Fundamental methodological issues of syntactic pattern recognition

Flasiński, Mariusz; Jurek, Janusz

doi:10.1007/s10044-013-0322-1

Cited by 15 publications

(6 citation statements)

References 49 publications

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“…In general, systems for feature extraction and recognition from different CAD files combine information which is collected at a relatively low level (points, lines and curves) and convert them into features (holes, chamfers, slots, cylinders) [ 7 , 16 , 17 ]. Feature recognition methods can be divided into five areas [ 3 , 7 , 18 , 19 ]: (1) syntactic pattern recognition [ [20] , [21] , [22] , [23] , [24] , [25] , [26] ], (2) graph-based recognition [ 6 , 19 , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] ], (3) logic rule-based recognition [ [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] ], (4) hint-based recognition [ 32 , 38 , [44] , [45] , [46] ] and (5) artificial neural nets [ 18 , 32 , [47] , [48] , [49] , [50] ].…”

Section: Related Workmentioning

confidence: 99%

Geometric feature extraction in manufacturing based on a knowledge graph

Köhler,

Song,

Bergmann

et al. 2023

Heliyon

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Section: Related Workmentioning

confidence: 99%

Geometric feature extraction in manufacturing based on a knowledge graph

Köhler,

Song,

Bergmann

et al. 2023

Heliyon

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“…In a recognition process, a pattern is analysed and assigned to a predefined class of features. If the pattern is complex, it is defined in a hierarchical way, whereby primitives are used at the bottom of the hierarchy in order to build simple substructures with symbols [41]. There are three major components in this approach: preprocessing, pattern description or representation, and syntax analysis.…”

Section: Syntactic Pattern Recognitionmentioning

confidence: 99%

“…The implementation of this method lacks success, if it is implemented with rotational parts that have non-turning [42] features or 3D parts with non-axis symmetry. Because of this weakness, it has been replaced by newer techniques that have overcome these limitations [41,46].…”

Section: Syntactic Pattern Recognitionmentioning

confidence: 99%

A survey on smart automated computer-aided process planning (ACAPP) techniques

Al-wswasi

Ivanov

Makatsoris

2018

Int J Adv Manuf Technol

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The concept of smart manufacturing has become an important issue in the manufacturing industry since the start of the twenty-first century in terms of time and production cost. In addition to high production quality, a quick response could determine the success or failure of many companies and factories. One the most effective concepts for achieving a smart manufacturing industry is the use of computer-aided process planning (CAPP) techniques. Computer-aided process planning refers to key technology that connects the computer-aided design (CAD) and the computer-aided manufacturing (CAM) processes. Researchers have used many approaches as an interface between CAD and CAPP systems. In this field of research, a lot of effort has been spent to take CAPP systems to the next level in the form of automatic computer-aided process planning (ACAPP). This is to provide complete information about the product, in a way that is automated, fast, and accurate. Moreover, automatic feature recognition (AFR) techniques are considered one of the most important tasks to create an ACAPP system. This article presents a comprehensive survey about two main aspects: the degree of automation in each required input and expected output of computer-aided process planning systems as well as the benefits and the limitations of the different automatic feature recognition techniques. The aim is to demonstrate the missing aspects in smart ACAPP generation, the limitations of current systems in recognising new features, and justifying the process of selection.

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“…Little is known about how to search for, or identify with high precision, critically important non--semantic similarities. Computer science and its allied domains have developed experimental techniques for concept extraction and topic modeling such as latent semantic analysis, latent dirichlet analysis, syntactic pattern recognition-which can be characterized as attempts to use patterns among terms occurring in documents (see for example Flasiński and Jurek 2014). Likewise, computational linguists have developed domain ontology engineering tools (e.g., Navigli and Velardi 2004).…”

Section: Knowledge Interaction For Interfacing Systemsmentioning

confidence: 99%

Producing Navigable Knowledge Organization with Knowledge Interaction

Smiraglia

Beak

2017

NASKO

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Knowledge interaction combines concept theory, instantiation theory, and the taxonomy of knowledge elements to suggest that knowledge organization systems might be used effectively to integrate different dimensional representations of information objects. Understanding knowledge structurally as well as semantically can lead to a variety of implementations that might provide temporal interfaces for understanding relationships among information objects that are not obviously semantically related. An experimental test--bed would rely on the actual experience of working scholars. Preliminary results come from a case study of the works of one prolific New Testament scholar whose works are available in digital form. We see clearly the distance between the theological positions, sociological interpretive positions, and methodological positions, indicating three interacting intellectual poles in this scholar's writing.

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Fundamental methodological issues of syntactic pattern recognition

Cited by 15 publications

References 49 publications

Geometric feature extraction in manufacturing based on a knowledge graph

Geometric feature extraction in manufacturing based on a knowledge graph

A survey on smart automated computer-aided process planning (ACAPP) techniques

Producing Navigable Knowledge Organization with Knowledge Interaction

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