Toward Plug&amp;Play Cyber-Physical System Components

Jirkovský, Václav; Obitko, Marek; Kadera, Petr; Mařı́k, Vladimı́r

doi:10.1109/tii.2018.2794982

Cited by 41 publications

(16 citation statements)

References 18 publications

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“…Flexible and reconfigurable manufacturing systems and the ability of products to control their manufacturing processes are the key ideas of Industry 4.0 [Jirkovsky et al, 2018]. The ability of autonomous AI systems to autonomously react on events and make planning in combination with learning from own experience will become the key features of the future development in Industry 5.0.…”

Section: Resultsmentioning

confidence: 99%

System engineering view on multi-agent technology for industrial applications: barriers and prospects

Gorodetsky

Skobelev

2020

CAP

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For over a quarter of a century, multi-agent systems have been considered as one of the most promising technologies for conceptualization, software development and implementation of Artificial Intelligence (AI) solutions. Not so widely but multi-agent technology is also considered as a way of designing complex adaptive systems based on bio-inspired principles of selforganization and evolution. However, in practice, the industry rarely uses multi-agent technology, despite the appearance of new classes of applications for which it is the perfect match, for example, smart cyber-physical systems with digital twins of controlled objects. The paper analyzes the recent anticipations and real achievements in the practical use of multi-agent systems at the industry level. It also identifies the engineering problems that currently impede the extensive industrial implementation of multi-agent systems and technologies as well as the ways to overcome them. Finally, prospects for development of these technologies are evaluated up to the level of industrial implementation.

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Section: Resultsmentioning

confidence: 99%

System engineering view on multi-agent technology for industrial applications: barriers and prospects

Gorodetsky

Skobelev

2020

CAP

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“…Due to the fact that semantic data modeling usually deals with data irregularity and diversity, sophisticated dynamic modeling methods have been derived [338]. With regards to IIoT and ICPS, OPC-UA and semantic web technologies are able to achieve integration at various levels [345]. UML-based approaches can fully automate the generation process of the IIoT-compliant layer that is required for the cyber-physical components to be effectively integrated in the shop-floor [333].…”

Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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“…Kumaravel et al [38] utilised a multi-domain layered ontology model to excavate the key information from unstructured documents and build thermal power plant ontology, which could be extended to any industry by integrating appropriate domain ontology with the domains. To obtain the general description of complex systems, Jirkovský et al [39] used the semantic sensor network ontology to describe the cyber-physical system (CPS) from the perspective of component. To detect security intrusion or attacks of the power Internet of Things and cloud (IoT-Cloud), Choi et al [40] devised an ontology-based security context reasoning method to improve the security service.…”

Section: Ontology Learningmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

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With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

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Toward Plug&Play Cyber-Physical System Components

Cited by 41 publications

References 18 publications

System engineering view on multi-agent technology for industrial applications: barriers and prospects

System engineering view on multi-agent technology for industrial applications: barriers and prospects

Data Management in Industry 4.0: State of the Art and Open Challenges

A survey on the development status and application prospects of knowledge graph in smart grids

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