Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system

Bombardier, Vincent; Mazaud, Cyril; Lhoste, Pascal; Vogrig, Raphaël

doi:10.1016/j.compind.2006.07.006

Cited by 28 publications

(23 citation statements)

References 15 publications

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“…Vision systems using such sensors have good performances regarding surface analysis, as well as production rate as recognition rate. On sawing product, it is possible to accurately detect, localize and measure wood defects such as black knots, sound knots, resin pockets, compression wood, colored wood, blue stain or wanes [Deng J.D., 2007;Bombardier V., 2007]. Results obtained on surface analysis with camera are equal to ones obtained with X rays scanner.…”

Section: Data Acquisition Systemsupporting

confidence: 50%

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Modeling Quality knowledge to design log sorting system by X rays tomography

Almecija

Bombardier

Charpentier

2012

IFAC Proceedings Volumes

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Abstract:The aim of this paper is to highlight the advantage of using human (expert) knowledge and know-how to design an automated quality control system. This study is based on an industrial case of a French sawmill. The modeling deals with qualitative analyses of round wood and sawn timber in order to tune X rays scanner system. NIAM/ORM method is used to model quality expert knowledge. It enables to establish existing or missing links in one quality or between different levels of quality. This model will improve the optimization of one or several decision methods based on experts' knowledge of the company.

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Section: Data Acquisition Systemsupporting

confidence: 50%

“…Our objective is to integrated knowledge in the decision system, as done in [Bombardier V., 2007]. The decision system becomes a useful support for the production operator who takes the final decision.…”

Section: Introductionmentioning

confidence: 99%

Modeling Quality knowledge to design log sorting system by X rays tomography

Almecija

Bombardier

Charpentier

2012

IFAC Proceedings Volumes

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“…Knowledge acquisition in fuzzy systems can either be from human experts or data-driven (Bombardier et al, 2007;Zajaczkowski & Verma, 2012;Zhang & Mahfouf, 2011). The human expert approach lends itself to a manual design of fuzzy models based on existing knowledge retrieved from an expert through interviews and open questions (Fay, 2000).…”

Section: Fuzzy Rule-based Systemmentioning

confidence: 99%

“…Java programming language has also been used to develop a software tool which supports the design and computation of recurrent fuzzy systems (Nurnberger, 2004). Natural language information analysis method and object role modelling have been applied in creating symbolic fuzzy models representing customer knowledge in a defect recognition system (Bombardier et al, 2007). Definition of membership functions and values captured from human experts, can also be represented using MATLAB fuzzy logic toolbox simulator (Celikyilmaz & Turksen, 2008;Guimaraes & Lapa, 2007;Jafelice et al, 2009).…”

Section: Fuzzy Rule-based Systemmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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“…Hence, ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. They are a popular research topic in various communities, such as knowledge engineering (Borst et al, 1997) (Bellandi et al, 2006), cooperative information systems (Diamantini et al, 2006b), information integration (Bolloju et al, 2002) (Perez-Rey et al, 2006), software agents (Bombardier et al, 2007), and knowledge management (Bernstein et al, 2005) (Cardoso and Lytras, 2009). In general, ontologies provide (Fensel et al, 2000): a shared and common understanding of a domain which can be communicated amongst people and across application systems; and, an explicit conceptualization (i.e., meta information) that describes the semantics of the data.…”

Section: Motivationmentioning

confidence: 99%