Knowledge Representation and Modelling: Structures and Trade-Offs

Minku, Leandro L.; Esterle, Lukas; Nebehay, Georg; Chen, Renzhi

doi:10.1007/978-3-319-39675-0_6

Cited by 2 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The differential equations were rearranged as discrete-time functions using the numerical integration method with i k and ω k as the armature current and rotor speed outputs of the model at instant k, respectively. The differential equations of i k and ω k are (11) and (12), respectively. The inputs are the armature voltage (V a ) and the load torque (T load ), the previous values of current (i k−1 ) and speed (ω k−1 ), and the integration time step.…”

Section: Motor Modelmentioning

confidence: 99%

“…Self-aware computing, which is considered a promising area of study and may become the future of computational systems, focuses on systems capable of seeking and maintaining knowledge about themselves, as well as learning and reasoning about what is being done so that they can express themselves or achieve their goals [ 10 , 11 , 12 ]. In turn, self-aware systems can bring greater efficiency to industrial environments, increasing adaptability and reducing production downtime due to maintenance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Interpretable Digital Twin for Self-Aware Industrial Machines

Vilar-Dias,

Junior,

Lima-Neto

2023

Sensors

View full text Add to dashboard Cite

This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system. Our methodology, as exemplified through DC Motor and Hydraulic Actuator simulations, underscores the potential of Digital Twins to augment the self-awareness of industrial machines. The results indicate that our approach can proficiently optimize system parameters in real-time and unveil previously unknown components, thereby enhancing the adaptive capacities of the Digital Twin. While the reliance on accurate data to develop Digital Twin models is a notable consideration, the proposed methodology serves as a promising framework for advancing the efficiency of industrial applications. It further extends its relevance to fault detection and system control. Central to our approach is the emphasis on interpretability, ensuring a more transparent understanding and effective usability of such systems.

show abstract

Section: Motor Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Interpretable Digital Twin for Self-Aware Industrial Machines

Vilar-Dias,

Junior,

Lima-Neto

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Finally, new ways of realising aspects of self-awareness are being proposed. In addition to our own approach to the architecture of self-aware systems [41], efforts exist to collate common features of self-aware systems [60], as well as common learning techniques for realising self-awareness in its various forms [61]. Meanwhile, the issue of collective self-awareness in distributed systems has been explored, using mechanisms based on hierarchies of self-aware components [62], [63].…”

Section: State Of the Artmentioning

confidence: 99%

Self-aware computing systems: From psychology to engineering

Lewis

2017

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2017

View full text Add to dashboard Cite

At the current time, there are several fundamental changes in the way computing systems are being developed, deployed and used. They are becoming increasingly large, heterogeneous, uncertain, dynamic and decentralised. These complexities lead to behaviours during run time that are difficult to understand or predict. One vision for how to rise to this challenge is to endow computing systems with increased self-awareness, in order to enable advanced autonomous adaptive behaviour. A desire for self-awareness has arisen in a variety of areas of computer science and engineering over the last two decades, and more recently a more fundamental understanding of what selfawareness concepts might mean for the design and operation of computing systems has been developed. This draws on selfawareness theories from psychology and other related fields, and has led to a number of contributions in terms of definitions, architectures, algorithms and case studies. This paper introduces some of the main aspects of self-awareness from psychology, that have been used in developing associated notions in computing. It then describes how these concepts have been translated to the computing domain, and provides examples of how their explicit consideration can lead to systems better able to manage tradeoffs between conflicting goals at run time in the context of a complex environment, while reducing the need for a priori domain modelling at design or deployment time.

show abstract

Knowledge Representation and Modelling: Structures and Trade-Offs

Cited by 2 publications

References 1 publication

An Interpretable Digital Twin for Self-Aware Industrial Machines

An Interpretable Digital Twin for Self-Aware Industrial Machines

Self-aware computing systems: From psychology to engineering

Contact Info

Product

Resources

About