A homogeneous and compact super-aligned carbon nanotube(SACNT)-reinforced nickel-matrix composite was successfully prepared by electrodeposition. The mechanical properties of the laminar SACNT/Ni composites were subst...A homogeneous and compact super-aligned carbon nanotube(SACNT)-reinforced nickel-matrix composite was successfully prepared by electrodeposition. The mechanical properties of the laminar SACNT/Ni composites were substantially improved compared with those of pure nickel. With increasing content of SACNTs, the tensile strength of the composite increased and the elongation decreased because of the high-strength SACNTs bearing part of an applied load and the fine-grained strengthening mechanism. The nanohardness of the SACNT/Ni composites was improved from 3.92 GPa(pure nickel) to 4.62 GPa(Ni-4 vol%SACNTs). The uniform distribution of SACNTs in the composites and strong interfacial bonding between the SACNTs and the nickel matrix resulted in an improvement of the mechanical properties of the SACNT/Ni composites. The introduced SACNTs refined the nickel grains, increased the amount of crystal twins, and changed the preferred orientation of grain growth.展开更多
In 2014, Vargas first defined a super-shuffle product and a cut-box coproduct on permutations. In 2020, Aval, Bergeron and Machacek introduced the super-shuffle product and the cut-box coproduct on labeled simple grap...In 2014, Vargas first defined a super-shuffle product and a cut-box coproduct on permutations. In 2020, Aval, Bergeron and Machacek introduced the super-shuffle product and the cut-box coproduct on labeled simple graphs. In this paper, we generalize the super-shuffle product and the cut-box coproduct from labeled simple graphs to (0,1)-matrices. Then we prove that the vector space spanned by (0,1)-matrices with the super-shuffle product is a graded algebra and with the cut-box coproduct is a graded coalgebra.展开更多
Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and exter...Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and external disturbance make the resolution of image still limited in a certain level.The goal of this paper is to use a single image super-resolution(SISR) method to predict a high-resolution(HR) MODIS image from a single low-resolution(LR) input.Recently,although the method based on sparse representation has tackled the ill-posed problem effectively,two fatal issues have been ignored.First,many methods ignore the relationships among patches,resulting in some unfaithful output.Second,the high computational complexity of sparse coding using l_1 norm is needed in reconstruction stage.In this work,we discover the semantic relationships among LR patches and the corresponding HR patches and group the documents with similar semantic into topics by probabilistic Latent Semantic Analysis(p LSA).Then,we can learn dual dictionaries for each topic in the low-resolution(LR) patch space and high-resolution(HR) patch space and also pre-compute corresponding regression matrices for dictionary pairs.Finally,for the test image,we infer locally which topic it corresponds to and adaptive to select the regression matrix to reconstruct HR image by semantic relationships.Our method discovered the relationships among patches and pre-computed the regression matrices for topics.Therefore,our method can greatly reduce the artifacts and get some speed-up in the reconstruction phase.Experiment manifests that our method performs MODIS image super-resolution effectively,results in higher PSNR,reconstructs faster,and gets better visual quality than some current state-of-art methods.展开更多
This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra A^-M. By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which ...This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra A^-M. By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which possesses the Hamiltonian structure. As its reduction cases, the multicomponent （2＋1）-dimensional Glachette-Johnson （G J） hierarchy is given. Finally, the super-integrable coupling system of multicomponent （2＋1）-dimensional GJ hierarchy is established through enlarging the spectral problem.展开更多
Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculatin...Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.展开更多
基金partially financially supported by the Tsinghua University Initiative Scientific Research Program(No.20111080980)the High Technology Research and Development Program of China(No.2013AA031201)
文摘A homogeneous and compact super-aligned carbon nanotube(SACNT)-reinforced nickel-matrix composite was successfully prepared by electrodeposition. The mechanical properties of the laminar SACNT/Ni composites were substantially improved compared with those of pure nickel. With increasing content of SACNTs, the tensile strength of the composite increased and the elongation decreased because of the high-strength SACNTs bearing part of an applied load and the fine-grained strengthening mechanism. The nanohardness of the SACNT/Ni composites was improved from 3.92 GPa(pure nickel) to 4.62 GPa(Ni-4 vol%SACNTs). The uniform distribution of SACNTs in the composites and strong interfacial bonding between the SACNTs and the nickel matrix resulted in an improvement of the mechanical properties of the SACNT/Ni composites. The introduced SACNTs refined the nickel grains, increased the amount of crystal twins, and changed the preferred orientation of grain growth.
文摘In 2014, Vargas first defined a super-shuffle product and a cut-box coproduct on permutations. In 2020, Aval, Bergeron and Machacek introduced the super-shuffle product and the cut-box coproduct on labeled simple graphs. In this paper, we generalize the super-shuffle product and the cut-box coproduct from labeled simple graphs to (0,1)-matrices. Then we prove that the vector space spanned by (0,1)-matrices with the super-shuffle product is a graded algebra and with the cut-box coproduct is a graded coalgebra.
基金partially supported by the National Natural Science Foundation of China (61471212)Natural Science Foundation of Zhejiang Province (LY16F010001)Natural Science Foundation of Ningbo (2016A610091, 2017A610297)
文摘Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and external disturbance make the resolution of image still limited in a certain level.The goal of this paper is to use a single image super-resolution(SISR) method to predict a high-resolution(HR) MODIS image from a single low-resolution(LR) input.Recently,although the method based on sparse representation has tackled the ill-posed problem effectively,two fatal issues have been ignored.First,many methods ignore the relationships among patches,resulting in some unfaithful output.Second,the high computational complexity of sparse coding using l_1 norm is needed in reconstruction stage.In this work,we discover the semantic relationships among LR patches and the corresponding HR patches and group the documents with similar semantic into topics by probabilistic Latent Semantic Analysis(p LSA).Then,we can learn dual dictionaries for each topic in the low-resolution(LR) patch space and high-resolution(HR) patch space and also pre-compute corresponding regression matrices for dictionary pairs.Finally,for the test image,we infer locally which topic it corresponds to and adaptive to select the regression matrix to reconstruct HR image by semantic relationships.Our method discovered the relationships among patches and pre-computed the regression matrices for topics.Therefore,our method can greatly reduce the artifacts and get some speed-up in the reconstruction phase.Experiment manifests that our method performs MODIS image super-resolution effectively,results in higher PSNR,reconstructs faster,and gets better visual quality than some current state-of-art methods.
基金supported by the National Key Basic Research Development of China (Grant No 2004CB318000)
文摘This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra A^-M. By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which possesses the Hamiltonian structure. As its reduction cases, the multicomponent （2＋1）-dimensional Glachette-Johnson （G J） hierarchy is given. Finally, the super-integrable coupling system of multicomponent （2＋1）-dimensional GJ hierarchy is established through enlarging the spectral problem.
文摘Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.
