To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on ...To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.展开更多
A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inne...A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.展开更多
To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are ...To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are put forward. First, the educational robot system is abstracted to a multibody system and a general dynamic model of the educational robot is constructed by the Newton-Euler method. Then the dynamic model is simplified by a combination of components with fixed connections according to the structural characteristics of the educational robot. Secondly, in order to obtain a high efficiency simulation algorithm, based on the sparse matrix technique, the augmentation algorithm and the direct projective constraint stabilization algorithm are improved. Finally, a numerical example is given. The results show that the model and the fast algorithm are valid and effective. This study lays a dynamic foundation for realizing the simulation platform of the educational robot.展开更多
In this paper, a two-level Bregman method is presented with graph regularized sparse coding for highly undersampled magnetic resonance image reconstruction. The graph regularized sparse coding is incorporated with the...In this paper, a two-level Bregman method is presented with graph regularized sparse coding for highly undersampled magnetic resonance image reconstruction. The graph regularized sparse coding is incorporated with the two-level Bregman iterative procedure which enforces the sampled data constraints in the outer level and updates dictionary and sparse representation in the inner level. Graph regularized sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge with a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can consistently reconstruct both simulated MR images and real MR data efficiently, and outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.展开更多
A channel estimator used in sparse muhipath fading channel for orthogonal frequency division multiplexing （OFDM） system is proposed. The dimension of signal subspace can be reduced to improve the performance of chan...A channel estimator used in sparse muhipath fading channel for orthogonal frequency division multiplexing （OFDM） system is proposed. The dimension of signal subspace can be reduced to improve the performance of channel estimation. The simplified version of original subspace fitting algorithm is employed to derive the sparse multipaths. In order to overcome the difficulty of termination condition, we consider it as a model identification problem and the set of nonzero paths is found under the generalized Akaike information criterion （GAIC）. The computational complexity can be kept very low under proper training design. Our proposed method is superior to other related schemes due to combining the procedure of selecting the most probable taps with GAIC model selection. Simulation in hilly terrain （HT） channel shows that the proposed method has an outstanding performance.展开更多
Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As...Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As one of the building blocks of the sparse representation method, dictionary learning plays an important role in the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although using training samples directly as dictionary bases can achieve good performance, the main drawback of this method is that it may result in a very large and inef- ficient dictionary due to noisy training instances. To obtain a smaller and more representative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary （LSD） learning. Our method is based on manifold learning and double sparsity. We incorporate the Laplacian weighted graph in the sparse representation model and impose the 11-norm sparsity on the dictionary. An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and learn a smaller dictionary for each class. The learned LSD can be easily integrated into a classification framework based on sparse representation. We compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA. Results show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification methods.展开更多
To understand genetic variation and population dispersal in the Yangtze vole Microtusfortis calamorum distributed in the Dongting Lake region, 144 individuals were collected from six habitat patches. The mitochondrial...To understand genetic variation and population dispersal in the Yangtze vole Microtusfortis calamorum distributed in the Dongting Lake region, 144 individuals were collected from six habitat patches. The mitochondrial DNA control region was sequenced and 17 haplotypes were observed. Of the six investigated populations, haplotype and nucleotide diversities of those from larger patches were higher than those from smaller patches. Nonparametric correlation analysis showed that patch size had a positive correlation with haplotype diversity （r = 0.943, P 〈 0.01）. A neighbour-joining tree of the 17 haplotypes showed no geo- graphic genetic structure among the six populations. Analysis of isolation by distance showed that genetic differentiation among the six populations was not positively related to geographic distance. Analysis of mismatch distribution indicated that the voles had passed through a population expansion. The pattern of haplotype distribution in the Changsha population suggests that the population was established by a founder effect展开更多
基金Project([2018]3010)supported by the Guizhou Provincial Science and Technology Major Project,China。
文摘To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.
基金The National Natural Science Foundation of China (No.61362001,61102043,61262084,20132BAB211030,20122BAB211015)the Basic Research Program of Shenzhen(No.JC201104220219A)
文摘A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.
