Information schema constructs for instantiation and composition of system manifestation features

Pourtalebi, Shahab; Horváth, Imre

doi:10.1631/fitee.1601235

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Compared to traditional products, the design of CPSS devices requires engineers to have a better understanding of the system level behaviors, as well as the new methodology for the optimization at the network scale. Systems level modeling methods and tools have been developed for CPSS design and analysis, such as hybrid discrete-event and continuous simulations (Jeon et al, 2012;Lee et al, 2015aLee et al, , 2015b, inductive constraint logic programming (Saeedloei and Gupta, 2011) abductive reasoning (Horváth, 2019), hybrid timed automaton (Burmester et al, 2012), ontologies (Petnga and Austin, 2016), information schema (Pourtalebi and Horváth, 2017), UML (Magureanu et al, 2010), and SysML (Palachi et al, 2013).…”

Section: Cpss System-of-systems Level Designmentioning

confidence: 99%

Topology-informed information dynamics modeling in cyber–physical–social system networks

Wang

2021

AIEDAM

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Cyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

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Section: Cpss System-of-systems Level Designmentioning

confidence: 99%

Topology-informed information dynamics modeling in cyber–physical–social system networks

Wang

2021

AIEDAM

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“…The proposed SMF-TB imposes a strictly physical view, i.e., it models system constituents in the 3D Euclidian space and captures their relationships as mereotopological relations (rather than just representing them by abstract modeling surrogates). The ISCs for: (i) representation of operation and architecture relations, (ii) assigning values to parameter variables, (iii) managing the meta-level knowledge-base of the model warehouse, (iv) recording the composition and parametrization history, and (v) recording the state, event and stream unification history of the instantiation and composition of system constituents are discussed in [78]. The overall process of The proposed SMF-TB imposes a strictly physical view, i.e., it models system constituents in the 3D Euclidian space and captures their relationships as mereotopological relations (rather than just representing them by abstract modeling surrogates).…”

Section: System-level Feature-based Conceptualization and Modeling Ofmentioning

confidence: 99%

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

Horváth

2018

Designs

Self Cite

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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.

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Design of Trustworthy Cyber–Physical–Social Systems With Discrete Bayesian Optimization

Wang

2021

Journal of Mechanical Design

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Cyber–physical–social systems (CPSS) with highly integrated functions of sensing, actuation, computation, and communication are becoming the mainstream consumer and commercial products. The performance of CPSS heavily relies on the information sharing between devices. Given the extensive data collection and sharing, security and privacy are of major concerns. Thus, one major challenge of designing those CPSS is how to incorporate the perception of trust in product and systems design. Recently, a trust quantification method was proposed to measure the trustworthiness of CPSS by quantitative metrics of ability, benevolence, and integrity. The CPSS network architecture can be optimized by choosing a subnet such that the trust metrics are maximized. The combinatorial network optimization problem, however, is computationally challenging. Most of the available global optimization algorithms for solving such problems are heuristic methods. In this paper, a surrogate-based discrete Bayesian optimization method is developed to perform network design, where the most trustworthy CPSS network with respect to a reference node is formed to collaborate and share information with. The applications of ability and benevolence metrics in design optimization of CPSS architecture are demonstrated.

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Information schema constructs for instantiation and composition of system manifestation features

Cited by 4 publications

References 20 publications

Topology-informed information dynamics modeling in cyber–physical–social system networks

Topology-informed information dynamics modeling in cyber–physical–social system networks

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

Design of Trustworthy Cyber–Physical–Social Systems With Discrete Bayesian Optimization

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