Parallel Bipartite Graph Algorithm for Protein Structure Matching Using OpenMP

Othman, Fadil; Umar, Ibrahim; Abdullah, Rosni; Rathi, A.

doi:10.1109/iccrd.2010.58

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[14] propose a general-purpose approach, whereas specific applications exist for the purpose of expert matching [15], prevention of radicalization [16] and social network analysis [17]. Examples of other applications include identification of persons in surveillance systems [18], protein structure matching [19], crime matching [20], ontology matching [21] and remote sensing [22]. Applications of graph matching to industrial process design documents are limited.…”

Section: Related Researchmentioning

confidence: 99%

Generating an industrial process graph from 3D pipe routing information

Sierla

Azangoo

Vyatkin

2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

View full text Add to dashboard Cite

The automatic generation of digital twins of industrial processes requires the integration of several sources of information. If the twin is expected to accurately capture thermo-hydraulic phenomena, dimensions of tanks and other process components as well as detailed pipe routing information is relevant. Such information is not comprehensively captured in P&IDs (Piping & Instrumentation Diagrams), but it is available from 3D CAD models. However, information about control loops is not available from 3D CAD models, but is available from P&IDs. Previous research has demonstrated the extraction of such information from machine-readable P&IDs and 3D CAD models and converting this information to graphs. Further research is expected on applying graph matching methods for integrating these separate graphs to a common graph-based data structure that captures all of the desired information. This common model could support further work to develop digital twins. A major obstacle to this is that the graphs that have currently been generated from P&IDs and 3D CAD models are at very different abstraction levels, so graph matching methods are not feasible. This article address this obstacle by building on previous work, in which graphs were generated from P&IDs and 3D CAD models. The contribution of this paper is several novel algorithms for preprocessing a 3D CAD generated graph, until it is at the same level of abstraction as a P&ID generated graph of the same industrial process. The algorithms are demonstrated in the context of a laboratory process.

show abstract