Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance de...Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.展开更多
A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric ...A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.展开更多
In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the researc...In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.展开更多
Dimensionality reduction （DR） methods based on sparse representation as one of the hottest research topics have achieved remarkable performance in many applications in recent years. However, it＇s a challenge for ex...Dimensionality reduction （DR） methods based on sparse representation as one of the hottest research topics have achieved remarkable performance in many applications in recent years. However, it＇s a challenge for existing sparse representation based methods to solve nonlinear problem due to the limitations of seeking sparse representation of data in the original space. Motivated by kernel tricks, we proposed a new framework called empirical kernel sparse representation （EKSR） to solve nonlinear problem. In this framework, non- linear separable data are mapped into kernel space in which the nonlinear similarity can be captured, and then the data in kernel space is reconstructed by sparse representation to preserve the sparse structure, which is obtained by minimiz- ing a ~1 regularization-related objective function. EKSR pro- vides new insights into dimensionality reduction and extends two models： 1） empirical kernel sparsity preserving projec- tion （EKSPP）, which is a feature extraction method based on sparsity preserving projection （SPP）; 2） empirical kernel sparsity score （EKSS）, which is a feature selection method based on sparsity score （SS）. Both of the two methods can choose neighborhood automatically as the natural discrimi- native power of sparse representation. Compared with sev- eral existing approaches, the proposed framework can reduce computational complexity and be more convenient in prac- tice.展开更多
Sodium diethyldithiocarbamate (DDTC-Na) was demonstrated to be a new colorimetric cyanide chemosensor by utilizing an indirect trick. First, some copper ions were added to the colorless aque- ous solution of DDTC-Na. ...Sodium diethyldithiocarbamate (DDTC-Na) was demonstrated to be a new colorimetric cyanide chemosensor by utilizing an indirect trick. First, some copper ions were added to the colorless aque- ous solution of DDTC-Na. Then, the resultant brown solution was studied upon the addition of different anions, including Cl-, I-, IO3-, SO42-, NO-2, Br-, H2PO4-, F-, SCN-, HSO-4, ClO-4 and CN-. It was observed by naked eyes that the brown solution changed to colorless immediately after the addition of the trace cyanide, but there were no changes towards other anions, making DDTC-Na a good selective cyanide chemosensor in pure water.展开更多
Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. Ho...Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.展开更多
The notion of quasi-biorthogonal frame wavelets is a generalization of the notion of orthog- onal wavelets. A quasi-biorthogonal frame wavelet with the cardinality r consists of r pairs of functions. In this paper we ...The notion of quasi-biorthogonal frame wavelets is a generalization of the notion of orthog- onal wavelets. A quasi-biorthogonal frame wavelet with the cardinality r consists of r pairs of functions. In this paper we first analyze the local property of the quasi-biorthogonal frame wavelet and show that its each pair of functions generates reconstruction formulas of the corresponding subspaces. Next we show that the lower bound of its cardinalities depends on a pair of dual frame multiresolution analyses deriving it. Finally, we present a split trick and show that any quasi-biorthogonal frame wavelet can be split into a new quasi-biorthogonal frame wavelet with an arbitrarily large cardinality. For generality, we work in the setting of matrix dilations.展开更多
Some results indicate that quantum information based on quantum physics is more powerful than classical one. In this paper, we propose new tricks based on quantum physics. Our tricks are methods inspired by the strate...Some results indicate that quantum information based on quantum physics is more powerful than classical one. In this paper, we propose new tricks based on quantum physics. Our tricks are methods inspired by the strategies of quantum game theory. In these tricks, magicians have the ability of quantum physics, but spectators have only classical one. We propose quantum tricks such that, by manipulating quantum coins and quantum cards, magicians guess spectators’ values.展开更多
The thermodynamics and the phase diagram of random field Ising model （RFIM） on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field （B）. A Gaussian distribution ...The thermodynamics and the phase diagram of random field Ising model （RFIM） on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field （B）. A Gaussian distribution of random field （hi） with zero mean and variance （hi2 = H2RF is considered. The free-energy （F）, the magnetization （M） and the order parameter （q） are investigated for several values of coordination number （z）. The phase diagram shows several interesting behaviours and presents tricritical point at critical temperature Tc = J/k and when HRF = 0 for finite z. The free-energy （F） values increase as T increases for different intensities of random field （HRE） and finite z. The internal energy （U） has a similar behaviour to that obtained from the Monte Carlo simulations. The ground state of magnetization decreases as the intensity of random field HRF increases, The ferromagnetic （FM） paramagnetic （PM） phase boundary is clearly observed only when z →∞. While FM PM-spin glass （SG） phase boundaries are present for finite z. The magnetic susceptibility （X） shows a sharp cusp at Tc in a small random field for finite z and rounded different peaks on increasing HRF.展开更多
基金Supported by the National Natural Science Foundation of China （61273160）, the Natural Science Foundation of Shandong Province of China （ZR2011FM014） and the Fundamental Research Funds for the Central Universities （10CX04046A）.
