Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decompos...Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.展开更多
CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs a...CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.展开更多
Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smar...Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.展开更多
This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information ...This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.展开更多
The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter ...The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.展开更多
This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorizati...This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.展开更多
Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique i...Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).展开更多
Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of informatio...Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper, we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF. Key words localized non-negative matrix factorization - profile - log mining - mail filtering CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000), National Research Foundation for the Doctoral Program of Higher Education of China (20020286004).Biography: Jiang Ji-xiang (1980-), male, Master candidate, research direction: data mining, knowledge representation on the Web.展开更多
In this paper, solutions to the generalized Sylvester matrix equations AX -XF = BY and MXN -X = TY with A, M ∈ R^n×n, B, T ∈ Rn×r, F, N ∈ R^p×p and the matrices N, F being in companion form, are est...In this paper, solutions to the generalized Sylvester matrix equations AX -XF = BY and MXN -X = TY with A, M ∈ R^n×n, B, T ∈ Rn×r, F, N ∈ R^p×p and the matrices N, F being in companion form, are established by a singular value decomposition of a matrix with dimensions n × （n ＋ pr）. The algorithm proposed in this paper for the euqation AX - XF = BY does not require the controllability of matrix pair （A, B） and the restriction that A, F do not have common eigenvalues. Since singular value decomposition is adopted, the algorithm is numerically stable and may provide great convenience to the computation of the solution to these equations, and can perform important functions in many design problems in control systems theory.展开更多
In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innova...In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.展开更多
Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more...Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.展开更多
We demonstrate that, when computing the LDU decomposition (a typical example of a direct solution method), it is possible to obtain the derivative of a determinant with respect to an eigenvalue of a non-symmetric matr...We demonstrate that, when computing the LDU decomposition (a typical example of a direct solution method), it is possible to obtain the derivative of a determinant with respect to an eigenvalue of a non-symmetric matrix. Our proposed method augments an LDU decomposition program with an additional routine to obtain a program for easily evaluating the derivative of a determinant with respect to an eigenvalue. The proposed method follows simply from the process of solving simultaneous linear equations and is particularly effective for band matrices, for which memory requirements are significantly reduced compared to those for dense matrices. We discuss the theory underlying our proposed method and present detailed algorithms for implementing it.展开更多
Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factor...Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.展开更多
In this paper we derive a practical method of solving simultaneously the problem of Schmidt decomposition of quaternion matrix and the orthonormalization of vectors in a generalized unitary space by using elementary c...In this paper we derive a practical method of solving simultaneously the problem of Schmidt decomposition of quaternion matrix and the orthonormalization of vectors in a generalized unitary space by using elementary column operations on matrices over the quaternion field.展开更多
As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry ...As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry high-speed and real time data,data losses and data quality degradation may happen constantly. For this problem,according to the strong spatial and temporal correlation of electricity data which isgenerated by human’s actions and feelings, we build a low-rank electricity data matrixwhere the row is time and the column is user. Inspired by matrix decomposition, we dividethe low-rank electricity data matrix into the multiply of two small matrices and use theknown data to approximate the low-rank electricity data matrix and recover the missedelectrical data. Based on the real electricity data, we analyze the low-rankness of theelectricity data matrix and perform the Matrix Decomposition-based method on the realdata. The experimental results verify the efficiency and efficiency of the proposed scheme.展开更多
