Machine Learning in Cyber-Physical Systems in Industry 4.0

Cyber-physical systems (CPS) have emerged with development of most great applications in the modern world due to their ability to integrate computation, networking, and physical process. CPS and ML applications are widely used in Industry 4.0, military, robotics, and physical security. Development of ML techniques in CPS is strongly linked according to the definition of CPS that states CPS is the mechanism of monitoring and controlling processes using computer-based algorithms. Optimizations adopted with ML in CPS include domain adaptation and fine tuning of current systems, boosting, introducing more safety and robustness by detection and reduction of vulnerabilities, and reducing computation time in time-critical systems. Generally, ML helps CPS to learn and adapt using intelligent models that are generated from training of large-scale data after processing and analysis.

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Cyber-Physical Systems, Internet of Things, and Big Data in Industry 4.0: Digital Manufacturing Technologies, Business Process Optimization, and Sustainable Organizational Performance

Economics Management and Financial Markets ◽

10.22381/emfm14320193 ◽

2019 ◽

Vol 14 (3) ◽

pp. 23

Keyword(s):

Big Data ◽

Internet Of Things ◽

Organizational Performance ◽

Process Optimization ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Digital Manufacturing ◽

Physical Systems ◽

Manufacturing Technologies ◽

Business Process Optimization

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The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06572-4 ◽

2021 ◽

Vol 113 (7-8) ◽

pp. 2395-2412

Author(s):

Baudouin Dafflon ◽

Nejib Moalla ◽

Yacine Ouzrout

Keyword(s):

Internet Of Things ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Review Literature ◽

Comprehensive Understanding ◽

Physical Systems ◽

Fine Grained ◽

Key Technologies ◽

Granularity Level ◽

Human Component

AbstractThis work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.

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A spatio-temporal specification language and its completeness & decidability

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00209-3 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Tengfei Li ◽

Jing Liu ◽

Haiying Sun ◽

Xiang Chen ◽

Lipeng Zhang ◽

...

Keyword(s):

Intelligent Transportation System ◽

Cyber Physical Systems ◽

Specification Language ◽

Finite Model ◽

Critical Systems ◽

Physical Systems ◽

Spatial Objects ◽

Temporal Properties ◽

Spatio Temporal ◽

Temporal Specification

AbstractIn the past few years, significant progress has been made on spatio-temporal cyber-physical systems in achieving spatio-temporal properties on several long-standing tasks. With the broader specification of spatio-temporal properties on various applications, the concerns over their spatio-temporal logics have been raised in public, especially after the widely reported safety-critical systems involving self-driving cars, intelligent transportation system, image processing. In this paper, we present a spatio-temporal specification language, STSL PC, by combining Signal Temporal Logic (STL) with a spatial logic S4 u, to characterize spatio-temporal dynamic behaviors of cyber-physical systems. This language is highly expressive: it allows the description of quantitative signals, by expressing spatio-temporal traces over real valued signals in dense time, and Boolean signals, by constraining values of spatial objects across threshold predicates. STSL PC combines the power of temporal modalities and spatial operators, and enjoys important properties such as finite model property. We provide a Hilbert-style axiomatization for the proposed STSL PC and prove the soundness and completeness by the spatio-temporal extension of maximal consistent set and canonical model. Further, we demonstrate the decidability of STSL PC and analyze the complexity of STSL PC. Besides, we generalize STSL to the evolution of spatial objects over time, called STSL OC, and provide the proof of its axiomatization system and decidability.

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Identifying Critical Components in Large Scale Cyber Physical Systems

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops ◽

10.1145/3387940.3391473 ◽

2020 ◽

Cited By ~ 1

Author(s):

Aida Akbarzadeh ◽

Sokratis Katsikas

Keyword(s):

Large Scale ◽

Cyber Physical Systems ◽

Physical Systems ◽

Critical Components

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Artificial Intelligence and the Internet of Things in Industry 4.0

CCF Transactions on Pervasive Computing and Interaction ◽

10.1007/s42486-021-00057-3 ◽

2021 ◽

Author(s):

Petar Radanliev ◽

David De Roure ◽

Razvan Nicolescu ◽

Michael Huth ◽

Omar Santos

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Physical Systems ◽

Leading Industry ◽

Key Technologies ◽

The Internet Of Things ◽

State Of Art ◽

Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

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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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Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽

10.3390/s21020487 ◽

2021 ◽

Vol 21 (2) ◽

pp. 487 ◽

Cited By ~ 2

Author(s):

Mahmoud Elsisi ◽

Karar Mahmoud ◽

Matti Lehtonen ◽

Mohamed M. F. Darwish

Keyword(s):

Machine Learning ◽

Smart Grids ◽

Industry 4.0 ◽

Learning Algorithm ◽

Cyber Physical Systems ◽

The Internet ◽

Smart Meters ◽

Industrial Environment ◽

Physical Systems ◽

Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

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Introducing Gamification in the AR-Enhanced Order Picking Process: A Proposed Approach

Logistics ◽

10.3390/logistics5010014 ◽

2021 ◽

Vol 5 (1) ◽

pp. 14

Author(s):

Athina G. Bright ◽

Stavros T. Ponis

Keyword(s):

Industry 4.0 ◽

Business Processes ◽

Cyber Physical Systems ◽

Order Picking ◽

Physical Systems ◽

The Core ◽

Complex Process ◽

Core Elements ◽

Game Elements ◽

Innovative Industry

In the last decade, the Industry 4.0 concept has introduced automation and cyber-physical systems as the core elements of future logistics, supported by an array of technologies, such as augmented reality (AR) providing the necessary support for the digital transformation of manufacturing and logistics and the smartification and digital refinement of traditional pre-Industry 4.0 processes. This paper studies the influence and the potential of gamification techniques in supporting innovative Industry 4.0-enhanced processes in the contemporary warehouse work ecosystem. Gamification in the workplace aims to motivate the employees and increase their involvement in an activity, while at the same time creating a sense of an everyday different experience rather than a set of repetitive and monotonous tasks. Since the design of such a system is a complex process, the most widespread design frameworks are studied, and the emphasis is on the principal game elements and their connection to mobilization mechanisms. Finally, an initial proposal of a gamification framework to support the AR-enhanced order picking process in contemporary logistics centers is provided with an emphasis on the mechanics of a fair and functional reward system. The proposed approach aims to showcase the potential alignment of business processes to human motivation, respecting the differences between tasks and the workers’ cognitive workload.

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