文摘Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.
基金National Hi-tech Research Development Program of China(863 Program,No.2007AA04Z421)National Natural Science Foundation of China(No.50475078,No.50775035)
文摘A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.
基金supported by the National Natural Science Foundation of China(5110505261173163)the Liaoning Provincial Natural Science Foundation of China(201102037)
文摘In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.
文摘Dimensionality reduction （DR） methods based on sparse representation as one of the hottest research topics have achieved remarkable performance in many applications in recent years. However, it＇s a challenge for existing sparse representation based methods to solve nonlinear problem due to the limitations of seeking sparse representation of data in the original space. Motivated by kernel tricks, we proposed a new framework called empirical kernel sparse representation （EKSR） to solve nonlinear problem. In this framework, non- linear separable data are mapped into kernel space in which the nonlinear similarity can be captured, and then the data in kernel space is reconstructed by sparse representation to preserve the sparse structure, which is obtained by minimiz- ing a ~1 regularization-related objective function. EKSR pro- vides new insights into dimensionality reduction and extends two models： 1） empirical kernel sparsity preserving projec- tion （EKSPP）, which is a feature extraction method based on sparsity preserving projection （SPP）; 2） empirical kernel sparsity score （EKSS）, which is a feature selection method based on sparsity score （SS）. Both of the two methods can choose neighborhood automatically as the natural discrimi- native power of sparse representation. Compared with sev- eral existing approaches, the proposed framework can reduce computational complexity and be more convenient in prac- tice.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 20674059 & 20402011)
文摘Sodium diethyldithiocarbamate (DDTC-Na) was demonstrated to be a new colorimetric cyanide chemosensor by utilizing an indirect trick. First, some copper ions were added to the colorless aque- ous solution of DDTC-Na. Then, the resultant brown solution was studied upon the addition of different anions, including Cl-, I-, IO3-, SO42-, NO-2, Br-, H2PO4-, F-, SCN-, HSO-4, ClO-4 and CN-. It was observed by naked eyes that the brown solution changed to colorless immediately after the addition of the trace cyanide, but there were no changes towards other anions, making DDTC-Na a good selective cyanide chemosensor in pure water.
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province of China(ZR2011FM014)+1 种基金the Doctoral Fund of Shandong Province(BS2012ZZ011)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.
文摘The notion of quasi-biorthogonal frame wavelets is a generalization of the notion of orthog- onal wavelets. A quasi-biorthogonal frame wavelet with the cardinality r consists of r pairs of functions. In this paper we first analyze the local property of the quasi-biorthogonal frame wavelet and show that its each pair of functions generates reconstruction formulas of the corresponding subspaces. Next we show that the lower bound of its cardinalities depends on a pair of dual frame multiresolution analyses deriving it. Finally, we present a split trick and show that any quasi-biorthogonal frame wavelet can be split into a new quasi-biorthogonal frame wavelet with an arbitrarily large cardinality. For generality, we work in the setting of matrix dilations.
文摘Some results indicate that quantum information based on quantum physics is more powerful than classical one. In this paper, we propose new tricks based on quantum physics. Our tricks are methods inspired by the strategies of quantum game theory. In these tricks, magicians have the ability of quantum physics, but spectators have only classical one. We propose quantum tricks such that, by manipulating quantum coins and quantum cards, magicians guess spectators’ values.
文摘The thermodynamics and the phase diagram of random field Ising model （RFIM） on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field （B）. A Gaussian distribution of random field （hi） with zero mean and variance （hi2 = H2RF is considered. The free-energy （F）, the magnetization （M） and the order parameter （q） are investigated for several values of coordination number （z）. The phase diagram shows several interesting behaviours and presents tricritical point at critical temperature Tc = J/k and when HRF = 0 for finite z. The free-energy （F） values increase as T increases for different intensities of random field （HRE） and finite z. The internal energy （U） has a similar behaviour to that obtained from the Monte Carlo simulations. The ground state of magnetization decreases as the intensity of random field HRF increases, The ferromagnetic （FM） paramagnetic （PM） phase boundary is clearly observed only when z →∞. While FM PM-spin glass （SG） phase boundaries are present for finite z. The magnetic susceptibility （X） shows a sharp cusp at Tc in a small random field for finite z and rounded different peaks on increasing HRF.
