Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducte...Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.展开更多
This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preser...This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.展开更多
In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems ...In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.展开更多
An automated method to optimize the definition of the progress variables in the flamelet-based dimension reduction is proposed. The performance of these optimized progress variables in coupling the flamelets and flow ...An automated method to optimize the definition of the progress variables in the flamelet-based dimension reduction is proposed. The performance of these optimized progress variables in coupling the flamelets and flow solver is presented. In the proposed method, the progress variables are defined according to the first two principal components （PCs） from the principal component analysis （PCA） or kernel-density-weighted PCA （KEDPCA） of a set of flamelets. These flamelets can then be mapped to these new progress variables instead of the mixture fraction/conventional progress variables. Thus, a new chemistry look-up table is constructed. A priori validation of these optimized progress variables and the new chemistry table is implemented in a CH4/N2/air lift-off flame. The reconstruction of the lift-off flame shows that the optimized progress variables perform better than the conventional ones, especially in the high temperature area. The coefficient determinations （R2 statistics） show that the KEDPCA performs slightly better than the PCA except for some minor species. The main advantage of the KEDPCA is that it is less sensitive to the database. Meanwhile, the criteria for the optimization are proposed and discussed. The constraint that the progress variables should monotonically evolve from fresh gas to burnt gas is analyzed in detail.展开更多
水泥生产过程中,分解炉出口温度是非常重要的工艺参数,为了应对出口温度变量的多样性,文章提出一种核主成分分析(kernel principal component analysis,KPCA)与双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络相...水泥生产过程中,分解炉出口温度是非常重要的工艺参数,为了应对出口温度变量的多样性,文章提出一种核主成分分析(kernel principal component analysis,KPCA)与双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络相结合的温度预测组合模型用来预测分解炉的出口温度。通过KPCA筛选出影响因素的主成分从而达到数据降维目的,将降维后的主成分作为BiLSTM神经网络的输入,分解炉出口温度作为BiLSTM神经网络的输出。经BiLSTM神经网络训练,得到分解炉出口温度预测模型。通过对比验证表明,使用KPCA-BiLSTM相结合的温度预测模型具有较好的预测精度。展开更多
环境敏感因子是沉积环境演化研究中的重要参数,其提取方法有多种,不同方法在海湾内应用成果相对较少。文章根据山东半岛威海湾WH-05岩心(钻探深度18.2 m)高分辨率(2 cm)粒度分析结果,采用基本端元模拟算法(BasEMMA)、粒级—标准偏差法...环境敏感因子是沉积环境演化研究中的重要参数,其提取方法有多种,不同方法在海湾内应用成果相对较少。文章根据山东半岛威海湾WH-05岩心(钻探深度18.2 m)高分辨率(2 cm)粒度分析结果,采用基本端元模拟算法(BasEMMA)、粒级—标准偏差法和粒级旋转主成分分析法(V-PCA)提取了环境敏感因子,结合AMS14C数据,对不同方法提取的环境敏感因子进行了对比分析,探讨了其对季风强度变化的响应关系。结果表明:8.4 ka B.P.以来威海湾沉积物类型均为粉砂,粉砂含量为主、黏土含量次之、砂含量较少,以跳跃组分为主,悬浮组分次之。BasEMMA和粒级—标准偏差法提取的3个敏感粒级范围(2.6~11.0μm、31.3~63.4μm、256.9~500.0μm)总体一致,能较好地反映东亚季风长周期变化事件(8.4~6.5 ka B.P.东亚季风强度减弱;6.5 ka B.P.至今东亚季风强度增强);V-PCA提取的环境敏感因子,能较好地反映东亚季风短周期变化事件(如明清小冰期、西汉小冰期事件等)。以上三种方法提取的环境敏感因子指示了研究区气候变化,对研究区沉积环境具有较好的指示作用。展开更多
基金Climbing Peak Discipline Project of Shanghai Dianji University,China(No.15DFXK02)Hi-Tech Research and Development Programs of China(No.2007AA041600)
文摘Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.
