Based on the engineering background of the Jiangxinzhou Bridge in Nanjing, issues related to the spatial main saddle of the self-anchored suspension bridge are studied. The refinement finite element model is establish...Based on the engineering background of the Jiangxinzhou Bridge in Nanjing, issues related to the spatial main saddle of the self-anchored suspension bridge are studied. The refinement finite element model is established by the secondary development technology based on the platform of the general finite element program, and a reasonable load pattern is used in its spatial structural analysis, by which its path of force transference and stress distribution are obtained. Matched with the spatial main cable, the tangency point correction method is also discussed. The results show that the lateral wall stress of the saddle groove is higher than the stress within the wall due to the role of lateral forces in the finished bridge state; the horizontal volume force of the main cable can generate a gradient distributed vertical extrusion pressure on the saddle clamping device and the main saddle body; the geometric nonlinear effect of the self- anchored suspension bridge cable system in the construction process is significant, which can be reflected in the spatial tangent point position of the main cable with the main saddle changes a lot from free cable to finished cable.展开更多
The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a...The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.展开更多
基金The National High Technology Research and Development Program of China(863 Program)(No.2006AA04Z416)the National Science Fund for Distinguished Young Scholars(No.50725828)
文摘Based on the engineering background of the Jiangxinzhou Bridge in Nanjing, issues related to the spatial main saddle of the self-anchored suspension bridge are studied. The refinement finite element model is established by the secondary development technology based on the platform of the general finite element program, and a reasonable load pattern is used in its spatial structural analysis, by which its path of force transference and stress distribution are obtained. Matched with the spatial main cable, the tangency point correction method is also discussed. The results show that the lateral wall stress of the saddle groove is higher than the stress within the wall due to the role of lateral forces in the finished bridge state; the horizontal volume force of the main cable can generate a gradient distributed vertical extrusion pressure on the saddle clamping device and the main saddle body; the geometric nonlinear effect of the self- anchored suspension bridge cable system in the construction process is significant, which can be reflected in the spatial tangent point position of the main cable with the main saddle changes a lot from free cable to finished cable.
文摘The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.