Demineralized bone matrix(DBM)is one of the standard biomaterials used to fill surgical bone defects in general orthopedic procedures.However,current DBM products come in the form of powder or viscous solutions that f...Demineralized bone matrix(DBM)is one of the standard biomaterials used to fill surgical bone defects in general orthopedic procedures.However,current DBM products come in the form of powder or viscous solutions that fail to mimic the natural hierarchical structure of bone while also using large amounts of valuable material.To overcome this,compact fibrous DBM/polymer(fDBM)composites were prepared via electrospinning.Then,by exploiting the catalytic decomposition of hydrogen peroxide,oxygen pockets are formed in the scaffold imparting it with a hierarchical porous structure similar to bone(Op-fDBM).These pockets created by bubbles of oxygen help give the scaffold a mechanically stable shape while the incorporated DBM supports cell adhesion and growth.In vivo evaluations reveal that fDBM increased bone volume by 41.7%while Op-fDBM increased bone volume by 68.6%.Significant increases in regenerated bone volume with the use of minimal amounts of DBM in fiber form go to show the great potential of this work in the field of bone regeneration.展开更多
NaI(T1) scintillation detectors have been widely applied for gamma-ray spectrum measurements owing to advantages such as high detection efficiency and low price.However,the mitigation of the limited energy resolution ...NaI(T1) scintillation detectors have been widely applied for gamma-ray spectrum measurements owing to advantages such as high detection efficiency and low price.However,the mitigation of the limited energy resolution of these detectors,which detracts from an accurate analysis of the instrument spectra obtained,remains a crucial need.Based on the physical properties and spectrum formation processes of NaI(T1) scintillation detectors,the detector response to gamma photons with different energies is represented by photopeaks that are approximately Gaussian in shape with unique full-width-at-half-maximum(FWHM) values.The FWHM is established as a detector parameter based on resolution calibrations and is used in the construction of a general Gaussian response matrix,which is employed for the inverse decomposition of gamma spectra obtained from the detector.The Gold and Boosted Gold iterative algorithms are employed to accelerate the decomposition of the measured spectrum.Tests of the inverse decomposition method on multiple simulated overlapping peaks and on experimentally obtained U and Th radionuclide series spectra verify the practicability of the method,particularly in the low-energy region of the spectrum,providing for the accurate qualitative and quantitative analysis of radionuclides.展开更多
The Na I(Tl) scintillation detector has a number of unique advantages, including wide use, high light yield,and its low price. It is difficult to obtain the decomposition of instrument response spectrum because of lim...The Na I(Tl) scintillation detector has a number of unique advantages, including wide use, high light yield,and its low price. It is difficult to obtain the decomposition of instrument response spectrum because of limitations associated with the Na I(Tl) scintillation detector's energy resolution. This paper, based on the physical process of c photons released from decay nuclides, generating an instrument response spectrum, uses the Monte Carlo method to simulate c photons with Na I(Tl) scintillation detector interaction. The Monte Carlo response matrix is established by different single energy γ-rays with detector effects. The Gold and the improved Boosted-Gold iterative algorithms have also been used in this paper to solve the response matrix parameters through decomposing tests,such as simulating a multi-characteristic energy c-ray spectrum and simulating synthesized overlapping peaks cray spectrum. An inversion decomposition of the c instrument response spectrum for measured samples(U series, Th series and U–Th mixed sources, among others)can be achieved under the response matrix. The decomposing spectrum can be better distinguished between the similar energy characteristic peaks, which improve the error levels of activity analysis caused by the overlapping peak with significant effects.展开更多
The perturbational reanalysis technique of matrix singular value decomposition is applicable to many theoretical and practical problems in mathematics, mechanics, control theory, engineering, etc.. An indirect perturb...The perturbational reanalysis technique of matrix singular value decomposition is applicable to many theoretical and practical problems in mathematics, mechanics, control theory, engineering, etc.. An indirect perturbation method has previously been proposed by the author in this journal, and now the direct perturbation method has also been presented in this paper. The second-order perturbation results of non-repeated singular values and the corresponding left and right singular vectors are obtained. The results can meet the general needs of most problems of various practical applications. A numerical example is presented to demonstrate the effectiveness of the direct perturbation method.展开更多
The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix...The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.展开更多
文摘Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.
基金the Gansu Province Industrial Support Plan(No.2023CYZC-25)Natural Science Foundation of Gansu Province(No.23JRRA770)the National Natural Science Foundation of China(No.62162040)。
文摘CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.
基金Supported by Shaanxi Provincial Overall Innovation Project of Science and Technology,China(Grant No.2013KTCQ01-06)
文摘Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.
基金supported by the National Natural Science Foundation of China(61702251,61363049,11571011)the State Scholarship Fund of China Scholarship Council(CSC)(201708360040)+3 种基金the Natural Science Foundation of Jiangxi Province(20161BAB212033)the Natural Science Basic Research Plan in Shaanxi Province of China(2018JM6030)the Doctor Scientific Research Starting Foundation of Northwest University(338050050)Youth Academic Talent Support Program of Northwest University
文摘This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.
