This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first l...This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.展开更多
Fiber reinforced composite frame structure is an ideal lightweight and large-span structure in the fields of aerospace,satellite and wind turbine.Natural fundamental frequency is one of key indicators in the design re...Fiber reinforced composite frame structure is an ideal lightweight and large-span structure in the fields of aerospace,satellite and wind turbine.Natural fundamental frequency is one of key indicators in the design requirement of the composite frame since structural resonance can be effectively avoided with the increase of the fundamental frequency.Inspired by the concept of integrated design optmization of composite frame structures and materials,the design optimization for the maximum structural fundamental frequency of fiber reinforced frame structures is proposed.An optimization model oriented at the maximum structural fundamental frequency under a composite material volume constraint is established.Two kinds of independent design variables are optimized,in which one is variables represented structural topology,the other is variables of continuous fiber winding angles.Sensitivity analysis of the frequency with respect to the two kinds of independent design variables is implemented with the semi-analytical sensitivity method.Some representative examples in the manuscript demonstrate that the integrated design optimization of composite structures can effectively explore coupled effects between structural configurations and material properties to increase the structural fundamental frequency.The proposed integrated optimization model has great potential to improve composite frames structural dynamic performance in aerospace industries.展开更多
Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distrib...Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.展开更多
Let X be the vertex set of Kn. A k-cycle packing of Kn is a triple （X, C, L）, where C is a collection of edge disjoint k-cycles of Kn and L is the collection of edges of Kn not belonging to any of the k-cycles in C....Let X be the vertex set of Kn. A k-cycle packing of Kn is a triple （X, C, L）, where C is a collection of edge disjoint k-cycles of Kn and L is the collection of edges of Kn not belonging to any of the k-cycles in C. A k-cycle packing （X, C, L） is called resolvable if C can be partitioned into almost parallel classes. A resolvable maximum k-cycle packing of Kn, denoted by k-RMCP（n）, is a resolvable k-cycle packing of Kn, （X, C, L）, in which the number of almost parallel classes is as large as possible. Let D（n, k） denote the number of almost parallel classes in a k-RMCP（n）. D（n, k） for k = 3, 4 has been decided. When n ≡ k （mod 2k） and k ≡1 （mod 2） or n ≡1 （mod 2k） and k e {6, 8, 10, 14} U{m： 5 ≤ m ≤ 49, m ≡1 （mod 2）}, D（n,k） also has been decided with few possible exceptions. In this paper, we shall decide D（n, 5） for all values of n ≥ 5.展开更多
文摘This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.
基金Financial supports for this research were provided by the National Natural Science Foundation of China(Grants 11372060,11672057 and 11711530018)the 111 Project(Grant B14013)the Program of BK21 Plus.These supports are gratefully acknowledged.
文摘Fiber reinforced composite frame structure is an ideal lightweight and large-span structure in the fields of aerospace,satellite and wind turbine.Natural fundamental frequency is one of key indicators in the design requirement of the composite frame since structural resonance can be effectively avoided with the increase of the fundamental frequency.Inspired by the concept of integrated design optmization of composite frame structures and materials,the design optimization for the maximum structural fundamental frequency of fiber reinforced frame structures is proposed.An optimization model oriented at the maximum structural fundamental frequency under a composite material volume constraint is established.Two kinds of independent design variables are optimized,in which one is variables represented structural topology,the other is variables of continuous fiber winding angles.Sensitivity analysis of the frequency with respect to the two kinds of independent design variables is implemented with the semi-analytical sensitivity method.Some representative examples in the manuscript demonstrate that the integrated design optimization of composite structures can effectively explore coupled effects between structural configurations and material properties to increase the structural fundamental frequency.The proposed integrated optimization model has great potential to improve composite frames structural dynamic performance in aerospace industries.
文摘Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.
文摘Let X be the vertex set of Kn. A k-cycle packing of Kn is a triple （X, C, L）, where C is a collection of edge disjoint k-cycles of Kn and L is the collection of edges of Kn not belonging to any of the k-cycles in C. A k-cycle packing （X, C, L） is called resolvable if C can be partitioned into almost parallel classes. A resolvable maximum k-cycle packing of Kn, denoted by k-RMCP（n）, is a resolvable k-cycle packing of Kn, （X, C, L）, in which the number of almost parallel classes is as large as possible. Let D（n, k） denote the number of almost parallel classes in a k-RMCP（n）. D（n, k） for k = 3, 4 has been decided. When n ≡ k （mod 2k） and k ≡1 （mod 2） or n ≡1 （mod 2k） and k e {6, 8, 10, 14} U{m： 5 ≤ m ≤ 49, m ≡1 （mod 2）}, D（n,k） also has been decided with few possible exceptions. In this paper, we shall decide D（n, 5） for all values of n ≥ 5.
