Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing

Nowadays, smart manufacturing has attracted more and more interesting and attentions of researchers. As an important prerequisite for smart manufacturing, the cyber-physical integration of manufacturing is becoming more and more important. Cyber-physical systems (CPS) and digital twin (DT) are the preferred means to achieve the interoperability and integration between the physical and cyber worlds. From the perspective of hierarchy, CPS and DT can be divided into unit level, system level, and SoS (system of system) level. To meet the different requirements of each level, the following three complementary technologies, i.e., edge computing, fog computing and cloud computing, are instrumental to accelerate the development of various CPS and DT. In this article, the perspectives of unit-level, system-level, and SoS-level of CPS and DT supported by edge computing, fog computing and cloud computing are discussed.

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Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010018 ◽

2021 ◽

Vol 10 (1) ◽

pp. 18

Author(s):

Quentin Cabanes ◽

Benaoumeur Senouci ◽

Amar Ramdane-Cherif

Keyword(s):

Neural Network ◽

Deep Learning ◽

Computation Time ◽

Physical World ◽

Cyber Physical Systems ◽

Physical Systems ◽

Research Technology ◽

Standard Design ◽

Hardware Architectures ◽

Voxel Grid

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

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A Blockchain-based Deep Learning Approach for Cyber Security in Next Generation Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3040968 ◽

2020 ◽

pp. 1-1

Author(s):

Shailendra Rathore ◽

J. H. Park

Keyword(s):

Deep Learning ◽

Cyber Security ◽

Cyber Physical Systems ◽

Learning Approach ◽

Next Generation ◽

Physical Systems

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Optimization of energy efficiency in smart manufacturing through the application of cyber-physical systems and industry 4.0 technologies - A systematic review

10.26678/abcm.cobef2021.cob21-0259 ◽

2021 ◽

Author(s):

Fernando Matsunaga ◽

Pablo Deivid Valle ◽

Fernando Deschamps

Keyword(s):

Systematic Review ◽

Energy Efficiency ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Smart Manufacturing ◽

Physical Systems

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Validity Frame Supported Digital Twin Design of Complex Cyber-Physical Systems

10.23919/annsim52504.2021.9552100 ◽

2021 ◽

Author(s):

Bert Van Acker ◽

Joost Mertens ◽

Paul De Meulenaere ◽

Joachim Denil

Keyword(s):

Cyber Physical Systems ◽

Twin Design ◽

Physical Systems ◽

Digital Twin

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IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation

Energies ◽

10.3390/en13184762 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4762 ◽

Cited By ~ 1

Author(s):

Ahmed Saad ◽

Samy Faddel ◽

Osama Mohammed

Keyword(s):

Power Systems ◽

Systems Design ◽

Cyber Physical Systems ◽

Superior Performance ◽

Physical Systems ◽

Digital Twin ◽

Power And Control ◽

Amazon Web Services ◽

High Bandwidth ◽

And Control

With the emergence of distributed energy resources (DERs), with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system. The digital twin (DT) is a new concept that can unleash tremendous opportunities and can be used at the different control and security levels of power systems. This paper provides a methodology for the modelling of the implementation of energy cyber-physical systems (ECPSs) that can be used for multiple applications. Two DT types are introduced to cover the high-bandwidth and the low-bandwidth applications that need centric oversight decision making. The concept of the digital twin is validated and tested using Amazon Web Services (AWS) as a cloud host that can incorporate physical and data models as well as being able to receive live measurements from the different actual power and control entities. The experimental results demonstrate the feasibility of the real-time implementation of the DT for the ECPS based on internet of things (IoT) and cloud computing technologies. The normalized mean-square error for the low-bandwidth DT case was 3.7%. In the case of a high-bandwidth DT, the proposed method showed superior performance in reconstructing the voltage estimates, with 98.2% accuracy from only the controllers’ states.

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