基金Sponsored by the National Science Foundation of China( Grant No. 61201370,61100103)the Independent Innovation Foundation of Shandong University( Grant No. 2012DX07)
文摘This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.
基金supported by National High Technology Research and Development Program of China (863 Program)(No. 2009AA04Z162)National Nature Science Foundation of China(No. 60825302, No. 60934007, No. 61074061)+1 种基金Program of Shanghai Subject Chief Scientist,"Shu Guang" project supported by Shang-hai Municipal Education Commission and Shanghai Education Development FoundationKey Project of Shanghai Science and Technology Commission, China (No. 10JC1403400)
文摘In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
基金Project supported by the National Natural Science Foundation of China(Nos.50936005,51576182,and 11172296)
文摘An automated method to optimize the definition of the progress variables in the flamelet-based dimension reduction is proposed. The performance of these optimized progress variables in coupling the flamelets and flow solver is presented. In the proposed method, the progress variables are defined according to the first two principal components （PCs） from the principal component analysis （PCA） or kernel-density-weighted PCA （KEDPCA） of a set of flamelets. These flamelets can then be mapped to these new progress variables instead of the mixture fraction/conventional progress variables. Thus, a new chemistry look-up table is constructed. A priori validation of these optimized progress variables and the new chemistry table is implemented in a CH4/N2/air lift-off flame. The reconstruction of the lift-off flame shows that the optimized progress variables perform better than the conventional ones, especially in the high temperature area. The coefficient determinations （R2 statistics） show that the KEDPCA performs slightly better than the PCA except for some minor species. The main advantage of the KEDPCA is that it is less sensitive to the database. Meanwhile, the criteria for the optimization are proposed and discussed. The constraint that the progress variables should monotonically evolve from fresh gas to burnt gas is analyzed in detail.
文摘水泥生产过程中,分解炉出口温度是非常重要的工艺参数,为了应对出口温度变量的多样性,文章提出一种核主成分分析(kernel principal component analysis,KPCA)与双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络相结合的温度预测组合模型用来预测分解炉的出口温度。通过KPCA筛选出影响因素的主成分从而达到数据降维目的,将降维后的主成分作为BiLSTM神经网络的输入,分解炉出口温度作为BiLSTM神经网络的输出。经BiLSTM神经网络训练,得到分解炉出口温度预测模型。通过对比验证表明,使用KPCA-BiLSTM相结合的温度预测模型具有较好的预测精度。
文摘环境敏感因子是沉积环境演化研究中的重要参数,其提取方法有多种,不同方法在海湾内应用成果相对较少。文章根据山东半岛威海湾WH-05岩心(钻探深度18.2 m)高分辨率(2 cm)粒度分析结果,采用基本端元模拟算法(BasEMMA)、粒级—标准偏差法和粒级旋转主成分分析法(V-PCA)提取了环境敏感因子,结合AMS14C数据,对不同方法提取的环境敏感因子进行了对比分析,探讨了其对季风强度变化的响应关系。结果表明:8.4 ka B.P.以来威海湾沉积物类型均为粉砂,粉砂含量为主、黏土含量次之、砂含量较少,以跳跃组分为主,悬浮组分次之。BasEMMA和粒级—标准偏差法提取的3个敏感粒级范围(2.6~11.0μm、31.3~63.4μm、256.9~500.0μm)总体一致,能较好地反映东亚季风长周期变化事件(8.4~6.5 ka B.P.东亚季风强度减弱;6.5 ka B.P.至今东亚季风强度增强);V-PCA提取的环境敏感因子,能较好地反映东亚季风短周期变化事件(如明清小冰期、西汉小冰期事件等)。以上三种方法提取的环境敏感因子指示了研究区气候变化,对研究区沉积环境具有较好的指示作用。
