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Huafei Yang

dblp:57/1521 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge-based systems
0.112009
A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation · IEEE Trans. Pattern Anal. Mach. Intell. 2009

Methods — techniques the papers use, named apart from their topics

depth-first search · 0.1EBNF parsing · 0.1
YearPublicationVenuePosition
2009 A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation
abstract
We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.
Tong Lu 0002, Chiew-Lan Tai, Huafei Yang, Shijie Cai
IEEE Trans. Pattern Anal. Mach. Intell.3
2007 Automatic analysis and integration of architectural drawings
Huafei Yang, Ruoyu Yang, Shijie Cai
Int. J. Document Anal. Recognit.2