From artificial cognitive systems and open architectures to cognitive manufacturing systems

Iarovyi, Sergii; Lastra, José L. Martínez; Haber, Rodolfo E.; Toro, Raúl del

doi:10.1109/indin.2015.7281910

Cited by 55 publications

(28 citation statements)

References 37 publications

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“…Some of the QoS improvements are related to new sensing services ( [18], [19]), while others focus on automated configuration and re-configurability for QoS improvement ( [20] and [21]). Other studies focus on flexibility enhancement ( [20], [22]), integration, efficiency, agility, and few others. Optimization is claimed to be the main strength in 7 studies.…”

Section: Rq4 What Are the Strengths And Limitations Of Proposed Apprmentioning

confidence: 99%

“…Study [33] points out the complexity on CPS time management, and argues for improvements. Study [22] reports on the lack of systematic procedure for learning and optimization in shared circuits modeling. The need of better mechanisms for interoperability is advocated in [34], while [35] reports a need for more comprehensive approaches for testing CPS.…”

Section: Rq4 What Are the Strengths And Limitations Of Proposed Apprmentioning

confidence: 99%

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Self-adaptation for cyber-physical systems

Muccini

Sharaf

Weyns

2016

Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

106

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Context: Cyber-physical systems (CPS) seamlessly integrate computational and physical components. Adaptability, realized through feedback loops, is a key requirement to deal with uncertain operating conditions in CPS.Objective: We aim at assessing state-of-art approaches to handle self-adaptation in CPS at the architectural level.Method: We conducted a systematic literature review by searching four major scientific data bases, resulting in 1103 candidate studies and eventually retaining 42 primary studies included for data collection after applying inclusion and exclusion criteria.Results: The primary concerns of adaptation in CPS are performance, flexibility, and reliability. 64% of the studies apply adaptation at the application layer and 24% at the middleware layer. MAPE (Monitor-Analyze-Plan-Execute) is the dominant adaptation mechanism (60%), followed by agents and self-organization (both 29%). Remarkably, 36% of the studies combine different mechanisms to realize adaptation; 17% combine MAPE with agents. The dominating application domain is energy (24%).Conclusions: Our findings show that adaptation in CPS is a cross-layer concern, where solutions combine different adaptation mechanisms within and across layers. This raises challenges for future research both in the field of CPS and self-adaptation, including: how to map concerns to layers and adaptation mechanisms, how to coordinate adaptation mechanisms within and across layers, and how to ensure system-wide consistency of adaptation.

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Section: Rq4 What Are the Strengths And Limitations Of Proposed Apprmentioning

confidence: 99%

Section: Rq4 What Are the Strengths And Limitations Of Proposed Apprmentioning

confidence: 99%

Self-adaptation for cyber-physical systems

Muccini

Sharaf

Weyns

2016

Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

106

View full text Add to dashboard Cite

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“…Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment.…”

Section: Introductionmentioning

confidence: 99%

“…The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment.…”

mentioning

confidence: 99%

“…The agent technology is a key technology for realizing the autonomous behaviors of CPS [55,[77][78][79]. For improving the autonomous characteristics and intelligence of agents, architectures of cognitive system [3,56,58,80] have been studied and intelligent agents, namely cognitive agents, are proposed [81]. Currently, application of cognitive agents in a smart grid have been applied and shown efficiently [57,82].…”

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confidence: 99%

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Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Tran

Park²,

Nguyen

et al. 2019

Applied Sciences

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Featured Application: A proposed smart manufacturing system has the ability to adapt to manufacturing changes. Abstract:The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment. To face this challenge, the new trend of manufacturing system development is to apply autonomous behaviors inspired from living systems and nature to have the sustainable manufacturing systems. Industry 4.0 enables the realization of sustainable manufacturing using the ubiquitous information and communication technology (ICT) infrastructure [4]. National strategies for developing smart manufacturing systems are focused on by both developing and developed countries [5,6]. The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20]...

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Bionic Banking Project

Nicoletti

2022

Beyond Fintech

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From artificial cognitive systems and open architectures to cognitive manufacturing systems

Cited by 55 publications

References 37 publications

Self-adaptation for cyber-physical systems

Self-adaptation for cyber-physical systems

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Bionic Banking Project

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