Towards IEC 61499 Based Distributed Intelligent Automation: Design and Computing Perspectives

Lyu, Guolin; Brennan, Robert W.

doi:10.1109/indin41052.2019.8972153

Cited by 7 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational tools and AI are instrumental in moving beyond traditional experimental trial-and-error methods [40, 41], paving the way for the adoption of intelligent automation, which utilizes AI in automated and digitized processes to aid research design and result analysis [40, 41].…”

Section: Computational Methods For Intelligent Biofabrication: the Te...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The application of computational tools and AI to process design and optimization of biomanufacturing processes allows moving beyond experimental trial-and-error approaches [30, 31]. Intelligent automation applies AI to automated and digitalized processes, supporting research design and analysis of results [30, 31]. AI technologies have the potential to automatically adapt experimental strategies as new data is being generated to maximize information extraction and process improvement efficiency [32].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Bardini

Carlo

2023

Preprint

View full text Add to dashboard Cite

Biofabrication is the generation of biologically functional products from living cells and biomaterials through bioprinting and subsequent maturation processes. Technological and scientific domains are underlying biofabrication ranging from biology to automated manufacturing and culture systems. Among its application domains, Tissue Engineering and Regenerative Medicine poses strict quality requirements for biofabrication, requiring fast and disruptive innovation for processes and products to comply. Innovation in biofabrication is slow and incremental, relying on R&D processes that are inefficient (slow, risky, and costly), ineffective (products have sub-optimal quality), and challenging to replicate. While automation and digitalization support process execution, design, and innovation, they often work as tools for empirical, problem-specific, and operator-dependent approaches. On the other hand, computational approaches support biofabrication process modeling, design, and optimization of experimental activity to improve the quality of processes and products. To express their full potential, computational methods must factor in the biological complexity underlying biofabrication. This review analyzes computational approaches to biofabrication process modeling, design, and optimization with a focus on solutions explicitly accounting for biological complexity.

show abstract

Section: Computational Methods For Intelligent Biofabrication: the Te...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Bardini

Carlo

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the recent survey conducted by authors in [23] and [24], it is highlighted that the IEC 61499 standard adopts the object-oriented programming pattern to design control application and also mention that adapters provide a kind of inheritance similar for FB's to share common interfaces. However, they the functionalities to attain the goals of polymorphism and inheritance.…”

Section: Related Workmentioning

confidence: 99%

Proposing and Prototyping an Extension to the Adapter Concept in the IEC 61499 Standard

Jhunjhunwala

Vyatkin

2022

IEEE Access

View full text Add to dashboard Cite

Component-Design Architecture has been in demand based on the growing needs for modularity and flexibility in the automation industry. IEC 61499 standard, a component-based automation architecture, provides various tools and techniques for automation developers to accommodate the need for flexibility in automation sequences. However, the adapter concept, one of the significant features of the standard, remains untouched and developed since its inclusion in the standard and lacks the utilization of its true potential. In this work, we enhance the adapter concept by proposing the addition of logic into them. This proposition advances the adapter technology and gives the automation standard more capabilities to support higher levels of modularization without the increase of applications complexity.

show abstract

“…Nevertheless, DoE approaches show limitations in supporting complex processes such as biofabrication due to the enormous number of experiments required to map the design space and to build predictive models from data. Computational approaches and Artificial Intelligence (AI) can support the design and optimization of biofabrication processes beyond the limitations of the empirical approach [15,16]. In particular, computational modeling and optimization exploit the power of computation to explore system behavior and to conduct systematic DSE, accounting for the relations between complex parameter configurations and process outputs.…”

Section: Introductionmentioning

confidence: 99%

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Castrignanò

Bardini

Savino

et al. 2023

Preprint

View full text Add to dashboard Cite

Tissue Engineering and Regenerative Medicine aim to replicate and replace tissues for curing disease. However, full tissue integration and homeostasis are still far from reach. Biofabrication is an emerging field that identifies the processes required for generating biologically functional products with the desired structural organization and functionality and can potentially revolutionize the regenerative medicine domain, which aims to use patients' cells to restore the structure and function of damaged tissues and organs. However, biofabrication still has limitations in the quality of processes and products. Biofabrication processes are often improved empirically, but this is slow, costly, and provides partial results. Computational approaches can tap into biofabrication underused potential, supporting analysis, modeling, design, and optimization of biofabrication processes, speeding up their improvement towards a higher quality of products and subsequent higher clinical relevance. This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal protocols for the simulated fabrication of epithelial sheets. The optimization strategy relies on a deep reinforcement learning algorithm, the Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.

show abstract

Towards IEC 61499 Based Distributed Intelligent Automation: Design and Computing Perspectives

Cited by 7 publications

References 40 publications

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

Proposing and Prototyping an Extension to the Adapter Concept in the IEC 61499 Standard

A methodology combining reinforcement learning and simulation to optimize thein silicoculture of epithelial sheets

Contact Info

Product

Resources

About