基金financially supported by the Nord-Pas-de-Calais Region Councilthe Ministry of Higher Education and Research+1 种基金the European Regional Development FundsAdib Kfoury acknowledges the“Pole Metropolitain Cote d'Opale”(PMCO)for its PhD financial support
文摘The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.
基金supported by the National Natural Science Foundation of China(61702251,41971424,61701191,U1605254)the Natural Science Basic Research Plan in Shaanxi Province of China(2018JM6030)+4 种基金the Key Technical Project of Fujian Province(2017H6015)the Science and Technology Project of Xiamen(3502Z20183032)the Doctor Scientific Research Starting Foundation of Northwest University(338050050)Youth Academic Talent Support Program of Northwest University(360051900151)the Natural Sciences and Engineering Research Council of Canada,Canada。
文摘This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.
基金Supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry.
文摘Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).
文摘Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper, we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF. Key words localized non-negative matrix factorization - profile - log mining - mail filtering CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000), National Research Foundation for the Doctoral Program of Higher Education of China (20020286004).Biography: Jiang Ji-xiang (1980-), male, Master candidate, research direction: data mining, knowledge representation on the Web.
基金This work was supported by the Chinese Outstanding Youth Foundation(No.69925308)Program for Changjiang Scholars and Innovative ResearchTeam in University.
文摘In this paper, solutions to the generalized Sylvester matrix equations AX -XF = BY and MXN -X = TY with A, M ∈ R^n×n, B, T ∈ Rn×r, F, N ∈ R^p×p and the matrices N, F being in companion form, are established by a singular value decomposition of a matrix with dimensions n × （n ＋ pr）. The algorithm proposed in this paper for the euqation AX - XF = BY does not require the controllability of matrix pair （A, B） and the restriction that A, F do not have common eigenvalues. Since singular value decomposition is adopted, the algorithm is numerically stable and may provide great convenience to the computation of the solution to these equations, and can perform important functions in many design problems in control systems theory.
基金National Natural Science Foundation of China(Nos.41571410,41977067,42171422)。
文摘In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.
基金supported by National High Technology Research and Development Program of China (863 Program) (No.2015AA016306)National Nature Science Foundation of China (No.61231015)National Nature Science Foundation of China (No.61671335)
文摘Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.
文摘We demonstrate that, when computing the LDU decomposition (a typical example of a direct solution method), it is possible to obtain the derivative of a determinant with respect to an eigenvalue of a non-symmetric matrix. Our proposed method augments an LDU decomposition program with an additional routine to obtain a program for easily evaluating the derivative of a determinant with respect to an eigenvalue. The proposed method follows simply from the process of solving simultaneous linear equations and is particularly effective for band matrices, for which memory requirements are significantly reduced compared to those for dense matrices. We discuss the theory underlying our proposed method and present detailed algorithms for implementing it.
基金supported by the National Natural Science Foundation of China (61074131 and 91132722)the Doctoral Fund of the Ministry of Education of China (21101202110007)
文摘Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.
文摘In this paper we derive a practical method of solving simultaneously the problem of Schmidt decomposition of quaternion matrix and the orthonormalization of vectors in a generalized unitary space by using elementary column operations on matrices over the quaternion field.
文摘As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry high-speed and real time data,data losses and data quality degradation may happen constantly. For this problem,according to the strong spatial and temporal correlation of electricity data which isgenerated by human’s actions and feelings, we build a low-rank electricity data matrixwhere the row is time and the column is user. Inspired by matrix decomposition, we dividethe low-rank electricity data matrix into the multiply of two small matrices and use theknown data to approximate the low-rank electricity data matrix and recover the missedelectrical data. Based on the real electricity data, we analyze the low-rankness of theelectricity data matrix and perform the Matrix Decomposition-based method on the realdata. The experimental results verify the efficiency and efficiency of the proposed scheme.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Project No.2016R1C1B2008296)It was also partially supported by the Na-tional Research Foundation of Korea(NRF)grant funded by the Korean Ministry of Education(Nos.NRF-2021R1I1A1A01047751 and RS-2023-00240020).
