Digital Twinning Based Adaptive Development Environment for Automotive Cyber-Physical Systems

Xie, Guoqi; Yang, Kehua; Xu, Cheng; Li, Renfa; Hu, Shiyan

doi:10.1109/tii.2021.3064364

Cited by 27 publications

(11 citation statements)

References 12 publications

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“…With the development of sensor and data processing technology, network physical system-based intelligent manufacturing is the development trend in the manufacturing industry. DTs has always been regarded as the implementation method of network physical system [62][63][64]. Given the complexity and uncertainty of discrete manufacturing plants, carbon emission data integration and automatic low-carbon control of manufacturing systems are two major challenges.…”

Section: Dts Modeling Contributes To Intelligent Manufacturingmentioning

confidence: 99%

Modeling Methods of 3D Model in Digital Twins

Liu¹,

Li²,

Lv³

2023

Computer Modeling in Engineering &Amp; Sciences

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To understand the current application and development of 3D modeling in Digital Twins (DTs), abundant literatures on DTs and 3D modeling are investigated by means of literature review. The transition process from 3D modeling to DTs modeling is analyzed, as well as the current application of DTs modeling in various industries. The application of 3D DTs modeling in the elds of smart manufacturing, smart ecology, smart transportation, and smart buildings in smart cities is analyzed in detail, and the current limitations are summarized. It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles. At the same time, the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data, Internet of Things, Cloud Computing, Arti cial Intelligence, and game technology. Therefore, although some results have been achieved, there are still limitations. This work aims to provide a good theoretical support for the further development of 3D DTs modeling.

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Section: Dts Modeling Contributes To Intelligent Manufacturingmentioning

confidence: 99%

Modeling Methods of 3D Model in Digital Twins

Liu¹,

Li²,

Lv³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…1) Digital Twin: The digital twin has become an expected enabling technology at the application layer to promote the digital conversion and intelligent evolution of the Internet of intelligence. Digital twins build high-fidelity digital-virtual models of physical objects to simulate their behaviors and describe their operating states, thereby providing a solution for realizing cyber-physical integration [165], [166]. Advanced computing and analysis functions in cyberspace have also opened up a bright perspective for it.…”

Section: E Application Layermentioning

confidence: 99%

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Tang,

Yu,

Xie

et al. 2022

Preprint

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The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence.

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“…CPS has the advantages of high integration, efficient collaboration, high self-adaptation, safety and reliability, and has been widely used in smart cities, smart medical care, smart manufacturing and other fields. [4][5][6] Currently, scholars have conducted in-depth research on the theoretical framework of CPS modeling, control and optimization, and proposed a variety of theoretical methods and algorithms, such as data transmission mechanism, 7,8 neural networks 9,10 and information fusions, 11,12 to provide theoretical support for the application of CPS.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data control for cyber physical systems via identifying false data injection attacks

Yu,

Wei,

Jia

et al. 2023

Adaptive Control & Signal

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SummaryIn this paper, we discuss the security control problem of cyber physical systems based on identifying false data injection attacks. First, the unknown adversary randomly attacks the measurement output, a mechanism is set in the sensor to update the measurement output to the constant, called the one‐valued quantized mechanism. Then, the Bienayme–Chebyshev inequality and the one‐valued quantized mechanism are utilized to design an identification algorithm, the estimated value of the false data injection attack and the minimum identification time can be obtained. Furthermore, based on the LMI theory, sampled‐data control gains with and without attacks are obtained to ensure the system mean‐square exponential stable, respectively. And the corresponding maximum sampling period of the controller is obtained. Finally, numerical examples verify the correctness and effectiveness of the theory.

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Digital Twinning Based Adaptive Development Environment for Automotive Cyber-Physical Systems

Cited by 27 publications

References 12 publications

Modeling Methods of 3D Model in Digital Twins

Modeling Methods of 3D Model in Digital Twins

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Sampled‐data control for cyber physical systems via identifying false data injection attacks

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