Including human tasks as semantic resources in manufacturing ontology models

Ferrer, Borja Ramis; Mohammed, Wael M.; Lobov, Andrei; Galera, Amalia Moreno; Lastra, José L. Martínez

doi:10.1109/iecon.2017.8216587

Cited by 19 publications

(6 citation statements)

References 36 publications

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“…There are many KR and R formalisms used to implement semantic models, such as ontologies, databases and semantic rules. The former formalism is getting attraction in several fields, such as in the industrial automation field [122,123], needing to build semantic models including information from different and interrelated concepts. One of the major benefits of using such technic is the possibility of adding a layer of reasoning in order to discover implicit knowledge from the explicit data graphs that are fed to the model.…”

Section: Artificial Intelligence: Machine Learning (Ml) and Knowledgementioning

confidence: 99%

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

2019

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This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU–China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.

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Section: Artificial Intelligence: Machine Learning (Ml) and Knowledgementioning

confidence: 99%

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

2019

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“…For example, dynamic orchestration of semantic web services is used in [12] to solve the reconfiguration task at runtime. A human-in-the-loop solution for automatic production line assembly is presented in [13], with the aim of seamlessly including and assisting human operators. In [14], the authors propose a visual programming tool that allows the user to specify the desired state of a smart environment graphically.…”

Section: Related Workmentioning

confidence: 99%

Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

Mihailescu

Kyriakou²,

Papangelis³

2020

IoT

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In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup.

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“…device vendors, equipment vendors, system integrators, standardization institutions and customers cooperate using a common language -the Web Ontology Language (OWL) 1 -for building a manufacturing system. The ontologies can be organized into three main categories that of Product, Process and Resource (that also includes, for instance, equipment) [21]. The Process ontology can be seen as a pivotal point to join product and resource ontologies, i.e.…”

Section: State Of the Artmentioning

confidence: 99%

“…https://docs.oracle.com/javaee/6/tutorial/doc/gijqy.html 2 https://tools.ietf.org/html/rfc2068 "Instrumentation Engineering, Electronics and Telecommunications -2019" Proceedings of the V International Forum (Izhevsk, Russia, November[20][21][22] 2019) …”

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confidence: 99%

Towards a web-based framework for computer-aided manufacturing

Lobov¹,

Martikkala²,

Biswas³

2019

Instrumentation Engineering, Electronics and Telecommunications

Self Cite

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Computer-aided manufacturing (CAM) helps to see how manufacturing processes could be executed checking if desired products can be made and at what cost. Usually, a 3D model of a targeted product is used as one of the inputs for CAM. Although actively used for some application domains and despite of well-developed CAM technologies and tools frameworks, further acceptance of CAM is hindered by, on one hand, a restricted access and, on another, knowledge needed to use CAM tools and methods. This article outlines a concept for web-based integration framework architecture for CAM. It defines main stakeholders and their roles in efficient product development and virtual commissioning.

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Including human tasks as semantic resources in manufacturing ontology models

Cited by 19 publications

References 36 publications

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

Towards a web-based framework for computer-aided manufacturing

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