Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the m...Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes' sequence in the graph. DAG is equivalent to guided operating SVM on a list,and the classification performance depends on the nodes' sequence in the graph. Jeffries-Matusita distance( JMD) is introduced to estimate the separability of each class,and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method,numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile,comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the proposed JMD-DAG method.展开更多
In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based ...In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.展开更多
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN...Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.展开更多
Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for ...Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.展开更多
This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The sche...This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation,and also avoids the sophisticated process of threshold value selection.The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM.The output of the SVM determines the fault type.A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC.Simulation results of EMTDC confirm that the proposed scheme achieves 100%accuracy in detecting short-circuit faults with high resistance on long transmission lines.The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults.Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme,thus ensuring its reliability and stability.展开更多
The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the l...The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.展开更多
To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited pene...To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.展开更多
Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyc...Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.展开更多
Classification problem is the central problem in machine learning.Support vector machines(SVMs)are supervised learning models with associated learning algorithms and are used for classification in machine learning.In ...Classification problem is the central problem in machine learning.Support vector machines(SVMs)are supervised learning models with associated learning algorithms and are used for classification in machine learning.In this paper,we establish two consensus proximal support vector machines(PSVMs)models,based on methods for binary classification.The first one is to separate the objective functions into individual convex functions by using the number of the sample points of the training set.The constraints contain two types of the equations with global variables and local variables corresponding to the consensus points and sample points,respectively.To get more sparse solutions,the second one is l1–l2 consensus PSVMs in which the objective function contains an■1-norm term and an■2-norm term which is responsible for the good classification performance while■1-norm term plays an important role in finding the sparse solutions.Two consensus PSVMs are solved by the alternating direction method of multipliers.Furthermore,they are implemented by the real-world data taken from the University of California,Irvine Machine Learning Repository(UCI Repository)and are compared with the existed models such as■1-PSVM,■p-PSVM,GEPSVM,PSVM,and SVM-light.Numerical results show that our models outperform others with the classification accuracy and the sparse solutions.展开更多
In this paper, we propose a novel Residuals-Based Deep Least Squares Support Vector Machine(RBDLSSVM). In the RBD-LSSVM, multiple LSSVMs are sequentially connected. The second LSSVM uses the fitting residuals of the f...In this paper, we propose a novel Residuals-Based Deep Least Squares Support Vector Machine(RBDLSSVM). In the RBD-LSSVM, multiple LSSVMs are sequentially connected. The second LSSVM uses the fitting residuals of the first LSSVM as input time series, and the third LSSVM trains the residuals of the second, and so on. The original time series is the input of the first LSSVM. Additionally, to obtain the best hyper-parameters for the RBD-LSSVM, we propose a model validation method based on redundancy test using Omni-Directional Correlation Function(ODCF). This method is based on the fact when a model is appropriate for a given time series, there should be no information or correlation in the residuals. We propose the use of ODCF as a statistic to detect nonlinear correlation between two random variables. Thus, we can select hyper-parameters without encountering overfitting,which cannot be avoided by only cross validation using the validation set. We conducted experiments on two time series: annual sunspot number series and monthly Total Column Ozone(TCO) series in New Delhi. Analysis of the prediction results and comparisons with recent and past studies demonstrate the promising performance of the proposed RBD-LSSVM approach with redundancy test based model selection method for modeling and predicting nonlinear time series.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61201310)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201160)the China Postdoctoral Science Foundation(Grant No.20110491067)
文摘Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes' sequence in the graph. DAG is equivalent to guided operating SVM on a list,and the classification performance depends on the nodes' sequence in the graph. Jeffries-Matusita distance( JMD) is introduced to estimate the separability of each class,and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method,numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile,comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the proposed JMD-DAG method.
基金Sponsored by the National Natural Science Foundation of China ( Grant No 50576033)
文摘In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.
基金National Natural Science Foundation of China(Nos.11262014,11962021 and 51965051)Inner Mongolia Natural Science Foundation,China(No.2019MS05064)+1 种基金Inner Mongolia Earthquake Administration Director Fund Project,China(No.2019YB06)Inner Mongolia University of Technology Foundation,China(No.2020015)。
文摘Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.
基金TheNationalHighTechnologyResearchandDevelopmentProgramofChina (No .86 3 5 11 930 0 0 9)
文摘Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
文摘This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation,and also avoids the sophisticated process of threshold value selection.The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM.The output of the SVM determines the fault type.A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC.Simulation results of EMTDC confirm that the proposed scheme achieves 100%accuracy in detecting short-circuit faults with high resistance on long transmission lines.The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults.Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme,thus ensuring its reliability and stability.
基金This work was supported by the National Defence Pre-research Foundation of China(30502010103).
文摘The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.
基金supported by the China Postdoctoral Science Foundation(2015M572694,2016T90979).
文摘To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.
基金supported by the National Natural Science Foundation of China(61571063,61202399,61171051)
文摘Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.
基金This work is supported by the National Natural Science Foundation of China(Grant No.11371242)and the“085 Project”in Shanghai University.
文摘Classification problem is the central problem in machine learning.Support vector machines(SVMs)are supervised learning models with associated learning algorithms and are used for classification in machine learning.In this paper,we establish two consensus proximal support vector machines(PSVMs)models,based on methods for binary classification.The first one is to separate the objective functions into individual convex functions by using the number of the sample points of the training set.The constraints contain two types of the equations with global variables and local variables corresponding to the consensus points and sample points,respectively.To get more sparse solutions,the second one is l1–l2 consensus PSVMs in which the objective function contains an■1-norm term and an■2-norm term which is responsible for the good classification performance while■1-norm term plays an important role in finding the sparse solutions.Two consensus PSVMs are solved by the alternating direction method of multipliers.Furthermore,they are implemented by the real-world data taken from the University of California,Irvine Machine Learning Repository(UCI Repository)and are compared with the existed models such as■1-PSVM,■p-PSVM,GEPSVM,PSVM,and SVM-light.Numerical results show that our models outperform others with the classification accuracy and the sparse solutions.
文摘In this paper, we propose a novel Residuals-Based Deep Least Squares Support Vector Machine(RBDLSSVM). In the RBD-LSSVM, multiple LSSVMs are sequentially connected. The second LSSVM uses the fitting residuals of the first LSSVM as input time series, and the third LSSVM trains the residuals of the second, and so on. The original time series is the input of the first LSSVM. Additionally, to obtain the best hyper-parameters for the RBD-LSSVM, we propose a model validation method based on redundancy test using Omni-Directional Correlation Function(ODCF). This method is based on the fact when a model is appropriate for a given time series, there should be no information or correlation in the residuals. We propose the use of ODCF as a statistic to detect nonlinear correlation between two random variables. Thus, we can select hyper-parameters without encountering overfitting,which cannot be avoided by only cross validation using the validation set. We conducted experiments on two time series: annual sunspot number series and monthly Total Column Ozone(TCO) series in New Delhi. Analysis of the prediction results and comparisons with recent and past studies demonstrate the promising performance of the proposed RBD-LSSVM approach with redundancy test based model selection method for modeling and predicting nonlinear time series.
