Graph-based reasoning in collaborative knowledge management for industrial maintenance

Kamsu-Foguem, Bernard; Noyes, Daniel

doi:10.1016/j.compind.2013.06.013

Cited by 53 publications

(26 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main reason is because of the automation of production systems in industrial engineering compounded by the fact that industrial products often have similar characteristics. Thus, it is not uncommon to find experience feedback applications in industrial repairs and maintenance management ( [16], [18]; Potes [23]). While experience feedback process can easily be applied in industrial production/engineering, because most of its tasks/ products are often repetitive and standardized, its application in AEC is very challenging.…”

Section: Discussionmentioning

confidence: 99%

“…Once edited into CoGui, the CoGui knowledge model can be converted into different file/data model formats (e.g., Resource Description Framework (RDF)) for other uses such as in application development. The model can also be enriched with conceptual graph rules and constraints to capture real life situations [17,[35][36][37][38][39][40][41][42][43]. The rules and constraints are based on Concepts Types, Relationship Types and Individuals.…”

Section: Ontology-driven Approach For Knowledge Modeling In Aecmentioning

confidence: 99%

See 1 more Smart Citation

Experience modeling with graphs encoded knowledge for construction industry

Kamsu-Foguem

Abanda

2015

Computers in Industry

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Ontology-driven Approach For Knowledge Modeling In Aecmentioning

confidence: 99%

Experience modeling with graphs encoded knowledge for construction industry

Kamsu-Foguem

Abanda

2015

Computers in Industry

Self Cite

View full text Add to dashboard Cite

“…. .. Based on the performance of a rapid DL (deep learning) method, YOLO, which is one of the most efficient algorithms for objects detection, classification, and tracking [32][33][34][35][36], such implications of EC to video surveillance and the attention value and warning level are displayed in Figure 2.…”

Section: Preliminary Formulationmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

View full text Add to dashboard Cite

Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to +1 , = 1, 2, . . .) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

show abstract

“…significantly influences the manner in which collaborative work takes place, as evidenced by their contribution in the exchange of information and generated knowledge [21,34]. The factual and procedural knowledge are based on the representation of concepts and their relationships.…”

Section: Conceptual Graphs Representationmentioning

confidence: 99%