One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification ...One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.展开更多
The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli...The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.展开更多
Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonab...Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.展开更多
Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.T...Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.展开更多
The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimina...The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20).展开更多
One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of t...One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.展开更多
Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the...Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.展开更多
In this paper,the authors employ the splitting method to address support vector machine within a reproducing kernel Banach space framework,where a lower semi-continuous loss function is utilized.They translate support...In this paper,the authors employ the splitting method to address support vector machine within a reproducing kernel Banach space framework,where a lower semi-continuous loss function is utilized.They translate support vector machine in reproducing kernel Banach space with such a loss function to a finite-dimensional tensor optimization problem and propose a splitting method based on the alternating direction method of mul-tipliers.Leveraging Kurdyka-Lojasiewicz property of the augmented Lagrangian function,the authors demonstrate that the sequence derived from this splitting method is globally convergent to a stationary point if the loss function is lower semi-continuous and subana-lytic.Through several numerical examples,they illustrate the effectiveness of the proposed splitting algorithm.展开更多
One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative compar...One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative comparisons. The analysis uses a hypersphere version of one-class SVMs with a penalty term appended to the objective function. The method simultaneously finds the minimum sphere in the feature space that encloses most of the target points and considers the relative comparisons. The result is a standard convex quadratic programming problem, which can be solved by adapting standard methods for SVM training, i.e., sequential minimal optimization. This one-class SVM can be applied to semi-supervised clustering and multi-classification problems. Tests show that this method achieves higher accuracy and better generalization performance than previous SVMs.展开更多
Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this...Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are applied.Considering the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data.This model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training time.In the case of existence,the disturbances are further classified into different types such as sag,swell,transients and unbalanced.Being light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring.The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.展开更多
结构磁共振成像(s MRI)本质上具有三维张量结构,而传统的向量空间机器学习方法将其展开成向量进行建模,这破坏了数据的内在结构信息的完整性,降低了机器学习性能。为了克服数据向量化的弊端,提出了一种基于支持张量机(Support tensor ma...结构磁共振成像(s MRI)本质上具有三维张量结构,而传统的向量空间机器学习方法将其展开成向量进行建模,这破坏了数据的内在结构信息的完整性,降低了机器学习性能。为了克服数据向量化的弊端,提出了一种基于支持张量机(Support tensor machine,STM)的以3D T1加权MR脑白质图像为输入的阿尔兹海默症诊断算法。首先用SPM8软件将采集的MRI数据进行预处理,分割为灰质、白质、脑脊液3部分,提取脑白质各体素的灰度值构建三阶灰度张量,然后用递归特征消除(Recursive Feature Elimination,RFE)法结合支持张量机进行特征选择,最后用支持张量机进行分类。在阿尔兹海默症患者(AD),轻度认知障碍患者(MCI)(包括转化为AD的MCI-C和未转化的MCI-NC)以及正常对照(NC)4组人群中进行实验测试,并用10折交叉验证方法获得验证结果。用ROC曲线下面积AUC、分类准确率、敏感性、特异性这4个指标评价分类器的性能,AD vs NC组分别达到99.1%、97.14%、95.71%、98.57%;AD vs MCI组分别达到88.29%、84.07%、78.57%、91.07%;MCI vs NC组分别达到89.18%、87.91%、93.75%、78.57%;MCI-C vs MCI-NC组分别达到87.5%、82.08%、80.36%、82.14%。算法保持了原始图像的张量结构,提高了分类器的性能,实验结果表明此算法是一种有效的阿尔兹海默症诊断方法。展开更多
文摘One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.
基金Supported by the National Natural Science Foundation of China (No. 60771068)the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2007F248)
文摘Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.
基金supported by National Natural Science Foundation of China (Grant No. 50675219)Hu’nan Provincial Science Committee Excellent Youth Foundation of China (Grant No. 08JJ1008)
文摘Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.
基金L’Ore´al-UNESCO for the Women in Science Maghreb Program Grant Agreement No.4500410340.
文摘The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20).
