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
Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is propose...Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.展开更多
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the ...A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.展开更多
Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar(ISAR)target recognition,this paper imports the concept of visual perception and presents a novel method,which is based on...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar(ISAR)target recognition,this paper imports the concept of visual perception and presents a novel method,which is based on the combination of non-negative sparse coding(NNSC)and linear discrimination optimization,to recognize targets in ISAR images.This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training,to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers.Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors,which are input into k neighbors classifier to realize recognition finally.The feasibility and robustness of the proposed method are proved by comparing with the template matching,principle component analysis(PCA)and non-negative matrix factorization(NMF)via simulations.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
Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometr...Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometric structure of image data to optimize the basis matrix in two steps.A threshold value s was first set to judge the threshold value of the decomposed base matrix to filter the redundant information in the data.Using L2 norm,sparse constraints were then implemented on the basis matrix,and integrated into the objective function to obtain the objective function of New-SGNMF.In addition,the derivation process of the algorithm and the convergence analysis of the algorithm were given.The experimental results on COIL20,PIE-pose09 and YaleB database show that compared with K-means,PCA,NMF and other algorithms,the proposed algorithm has higher accuracy and normalized mutual information.展开更多
卷积神经网络(CNN)是目前交通状态估计的深度学习算法中提取交通特征的关键模块,其对稀疏数据和多模式交通状态的计算不稳定,制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度,本文建立了一种编码-解码的交通自适应卷积...卷积神经网络(CNN)是目前交通状态估计的深度学习算法中提取交通特征的关键模块,其对稀疏数据和多模式交通状态的计算不稳定,制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度,本文建立了一种编码-解码的交通自适应卷积网络。首先,本文所提网络构建一种交通特征编码CNN,采用下采样操作聚合邻域交通信息,以从稀疏数据中提取有效的交通特征;其次,构建交通状态自适应重构CNN,利用提取的特征准确重构不同模式的交通状态。为表征多样化交通状态时空结构,该CNN引入先验知识引导卷积核形变。最后,实验采用长春市出租车GPS数据对算法在稀疏数据和不同情景下的交通状态估计性能进行验证。实验结果表明,与LSTM、GAN等先进算法相比,本文提出的改进的CNN算法估计精度提高了6.05 km/h RMSE,同时在不同交通情景下的估计精度仅差3.34%RMSE,能为深度学习估计交通状态提供有力的交通特征分析支撑。展开更多
单一物探方法在解释时不可避免地存在多解性,尤其是在复杂地质条件区。通常对同一测线不同方法的数据分别解释,再基于解释成果,综合分析,相互佐证,是一种简单的组合分析法。虽然考虑了不同方法的数据特征,但未能从数据层级挖掘其中更深...单一物探方法在解释时不可避免地存在多解性,尤其是在复杂地质条件区。通常对同一测线不同方法的数据分别解释,再基于解释成果,综合分析,相互佐证,是一种简单的组合分析法。虽然考虑了不同方法的数据特征,但未能从数据层级挖掘其中更深层次的特征,解释成果是多个数据剖面,显示也不直观。为此,文中提出一种基于稀疏自编码器(Sparse Auto Encoders,SAE)的多方法工程物探数据融合方法。SAE是一种深度网络算法,通过不断学习,自动挖掘蕴含在数据中的深层次特征。融合数据兼备了多种物探数据中蕴含的物性参数特征,充分挖掘了数据中的地质信息,有效降低了解释的多解性,并能做到更直观地显示,可以更加全面地反映地质异常体的特征。展开更多
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.
基金The National Natural Science Foundation of China (No.50875048)the Natural Science Foundation of Jiangsu Province (No.BK2007115)the National High Technology Research and Development Program of China (863 Program)(No.2007AA04Z421)
文摘Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
基金The Foundation of Hygiene and Health of Jiangsu Province(No.H2018042)the National Natural Science Foundation of China(No.61773114)the Key Research and Development Plan(Industry Foresight and Common Key Technology)of Jiangsu Province(No.BE2017007-3)
文摘A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China(61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar(ISAR)target recognition,this paper imports the concept of visual perception and presents a novel method,which is based on the combination of non-negative sparse coding(NNSC)and linear discrimination optimization,to recognize targets in ISAR images.This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training,to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers.Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors,which are input into k neighbors classifier to realize recognition finally.The feasibility and robustness of the proposed method are proved by comparing with the template matching,principle component analysis(PCA)and non-negative matrix factorization(NMF)via simulations.
