Qualitative Modelling of Physical Systems for Knowledge Based Control

1988 ◽  
pp. 31-51 ◽  
Author(s):  
Roy Leitch
Keyword(s):  
Qualitative Modelling ◽  
Physical Systems ◽  
Knowledge Based

scholarly journals A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Designs ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Imre Horváth
Keyword(s):  
Performance Enhancement ◽  
Cyber Physical Systems ◽  
Comprehensive Description ◽  
Computational Framework ◽  
Learning Mechanism ◽  
Physical Systems ◽  
Knowledge Based ◽  
System Adaptation ◽  
Ampliative Reasoning ◽  
Run Time

To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness of the state and circumstances of operation, (v) devising alternative performance enhancement strategies, (vi) deciding on the best system adaptation, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. Several cognitive algorithms and computational actions are used to implement PA in a compositional manner. PA necessitates not only a synergic interoperation of the algorithms, but also an objective-dependent fusion of the pre-programmed and the run time acquired chunks of knowledge. A fully fledged implementation of PA is underway, which will make verification and validation possible in the context of various smart CPSs.

scholarly journals Knowledge-based cyber-physical systems for assembly automation

2019 ◽  
Vol 7 (1) ◽  
pp. 223-254 ◽  
Author(s):  
Munir Merdan ◽  
Timon Hoebert ◽  
Erhard List ◽  
Wilfried Lepuschitz
Keyword(s):  
Cyber Physical Systems ◽  
Physical Systems ◽  
Knowledge Based ◽  
Assembly Automation

RDR-based knowledge based system to the failure detection in industrial cyber physical systems

2018 ◽  
Vol 150 ◽  
pp. 1-13 ◽  
Author(s):  
Dohyeong Kim ◽  
Soyeon Caren Han ◽  
Yingru Lin ◽  
Byeong Ho Kang ◽  
Sungyoung Lee
Keyword(s):  
Failure Detection ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Knowledge Based System ◽  
Knowledge Based

scholarly journals Knowledge-Based Programming for the Cybersecurity Solution

2018 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Stuart H. Rubin
Keyword(s):  
Sensory Information ◽  
Cost Effective ◽  
Cyber Physical Systems ◽  
Neural Nets ◽  
Automatic Programming ◽  
Physical Systems ◽  
Knowledge Based ◽  
Significant Difference ◽  
System States ◽  
The Cost

Introduction:The problem of cyberattacks reduces to the unwanted infiltration of software through latent vulnerable access points. There are several approaches to protection here. First, unknown or improper system states can be detected through their characterization (using neural nets and/or symbolic codes), then interrupting the execution to run benchmarks and observe if they produce the states they should. If not, the execution can be rewound to the last successful benchmark, all states restored, and rerun.Methods:This will only work for cyber-physical systems that can be rewound. Benchmarks will often include sensory information. The second approach is termed, “semantic randomization”. This is similar to the well-known compiler technique known as “syntactic randomization”. The significant difference is that different variants of the algorithm itself are being automatically programmed. Cyberattacks will generally not be successful at more than one variant. This means that cybersecurity is moving us towards automatic programming as a desirable consequence. Knowledge-Based Software Engineering (KBSE) is the way to achieve automatic programming in practice.Discussion:There is non-determinism in the execution of such systems, which provides cybersecurity. Knowledge-based algorithmic compilers are the ultimate solution for scalable cost-effective cybersecurity. However, unlike the case for the less-secure syntactic randomization, the cost-effectiveness of semantic randomization is a function of scale. A simple randomization-based automatic programming method is illustrated and discussed.Conclusion:Semantic randomization is overviewed and compared against other technologies used to protect against cyberattack. Not only does semantic randomization itself, or in combination with other methodologies, offer improved protection; but, it serves as the basis for a methodology for automatic programming, which in turn makes the semantic randomization methodology for cybersecurity cost-effective.

scholarly journals Generating realistic IoT‐based IDS dataset centred on fuzzy qualitative modelling for cyber‐physical systems

2020 ◽  
Vol 56 (9) ◽  
pp. 441-444
Author(s):  
Izhar Ahmed Khan ◽  
Dechang Pi ◽  
Ajeet Kumar Bhatia ◽  
Nasrullah Khan ◽  
Waqas Haider ◽  
...  
Keyword(s):  
Cyber Physical Systems ◽  
Qualitative Modelling ◽  
Physical Systems
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