Cyber Physical Computing

Hahanov, Vladimir; Hussein, Mazen Abdelrahman Abdelaziz; Hahanova, Anna; Man, Ka Lok

doi:10.1007/978-3-319-54825-8_1

Cited by 4 publications

(2 citation statements)

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“…Методы технической диагностики [1][2][3][4][5][6] повсеместно используются на всех этапах жизненного цикла устройств и систем автоматического управления . На этапе разработки устройства или системы, например, это выражается в тестировании и верификации аппаратных и программных компонентов, в выборе и обосновании способа реализации подсистем внутреннего контроля и диагностирования и пр .…”

Section: • введениеunclassified

Integration of Control and Monitoring Systems

Ефанов

2020

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Stationary systems for monitoring devices of train traffic control are currently being implemented as external means of technical diagnostics and are located mainly centrally. The set of diagnostic parameters is scarce, and most measurements are indirect. This, ultimately, leads to low efficiency of monitoring systems, and the share of useful information from the total amount of data according to experts does not exceed 5 %. The development of monitoring technologies should follow the path of integration of measurement and control functions. The objective of the work is to draw the attention of the scientific community to the principles of monitoring and control systems implementation and the transition from their disunity to integration. Using methods of technical diagnostics and monitoring, it is proposed to switch to more advanced control systems with built-in means of troubleshooting and monitoring. The author has proposed the concept of integrated technical diagnostic tools with object controllers in the form of removable monitoring modules that transmit data along dedicated diagnostic transmission paths. Depending on the geographical location of the controller (centralized at the control station or decentralized near the controlled object), a set of diagnostic parameters is determined and a choice of diagnostic information processing methods is carried out. With decentralized location of controllers, the diagnostic modules can transmit diagnostic information from external and distributed sensors at railway infrastructure facilities. Implementation of the presented concept will allow receiving a much larger amount of initial data for operation of monitoring systems, including transition to obtaining digital twins of railway infrastructure facilities. In a broader sense, when organizing monitoring systems, it is necessary to focus not only on railway automation, but also pay attention to other infrastructure facilities serviced by personnel of adjacent sectors. Since all objects function together, such an approach to organization of monitoring will improve quality of diagnosis and prognosis, as well as provide an opportunity to assess the residual resource of technical objects.

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Section: • введениеunclassified

Integration of Control and Monitoring Systems

Ефанов

2020

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“…These systems will eventually show largely diverse levels of intellectualization. Evidently, implementation of smartness is inseparable from the realization of cyber-physical computing (CPC) that intends to go beyond the classical (predefined explicit algorithms-based) computation that is underpinned by the von Neumann theory of computing [19]. CPC complements or replaces predefined flows of computation with run-time devised flows, and appends run-time constructed or acquired (hidden, but learnable) implicit algorithms to the (preprogrammed) explicit algorithms.…”

Section: An Evolutionary View On Cyber-physical Systemsmentioning

confidence: 99%

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

Horváth

2018

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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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