文摘Demineralized bone matrix(DBM)is one of the standard biomaterials used to fill surgical bone defects in general orthopedic procedures.However,current DBM products come in the form of powder or viscous solutions that fail to mimic the natural hierarchical structure of bone while also using large amounts of valuable material.To overcome this,compact fibrous DBM/polymer(fDBM)composites were prepared via electrospinning.Then,by exploiting the catalytic decomposition of hydrogen peroxide,oxygen pockets are formed in the scaffold imparting it with a hierarchical porous structure similar to bone(Op-fDBM).These pockets created by bubbles of oxygen help give the scaffold a mechanically stable shape while the incorporated DBM supports cell adhesion and growth.In vivo evaluations reveal that fDBM increased bone volume by 41.7%while Op-fDBM increased bone volume by 68.6%.Significant increases in regenerated bone volume with the use of minimal amounts of DBM in fiber form go to show the great potential of this work in the field of bone regeneration.
基金supported by the National Natural Science Foundation of China(Grant No.11365001)National Major Scientific Equipment Development Projects(Grant No.041514065)+2 种基金the Educational Commission of Jiangxi Province of China(Grant No.GJJ13464)Plan of Science and Technology of Jiangxi Province(Grant No.20141BBE50024)the Fundamental Science on Radioactive Geology and Exploration Technology Laboratory,East China Institute of Technology(Grant No.RGET1316)
文摘NaI(T1) scintillation detectors have been widely applied for gamma-ray spectrum measurements owing to advantages such as high detection efficiency and low price.However,the mitigation of the limited energy resolution of these detectors,which detracts from an accurate analysis of the instrument spectra obtained,remains a crucial need.Based on the physical properties and spectrum formation processes of NaI(T1) scintillation detectors,the detector response to gamma photons with different energies is represented by photopeaks that are approximately Gaussian in shape with unique full-width-at-half-maximum(FWHM) values.The FWHM is established as a detector parameter based on resolution calibrations and is used in the construction of a general Gaussian response matrix,which is employed for the inverse decomposition of gamma spectra obtained from the detector.The Gold and Boosted Gold iterative algorithms are employed to accelerate the decomposition of the measured spectrum.Tests of the inverse decomposition method on multiple simulated overlapping peaks and on experimentally obtained U and Th radionuclide series spectra verify the practicability of the method,particularly in the low-energy region of the spectrum,providing for the accurate qualitative and quantitative analysis of radionuclides.
基金supported by National Natural Science Foundation of China(No.11365001)National Major Scientific Equipment Development Projects(No.041514065)+1 种基金Natural Science Foundation of Jiangxi(No.20161BAB201035)Fundamental Science on Radioactive Geology and Exploration Technology Laboratory,East China Institute of Technology(No.RGET1316)
文摘The Na I(Tl) scintillation detector has a number of unique advantages, including wide use, high light yield,and its low price. It is difficult to obtain the decomposition of instrument response spectrum because of limitations associated with the Na I(Tl) scintillation detector's energy resolution. This paper, based on the physical process of c photons released from decay nuclides, generating an instrument response spectrum, uses the Monte Carlo method to simulate c photons with Na I(Tl) scintillation detector interaction. The Monte Carlo response matrix is established by different single energy γ-rays with detector effects. The Gold and the improved Boosted-Gold iterative algorithms have also been used in this paper to solve the response matrix parameters through decomposing tests,such as simulating a multi-characteristic energy c-ray spectrum and simulating synthesized overlapping peaks cray spectrum. An inversion decomposition of the c instrument response spectrum for measured samples(U series, Th series and U–Th mixed sources, among others)can be achieved under the response matrix. The decomposing spectrum can be better distinguished between the similar energy characteristic peaks, which improve the error levels of activity analysis caused by the overlapping peak with significant effects.
文摘The perturbational reanalysis technique of matrix singular value decomposition is applicable to many theoretical and practical problems in mathematics, mechanics, control theory, engineering, etc.. An indirect perturbation method has previously been proposed by the author in this journal, and now the direct perturbation method has also been presented in this paper. The second-order perturbation results of non-repeated singular values and the corresponding left and right singular vectors are obtained. The results can meet the general needs of most problems of various practical applications. A numerical example is presented to demonstrate the effectiveness of the direct perturbation method.
文摘The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.
