Graph Model for OVSF Code Placement

2010 ◽  
Author(s):  
Chang Wu Yu ◽  
Wen Ouyang ◽  
Chien-Yeh Wang ◽  
Meng-Ti Liu ◽  
Yu-Wei Chang
Keyword(s):  
Graph Model ◽  
Code Placement

Graph model for optimal OVSF code placement strategies

2012 ◽  
Vol 9 (3) ◽  
pp. 133 ◽  
Author(s):  
Wen Ouyang ◽  
Chang Wu Yu ◽  
Meng Ti Liu ◽  
Yu Wei Chang
Keyword(s):  
Graph Model ◽  
Code Placement

Bond graph model of a PEM fuel cell stack

2008 ◽  
Vol 1 (06) ◽  
pp. 329-334
Author(s):  
S. Rabih ◽  
C. Turpin ◽  
S. Astier
Keyword(s):  
Fuel Cell ◽  
Graph Model ◽  
Pem Fuel Cell ◽  
Bond Graph ◽  
Fuel Cell Stack

Structural Analysis of Product and Computer-Aided Assembly Planning in AssemBL Software Package

2018 ◽  
pp. 11-33
Author(s):  
A. N. Bozhko
Keyword(s):  
Computer Aided Design ◽  
State Of The Art ◽  
Graph Model ◽  
Design System ◽  
Assembly Planning ◽  
Complex Products ◽  
Assembly Sequences ◽  
Computer Aided ◽  
Cad Cam ◽  
Aided Design

Computer-aided design of assembly processes (Computer aided assembly planning, CAAP) of complex products is an important and urgent problem of state-of-the-art information technologies. Intensive research on CAAP has been underway since the 1980s. Meanwhile, specialized design systems were created to provide synthesis of assembly plans and product decompositions into assembly units. Such systems as ASPE, RAPID, XAP / 1, FLAPS, Archimedes, PRELEIDES, HAP, etc. can be given, as an example. These experimental developments did not get widespread use in industry, since they are based on the models of products with limited adequacy and require an expert’s active involvement in preparing initial information. The design tools for the state-of-the-art full-featured CAD/CAM systems (Siemens NX, Dassault CATIA and PTC Creo Elements / Pro), which are designed to provide CAAP, mainly take into account the geometric constraints that the design imposes on design solutions. These systems often synthesize technologically incorrect assembly sequences in which known technological heuristics are violated, for example orderliness in accuracy, consistency with the system of dimension chains, etc.An AssemBL software application package has been developed for a structured analysis of products and a synthesis of assembly plans and decompositions. The AssemBL uses a hyper-graph model of a product that correctly describes coherent and sequential assembly operations and processes. In terms of the hyper-graph model, an assembly operation is described as shrinkage of edge, an assembly plan is a sequence of shrinkages that converts a hyper-graph into the point, and a decomposition of product into assembly units is a hyper-graph partition into sub-graphs.The AssemBL solves the problem of minimizing the number of direct checks for geometric solvability when assembling complex products. This task is posed as a plus-sum two-person game of bicoloured brushing of an ordered set. In the paradigm of this model, the brushing operation is to check a certain structured fragment for solvability by collision detection methods. A rational brushing strategy minimizes the number of such checks.The package is integrated into the Siemens NX 10.0 computer-aided design system. This solution allowed us to combine specialized AssemBL tools with a developed toolkit of one of the most powerful and popular integrated CAD/CAM /CAE systems.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

Random graphs

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Large Scale ◽  
Random Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Random Graph Model ◽  
Definition Of ◽  
Average Size

An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a discussion of some of the shortcomings of the random graph model.

scholarly journals Parameterization, Modeling, and Validation in Real Conditions of an External Gear Pump

2021 ◽  
Vol 13 (6) ◽  
pp. 3089
Author(s):  
Miquel Torrent ◽  
Pedro Javier Gamez-Montero ◽  
Esteban Codina
Keyword(s):  
Working Conditions ◽  
Test Bench ◽  
Fluid Dynamic ◽  
Graph Model ◽  
Field Tests ◽  
Black Box ◽  
Gear Pump ◽  
Volumetric Behavior ◽  
External Gear Pump ◽  
Standard Tests

This article presents a methodology for predicting the fluid dynamic behavior of a gear pump over its operating range. Complete pump parameterization was carried out through standard tests, and these parameters were used to create a bond graph model to simulate the behavior of the unit. This model was experimentally validated under working conditions in field tests. To carry this out, the pump was used to drive the auxiliary movements of a drilling machine, and the experimental data were compared with a simulation of the volumetric behavior under the same conditions. This paper aims to describe a method for characterizing any hydrostatic pump as a “black box” model predicting its behavior in any operating condition. The novelty of this method is based on the correspondence between the variation of the parameters and the internal changes of the unit when working in real conditions, that is, outside a test bench.

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