基金Supported by the National Natural Science Foundation of China(60603029)the Natural Science Foundation of Jiangsu Province(BK2007074)the Natural Science Foundation for Colleges and Universities in Jiangsu Province(06KJB520132)~~
文摘One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.
基金funding provided by the Scientific and Technological Research Council of Türkiye(TÜBİTAK).
文摘Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.
基金supported by the National Natural Science Foundation of China(Nos.12026602,12071157,12271108)the Natural Science Foundation of Guangdong Provience(No.2024A1515012288)+1 种基金the Science and Technology Commission of Shanghai Municipality(No.23JC1400501)the Ministry of Science and Technology of China(No.G2023132005L).
文摘In this paper,the authors employ the splitting method to address support vector machine within a reproducing kernel Banach space framework,where a lower semi-continuous loss function is utilized.They translate support vector machine in reproducing kernel Banach space with such a loss function to a finite-dimensional tensor optimization problem and propose a splitting method based on the alternating direction method of mul-tipliers.Leveraging Kurdyka-Lojasiewicz property of the augmented Lagrangian function,the authors demonstrate that the sequence derived from this splitting method is globally convergent to a stationary point if the loss function is lower semi-continuous and subana-lytic.Through several numerical examples,they illustrate the effectiveness of the proposed splitting algorithm.
基金Supported by the National Natural Science Foundation of China(No. 60872070)
文摘One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative comparisons. The analysis uses a hypersphere version of one-class SVMs with a penalty term appended to the objective function. The method simultaneously finds the minimum sphere in the feature space that encloses most of the target points and considers the relative comparisons. The result is a standard convex quadratic programming problem, which can be solved by adapting standard methods for SVM training, i.e., sequential minimal optimization. This one-class SVM can be applied to semi-supervised clustering and multi-classification problems. Tests show that this method achieves higher accuracy and better generalization performance than previous SVMs.
基金supported in part through U.S.National Science Foundation(No.1553494).
文摘Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are applied.Considering the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data.This model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training time.In the case of existence,the disturbances are further classified into different types such as sag,swell,transients and unbalanced.Being light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring.The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.
基金Supponed by the National Natural Science Foundation of China under Grant Nos.6060309660533090(国家自然科学基金)+3 种基金the National High-Tech Research and Development Plan of China under Grant No.2006AA010107(国家高技术研究发展计划(863)the N~ional Key Technology R&D Program 0f China under Grant No.2007BAH11B01(国家科技支撑计划)the Program for Changjiang Scholars and Innovative Research Team in University ofChina under Grant Nos.IRT0652PCSIRT(长江学者和创新团队发展计划)
文摘结构磁共振成像(s MRI)本质上具有三维张量结构,而传统的向量空间机器学习方法将其展开成向量进行建模,这破坏了数据的内在结构信息的完整性,降低了机器学习性能。为了克服数据向量化的弊端,提出了一种基于支持张量机(Support tensor machine,STM)的以3D T1加权MR脑白质图像为输入的阿尔兹海默症诊断算法。首先用SPM8软件将采集的MRI数据进行预处理,分割为灰质、白质、脑脊液3部分,提取脑白质各体素的灰度值构建三阶灰度张量,然后用递归特征消除(Recursive Feature Elimination,RFE)法结合支持张量机进行特征选择,最后用支持张量机进行分类。在阿尔兹海默症患者(AD),轻度认知障碍患者(MCI)(包括转化为AD的MCI-C和未转化的MCI-NC)以及正常对照(NC)4组人群中进行实验测试,并用10折交叉验证方法获得验证结果。用ROC曲线下面积AUC、分类准确率、敏感性、特异性这4个指标评价分类器的性能,AD vs NC组分别达到99.1%、97.14%、95.71%、98.57%;AD vs MCI组分别达到88.29%、84.07%、78.57%、91.07%;MCI vs NC组分别达到89.18%、87.91%、93.75%、78.57%;MCI-C vs MCI-NC组分别达到87.5%、82.08%、80.36%、82.14%。算法保持了原始图像的张量结构,提高了分类器的性能,实验结果表明此算法是一种有效的阿尔兹海默症诊断方法。
