Learning Mixed Membership from Adjacency Graph Via Systematic Edge Query: Identifiability and Algorithm

2021 ◽  
Author(s):  
Shahana Ibrahim ◽  
Xiao Fu
Keyword(s):  
Adjacency Graph

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals Contiguity: Contig adjacency graph construction and visualisation

2015 ◽  
Author(s):  
Mitchell J Sullivan ◽  
Nouri L Ben Zakour ◽  
Brian M Forde ◽  
Mitchell Stanton-Cook ◽  
Scott A Beatson
Keyword(s):  
De Novo ◽  
Reference Sequence ◽  
De Bruijn Graph ◽  
Interactive Software ◽  
Graph Exploration ◽  
Adjacency Graph ◽  
Highly Sensitive ◽  
Long Read ◽  
Genome Assemblies ◽  
Adjacency Graphs

Contiguity is an interactive software for the visualization and manipulation of de novo genome assemblies. Contiguity creates and displays information on contig adjacency which is contextualized by the simultaneous display of a comparison between assembled contigs and reference sequence. Where scaffolders allow unambiguous connections between contigs to be resolved into a single scaffold, Contiguity allows the user to create all potential scaffolds in ambiguous regions of the genome. This enables the resolution of novel sequence or structural variants from the assembly. In addition, Contiguity provides a sequencing and assembly agnostic approach for the creation of contig adjacency graphs. To maximize the number of contig adjacencies determined, Contiguity combines information from read pair mappings, sequence overlap and De Bruijn graph exploration. We demonstrate how highly sensitive graphs can be achieved using this method. Contig adjacency graphs allow the user to visualize potential arrangements of contigs in unresolvable areas of the genome. By combining adjacency information with comparative genomics, Contiguity provides an intuitive approach for exploring and improving sequence assemblies. It is also useful in guiding manual closure of long read sequence assemblies. Contiguity is an open source application, implemented using Python and the Tkinter GUI package that can run on any Unix, OSX and Windows operating system. It has been designed and optimized for bacterial assemblies. Contiguity is available at http://mjsull.github.io/Contiguity .

scholarly journals APPLICATION OF RIGID LINKS IN STRUCTURAL DESIGN MODELS

2017 ◽  
Vol 13 (3) ◽  
pp. 119-137 ◽  
Author(s):  
Sergey Yu. Fialko
Keyword(s):  
Finite Element ◽  
Structural Design ◽  
Stiffness Matrix ◽  
Sparse Matrix ◽  
Element Model ◽  
Rigid Bodies ◽  
Design Models ◽  
Adjacency Graph ◽  
Direct Solvers ◽  
The Finite Element Model

A special finite element modelling rigid links is proposed for the linear static and buckling analysis. Unlike the classical approach based on the theorems of rigid body kinematics, the proposed approach preserves the similarity between the adjacency graph for a sparse matrix and the adjacency graph for nodes of the finite element model, which allows applying sparse direct solvers more effectively. Besides, the proposed approach allows significantly reducing the number of nonzero entries in the factored stiffness matrix in comparison with the classical one, which greatly reduces the duration of the solution. For buckling problems of structures containing rigid bodies, this approach gives correct results. Several examples demonstrate its efficiency.

Facility Layout Planning

2013 ◽  
pp. 212-223
Author(s):  
Hossein Hojabri ◽  
Elnaz Miandoabchi
Keyword(s):  
Mathematical Models ◽  
Planar Graph ◽  
Facility Layout ◽  
Layout Design ◽  
Simple Graph ◽  
Layout Planning ◽  
Adjacency Graph ◽  
Manufacturing Plants ◽  
Maximal Planar Graph ◽  
Facilities Layout

The weighted maximal planar graph (WMPG) appears in many applications. It is currently used to design facilities layout in manufacturing plants. Given an edge-weighted complete simple graph G, the WMPG involves finding a sub-graph of G that is planar in the sense that it could be embedded on the plane such that none of its edges intersect, and is maximal in the sense that no more edges can be added to it unless its planarity is violated. Finally, it is optimal in the sense that the resulting maximal planar graph holds the maximum sum of edge weights. In this chapter, the aim is to explain the application of planarity in facility layout design. The mathematical models and the algorithms developed for the problem so far are explained. In the meanwhile, the corollaries and theorems needed to explain the algorithms and models are briefly given. In the last part, an explanation on how to draw block layout from the adjacency graph is given.

Sparse representation preserving embedding based on extreme learning machine for process monitoring

2020 ◽  
Vol 42 (10) ◽  
pp. 1895-1907
Author(s):  
Hui Yongyong ◽  
Zhao Xiaoqiang
Keyword(s):  
Sparse Representation ◽  
Extreme Learning Machine ◽  
Process Monitoring ◽  
Nonlinear Dynamic ◽  
Monitoring Method ◽  
Adjacency Graph ◽  
Manifold Structure ◽  
Fast Learning ◽  
Learning Machine ◽  
Adaptive Neighborhood

Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring.

scholarly journals Automated Generation of Linkage Loop Equations for Planar One Degree-of-Freedom Linkages, Demonstrated up to 8-Bar

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Brian E. Parrish ◽  
J. Michael McCarthy ◽  
David Eppstein
Keyword(s):  
Kinematic Analysis ◽  
Degree Of Freedom ◽  
Common Edge ◽  
Adjacency Graph ◽  
Automated Generation ◽  
Cycle Basis ◽  
Revolute Joints

In this paper, we present an algorithm that automatically creates the linkage loop equations for planar one degree of freedom, 1DOF, linkages of any topology with revolute joints, demonstrated up to 8 bar. The algorithm derives the linkage loop equations from the linkage adjacency graph by establishing a rooted cycle basis through a single common edge. Divergent and convergent loops are identified and used to establish the fixed angles of the ternary and higher links. Results demonstrate the automated generation of the linkage loop equations for the nine unique 6-bar linkages with ground-connected inputs that can be constructed from the five distinct 6-bar mechanisms, Watt I–II and Stephenson I–III. Results also automatically produced the loop equations for all 153 unique linkages with a ground-connected input that can be constructed from the 71 distinct 8-bar mechanisms. The resulting loop equations enable the automatic derivation of the Dixon determinant for linkage kinematic analysis of the position of every possible assembly configuration. The loop equations also enable the automatic derivation of the Jacobian for singularity evaluation and tracking of a particular assembly configuration over the desired range of input angles. The methodology provides the foundation for the automated configuration analysis of every topology and every assembly configuration of 1DOF linkages with revolute joints up to 8 bar. The methodology also provides a foundation for automated configuration analysis of 10-bar and higher linkages.

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