results demonstrate that the proposed approach can effectively identify each condition of free span when condition variation occurs even if under measurement noise. It is concluded that the proposed method is a promising tool for CIFS in real applications.
基金the National Natural Science Foundation of China(Nos.61702395 and 61972302)the Science and Technology Projects of Xi’an,China(No.201809170CX11JC12)。
文摘An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.
基金supported by the National Key Research and Development Program of China(2022YFB3304701)the National Natural Science Foundation of China(62173145,62322303,62303186)+2 种基金the Natural Science Foundation of Shanghai under Grant 24ZR1415900the Shanghai Pilot Program for Basic Research(22TQ1400100-16)the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26).
文摘Data-driven process monitoring methods are widely used in industrial tasks,with visual monitoring enabling operators to intuitively understand operational status,which is vital for maximizing industrial safety and production efficiency.However,high-dimensional industrial data often exhibit complex structures,making the traditional 2D visualization methods ineffective at distinguishing different fault types.Thus,a visual process monitoring method that combines supervised uniform manifold approximation and projection with a label assignment strategy is proposed herein.First,the proposed supervised projection method enhances the visualization step by incorporating label information to guide the nonlinear dimensionality reduction process,improving the degrees of class separation and intraclass compactness.Then,to address the lack of label information for online samples,a label assignment strategy is designed.This strategy integrates kernel Fisher discriminant analysis and Bayesian inference,assigning different label types to online samples based on their confidence levels.Finally,upon integrating the label assignment strategy with the proposed supervised projection method,the assigned labels enhance the separability of online projections and enable the visualization of unknown data to some extent.The proposed method is validated on the Tennessee Eastman process and a real continuous catalytic reforming process,demonstrating superior visual fault monitoring and diagnosis performance to that of the state-of-the-art methods,especially in real industrial applications.
基金the National Natural Science Foundation of China（60772109）.
文摘To improve the classification accuracy and reduce the training time, an intrusion detection technology is proposed, which combines feature extraction technology and multiclass support vector machine （SVM） classification algorithm. The intrusion detection model setup has two phases. The first phase is to project the original training data into kernel fisher discriminant analysis （KFDA） space. The second phase is to use fuzzy clustering technology to cluster the projected data and construct the decision tree, based on the clustering results. The overall detection model is set up based on the decision tree. Results of the experiment using knowledge discovery and data mining （KDD） from 99 datasets demonstrate that the proposed technology can be an an effective way for intrusion detection.
