Robust Team-Triggered Coordination of Networked Cyberphysical Systems

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

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Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

Modelling ◽

10.3390/modelling2010003 ◽

2021 ◽

Vol 2 (1) ◽

pp. 43-62

Author(s):

Kshirasagar Naik ◽

Mahesh D. Pandey ◽

Anannya Panda ◽

Abdurhman Albasir ◽

Kunal Taneja

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Nuclear Power ◽

Finite State Machine ◽

State Machine ◽

Plant Performance ◽

Series Data ◽

Cyberphysical Systems ◽

Finite State ◽

Machine Representation

Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

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Formal Verification of Cyberphysical Systems

Computer ◽

10.1109/mc.2021.3055883 ◽

2021 ◽

Vol 54 (9) ◽

pp. 15-24

Author(s):

James Bret Michael ◽

Doron Drusinsky ◽

Duminda Wijesekera

Keyword(s):

Formal Verification ◽

Cyberphysical Systems

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Formal Methods in Cyberphysical Systems

Computer ◽

10.1109/mc.2021.3089267 ◽

2021 ◽

Vol 54 (9) ◽

pp. 25-29

Author(s):

James Bret Michael ◽

Doron Drusinsky ◽

Duminda Wijesekera

Keyword(s):

Formal Methods ◽

Cyberphysical Systems

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Model-Based Control Design and Integration of Cyberphysical Systems: An Adaptive Cruise Control Case Study

Journal of Control Science and Engineering ◽

10.1155/2013/678016 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 17

Author(s):

Emeka Eyisi ◽

Zhenkai Zhang ◽

Xenofon Koutsoukos ◽

Joseph Porter ◽

Gabor Karsai ◽

...

Keyword(s):

Adaptive Cruise Control ◽

Control Design ◽

Systematic Design ◽

Cyberphysical Systems ◽

Cruise Control ◽

Automotive Control ◽

Experimental Platform ◽

Complex Interactions ◽

Systematic Methodology

The systematic design of automotive control applications is a challenging problem due to lack of understanding of the complex and tight interactions that often manifest during the integration of components from the control design phase with the components from software generation and deployment on actual platform/network. In order to address this challenge, we present a systematic methodology and a toolchain using well-defined models to integrate components from various design phases with specific emphasis on restricting the complex interactions that manifest during integration such as timing, deployment, and quantization. We present an experimental platform for the evaluation and testing of the design process. The approach is applied to the development of an adaptive cruise control, and we present experimental results that demonstrate the efficacy of the approach.

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Understanding and Fixing Complex Faults in Embedded Cyberphysical Systems

Computer ◽

10.1109/mc.2020.3029975 ◽

2021 ◽

Vol 54 (1) ◽

pp. 49-60

Author(s):

Alexander Weiss ◽

Smitha Gautham ◽

Athira Varma Jayakumar ◽

Carl R. Elks ◽

D. Richard Kuhn ◽

...

Keyword(s):

Cyberphysical Systems

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Towards scalable and reusable predictive models for cyber twins in manufacturing systems

Journal of Intelligent Manufacturing ◽

10.1007/s10845-021-01804-0 ◽

2021 ◽

Author(s):

Cinzia Giannetti ◽

Aniekan Essien

Keyword(s):

Data Streams ◽

Predictive Models ◽

Manufacturing Systems ◽

Model Building ◽

Model Performance ◽

Cyberphysical Systems ◽

Pragmatic Approach ◽

Analyse Data ◽

Fully Connected ◽

Smart Factories

AbstractSmart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories.

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