The Experts below are selected from a list of 247179 Experts worldwide ranked by ideXlab platform
Pierino Lestuzzi - One of the best experts on this subject based on the ideXlab platform.
-
an equivalent Frame Model for seismic analysis of masonry and reinforced concrete buildings
Construction and Building Materials, 2009Co-Authors: Y Belmouden, Pierino LestuzziAbstract:Abstract In this paper a novel equivalent planar-Frame Model with openings is presented. The Model deals with seismic analysis using the Pushover method for masonry and reinforced concrete buildings. Each wall with opening can be decomposed into parallel structural walls made of an assemblage of piers and a portion of spandrels. As formulated, the structural Model undergoes inelastic flexural as well as inelastic shear deformations. The mathematical Model is based on the smeared cracks and distributed plasticity approach. Both zero moment location shifting in piers and spandrels can be evaluated. The constitutive laws are Modeled as bilinear curves in flexure and in shear. A biaxial interaction rule for both axial force–bending moment and axial force–shear force are considered. The Model can support any shape of failure criteria. An event-to-event strategy is used to solve the nonlinear problem. Two applications are used to show the ability of the Model to study both reinforced concrete and unreinforced masonry structures. Relevant findings are compared to analytical results from experimental, simplified Models and finite element Models such as Drain3DX and ETABS finite element package.
Ying Nian Wu - One of the best experts on this subject based on the ideXlab platform.
-
Learning Sparse Frame Models for Natural Image Patterns
International Journal of Computer Vision, 2015Co-Authors: Jianwen Xie, Wenze Hu, Song-chun Zhu, Ying Nian WuAbstract:It is well known that natural images admit sparse representations by redundant dictionaries of basis functions such as Gabor-like wavelets. However, it is still an open question as to what the next layer of representational units above the layer of wavelets should be. We address this fundamental question by proposing a sparse Frame (Filters, Random field, And Maximum Entropy) Model for representing natural image patterns. Our sparse Frame Model is an inhomogeneous generalization of the original Frame Model. It is a non-stationary Markov random field Model that reproduces the observed statistical properties of filter responses at a subset of selected locations, scales and orientations. Each sparse Frame Model is intended to represent an object pattern and can be considered a deformable template. The sparse Frame Model can be written as a shared sparse coding Model, which motivates us to propose a two-stage algorithm for learning the Model. The first stage selects the subset of wavelets from the dictionary by a shared matching pursuit algorithm. The second stage then estimates the parameters of the Model given the selected wavelets. Our experiments show that the sparse Frame Models are capable of representing a wide variety of object patterns in natural images and that the learned Models are useful for object classification.
Jianwen Xie - One of the best experts on this subject based on the ideXlab platform.
-
Learning Sparse Frame Models for Natural Image Patterns
International Journal of Computer Vision, 2015Co-Authors: Jianwen Xie, Wenze Hu, Song-chun Zhu, Ying Nian WuAbstract:It is well known that natural images admit sparse representations by redundant dictionaries of basis functions such as Gabor-like wavelets. However, it is still an open question as to what the next layer of representational units above the layer of wavelets should be. We address this fundamental question by proposing a sparse Frame (Filters, Random field, And Maximum Entropy) Model for representing natural image patterns. Our sparse Frame Model is an inhomogeneous generalization of the original Frame Model. It is a non-stationary Markov random field Model that reproduces the observed statistical properties of filter responses at a subset of selected locations, scales and orientations. Each sparse Frame Model is intended to represent an object pattern and can be considered a deformable template. The sparse Frame Model can be written as a shared sparse coding Model, which motivates us to propose a two-stage algorithm for learning the Model. The first stage selects the subset of wavelets from the dictionary by a shared matching pursuit algorithm. The second stage then estimates the parameters of the Model given the selected wavelets. Our experiments show that the sparse Frame Models are capable of representing a wide variety of object patterns in natural images and that the learned Models are useful for object classification.
-
CVPR - Learning Inhomogeneous Frame Models for Object Patterns
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014Co-Authors: Jianwen Xie, Song-chun ZhuAbstract:We investigate an inhomogeneous version of the Frame (Filters, Random field, And Maximum Entropy) Model and apply it to Modeling object patterns. The inhomogeneous Frame is a non-stationary Markov random field Model that reproduces the observed marginal distributions or statistics of filter responses at all the different locations, scales and orientations. Our experiments show that the inhomogeneous Frame Model is capable of generating a wide variety of object patterns in natural images. We then propose a sparsified version of the inhomogeneous Frame Model where the Model reproduces observed statistical properties of filter responses at a small number of selected locations, scales and orientations. We propose to select these locations, scales and orientations by a shared sparse coding scheme, and we explore the connection between the sparse Frame Model and the linear additive sparse coding Model. Our experiments show that it is possible to learn sparse Frame Models in unsupervised fashion and the learned Models are useful for object classification.
Y Belmouden - One of the best experts on this subject based on the ideXlab platform.
-
an equivalent Frame Model for seismic analysis of masonry and reinforced concrete buildings
Construction and Building Materials, 2009Co-Authors: Y Belmouden, Pierino LestuzziAbstract:Abstract In this paper a novel equivalent planar-Frame Model with openings is presented. The Model deals with seismic analysis using the Pushover method for masonry and reinforced concrete buildings. Each wall with opening can be decomposed into parallel structural walls made of an assemblage of piers and a portion of spandrels. As formulated, the structural Model undergoes inelastic flexural as well as inelastic shear deformations. The mathematical Model is based on the smeared cracks and distributed plasticity approach. Both zero moment location shifting in piers and spandrels can be evaluated. The constitutive laws are Modeled as bilinear curves in flexure and in shear. A biaxial interaction rule for both axial force–bending moment and axial force–shear force are considered. The Model can support any shape of failure criteria. An event-to-event strategy is used to solve the nonlinear problem. Two applications are used to show the ability of the Model to study both reinforced concrete and unreinforced masonry structures. Relevant findings are compared to analytical results from experimental, simplified Models and finite element Models such as Drain3DX and ETABS finite element package.
Wu Xiao-yue - One of the best experts on this subject based on the ideXlab platform.
-
Study of tentative-based satellite data transmission tasks scheduling algorithm
Systems engineering and electronics, 2007Co-Authors: Wu Xiao-yueAbstract:The problem of satellite data transmission tasks scheduling is a very complex combination optimization problem,that is the problem of how to allocate the ground stations resources and their service time in order to satisfy tasks requests farthest.According to the characteristic of satellite data transmission,the Frame Model of satellite data transmission is proposed firstly,and then puts forward some concepts: task executing flexibility degree,task executing conflict degree and so on.Based above,this paper gives a tentative-based satellite data transmission tasks scheduling algorithm.At last,the algorithm is simulated by using AFIT benchmark data,the result shows that the algorithm is feasible.
-
Research on the Model of Satellite Data Transmission Scheduling
Journal of National University of Defense Technology, 2007Co-Authors: Wu Xiao-yueAbstract:The problem of satellite data transmission is an optimization problem with multi-time windows and multi-resources constrains.In view of the problem,the paper constructs satellite data transmission request Model,task Model and scheduling Model.In the process of constructing Models,the style of Frame Model is adopted,while all the constrains are put into every data transmission task.This not only reduces the complexity of scheduling Model,but also reduces the difficulty of designing scheduling algorithm.In addition,this paper also presents the idea of designing scheduling algorithm,and designs a flexibility based scheduling algorithm.Results from simulation show that the Model and algorithm are feasible for solving the satellite data transmission scheduling problem.