基金Hexa-Type Elites Peak Program of Jiangsu Province(No.2008144)Qing Lan Project of Jiangsu ProvinceFund for Excellent Young Teachers of Southeast University
文摘To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are put forward. First, the educational robot system is abstracted to a multibody system and a general dynamic model of the educational robot is constructed by the Newton-Euler method. Then the dynamic model is simplified by a combination of components with fixed connections according to the structural characteristics of the educational robot. Secondly, in order to obtain a high efficiency simulation algorithm, based on the sparse matrix technique, the augmentation algorithm and the direct projective constraint stabilization algorithm are improved. Finally, a numerical example is given. The results show that the model and the fast algorithm are valid and effective. This study lays a dynamic foundation for realizing the simulation platform of the educational robot.
基金Supported by the National Natural Science Foundation of China(No.61261010No.61362001+7 种基金No.61365013No.61262084No.51165033)Technology Foundation of Department of Education in Jiangxi Province(GJJ13061GJJ14196)Young Scientists Training Plan of Jiangxi Province(No.20133ACB21007No.20142BCB23001)National Post-Doctoral Research Fund(No.2014M551867)and Jiangxi Advanced Project for Post-Doctoral Research Fund(No.2014KY02)
文摘In this paper, a two-level Bregman method is presented with graph regularized sparse coding for highly undersampled magnetic resonance image reconstruction. The graph regularized sparse coding is incorporated with the two-level Bregman iterative procedure which enforces the sampled data constraints in the outer level and updates dictionary and sparse representation in the inner level. Graph regularized sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge with a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can consistently reconstruct both simulated MR images and real MR data efficiently, and outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.
基金Supported by the Starting Fund for Science Research of NJUST (AB41947)the Open Research Fund of National Mobile Communications Research Laboratory (N200609)Science Research Developing Fund of NJUST (XKF07023)
文摘A channel estimator used in sparse muhipath fading channel for orthogonal frequency division multiplexing （OFDM） system is proposed. The dimension of signal subspace can be reduced to improve the performance of channel estimation. The simplified version of original subspace fitting algorithm is employed to derive the sparse multipaths. In order to overcome the difficulty of termination condition, we consider it as a model identification problem and the set of nonzero paths is found under the generalized Akaike information criterion （GAIC）. The computational complexity can be kept very low under proper training design. Our proposed method is superior to other related schemes due to combining the procedure of selecting the most probable taps with GAIC model selection. Simulation in hilly terrain （HT） channel shows that the proposed method has an outstanding performance.
基金Project supported by the National Natural Science Foundation of China （Nos. 61272304 and 61363029） and the Guangxi Key Laboratory of Trusted Software （No. kx201313）
文摘Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As one of the building blocks of the sparse representation method, dictionary learning plays an important role in the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although using training samples directly as dictionary bases can achieve good performance, the main drawback of this method is that it may result in a very large and inef- ficient dictionary due to noisy training instances. To obtain a smaller and more representative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary （LSD） learning. Our method is based on manifold learning and double sparsity. We incorporate the Laplacian weighted graph in the sparse representation model and impose the 11-norm sparsity on the dictionary. An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and learn a smaller dictionary for each class. The learned LSD can be easily integrated into a classification framework based on sparse representation. We compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA. Results show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification methods.
文摘To understand genetic variation and population dispersal in the Yangtze vole Microtusfortis calamorum distributed in the Dongting Lake region, 144 individuals were collected from six habitat patches. The mitochondrial DNA control region was sequenced and 17 haplotypes were observed. Of the six investigated populations, haplotype and nucleotide diversities of those from larger patches were higher than those from smaller patches. Nonparametric correlation analysis showed that patch size had a positive correlation with haplotype diversity （r = 0.943, P 〈 0.01）. A neighbour-joining tree of the 17 haplotypes showed no geo- graphic genetic structure among the six populations. Analysis of isolation by distance showed that genetic differentiation among the six populations was not positively related to geographic distance. Analysis of mismatch distribution indicated that the voles had passed through a population expansion. The pattern of haplotype distribution in the Changsha population suggests that the population was established by a founder effect