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).
基金This work was supported by the National Natural Science Foundation of China(Grant No.61501005)the Anhui Natural Science Foundation(Grant No.1608085 MF 147)+2 种基金the Natural Science Foundation of Anhui Universities(Grant No.KJ2016A057)the Industry Collaborative Innovation Fund of Anhui Polytechnic University and Jiujiang District(Grant No.2021cyxtb4)the Science Research Project of Anhui Polytechnic University(Grant No.Xjky2020120).
文摘Aiming at the low recognition accuracy of non-negative matrix factorization(NMF)in practical application,an improved spare graph NMF(New-SGNMF)is proposed in this paper.New-SGNMF makes full use of the inherent geometric structure of image data to optimize the basis matrix in two steps.A threshold value s was first set to judge the threshold value of the decomposed base matrix to filter the redundant information in the data.Using L2 norm,sparse constraints were then implemented on the basis matrix,and integrated into the objective function to obtain the objective function of New-SGNMF.In addition,the derivation process of the algorithm and the convergence analysis of the algorithm were given.The experimental results on COIL20,PIE-pose09 and YaleB database show that compared with K-means,PCA,NMF and other algorithms,the proposed algorithm has higher accuracy and normalized mutual information.
文摘为降低室内定位指纹数据库构建的复杂度并提高定位精度,提出了一种5G超密集网络下的基于矩阵补全的室内指纹定位算法。在离线数据库构建阶段,算法首先采用K近邻(K-Nearest Neighbor,KNN)插值法对部分指纹库进行矩阵补全,构建完整的数据库;其次,采用稀疏自编码器提取指纹库的稀疏特征,对高维接收信号强度指示(Received Signal Strength Indication,RSSI)信号进行降维处理。在在线指纹匹配阶段,使用加权KNN算法估算出待定位点坐标。经过实验仿真分析,算法重构指纹数据库的平均相对误差为0.31%;与传统KNN指纹匹配算法相比,平均误差降低了24.41%。
文摘卷积神经网络(CNN)是目前交通状态估计的深度学习算法中提取交通特征的关键模块,其对稀疏数据和多模式交通状态的计算不稳定,制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度,本文建立了一种编码-解码的交通自适应卷积网络。首先,本文所提网络构建一种交通特征编码CNN,采用下采样操作聚合邻域交通信息,以从稀疏数据中提取有效的交通特征;其次,构建交通状态自适应重构CNN,利用提取的特征准确重构不同模式的交通状态。为表征多样化交通状态时空结构,该CNN引入先验知识引导卷积核形变。最后,实验采用长春市出租车GPS数据对算法在稀疏数据和不同情景下的交通状态估计性能进行验证。实验结果表明,与LSTM、GAN等先进算法相比,本文提出的改进的CNN算法估计精度提高了6.05 km/h RMSE,同时在不同交通情景下的估计精度仅差3.34%RMSE,能为深度学习估计交通状态提供有力的交通特征分析支撑。
文摘单一物探方法在解释时不可避免地存在多解性,尤其是在复杂地质条件区。通常对同一测线不同方法的数据分别解释,再基于解释成果,综合分析,相互佐证,是一种简单的组合分析法。虽然考虑了不同方法的数据特征,但未能从数据层级挖掘其中更深层次的特征,解释成果是多个数据剖面,显示也不直观。为此,文中提出一种基于稀疏自编码器(Sparse Auto Encoders,SAE)的多方法工程物探数据融合方法。SAE是一种深度网络算法,通过不断学习,自动挖掘蕴含在数据中的深层次特征。融合数据兼备了多种物探数据中蕴含的物性参数特征,充分挖掘了数据中的地质信息,有效降低了解释的多解性,并能做到更直观地显示,可以更加全面地反映地质异常体的特征。
