scholarly journals Data driven discovery of cyber physical systems

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ye Yuan ◽  
Xiuchuan Tang ◽  
Wei Zhou ◽  
Wei Pan ◽  
Xiuting Li ◽  
...  
Keyword(s):  
Smart Grids ◽  
Underlying Mechanism ◽  
Physical World ◽  
Intelligent Manufacturing ◽  
Cyber Physical Systems ◽  
The Novel ◽  
Physical Systems ◽  
Embed Software ◽  
Wide Range ◽  
Intrinsic Complexity

Abstract Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.

Emerging Cyber-Physical Systems : An Overview

2018 ◽  
pp. 306-316
Author(s):  
Okolie S.O. ◽  
Kuyoro S.O. ◽  
Ohwo O. B
Keyword(s):  
Health Care ◽  
Economic Benefit ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Cyber Physical System ◽  
Economic Sectors ◽  
Strategic Research ◽  
Physical Systems ◽  
Security Issues ◽  
Wide Range

Cyber-Physical Systems (CPS) will revolutionize how humans relate with the physical world around us. Many grand challenges await the economically vital domains of transportation, health-care, manufacturing, agriculture, energy, defence, aerospace and buildings. Exploration of these potentialities around space and time would create applications which would affect societal and economic benefit. This paper looks into the concept of emerging Cyber-Physical system, applications and security issues in sustaining development in various economic sectors; outlining a set of strategic Research and Development opportunities that should be accosted, so as to allow upgraded CPS to attain their potential and provide a wide range of societal advantages in the future.

scholarly journals A Survey of Blockchain Enabled Cyber-Physical Systems

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 282 ◽  
Author(s):  
Heena Rathore ◽  
Amr Mohamed ◽  
Mohsen Guizani
Keyword(s):  
Data Storage ◽  
Financial Services ◽  
Smart Grids ◽  
Health Care Systems ◽  
Physical World ◽  
Sharing Economy ◽  
Cyber Physical Systems ◽  
Digital Information ◽  
Distributed Data Storage ◽  
Physical Systems

Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. It has the capability to revolutionize a diverse set of business applications, ranging from sharing economy to data management and prediction markets. In this paper, we present a holistic survey of various applications of CPS where blockchain has been utilized. Smart grids, health-care systems, and industrial production processes are some of the many applications that can benefit from the blockchain technology and will be discussed in the paper.

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 1-25
Author(s):  
Pin Ni ◽  
Yuming Li ◽  
Gangmin Li ◽  
Victor Chang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Physical World ◽  
Heterogeneous Data ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Language Data ◽  
Text Language ◽  
Different Sources

Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters.

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

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.

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

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
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.

Durchgängiges Anforderungsmanagement und Engineering für Smart Grid Lösungen

2020 ◽  
Vol 68 (9) ◽  
pp. 711-719
Author(s):  
Mathias Uslar
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Cyber Physical Systems ◽  
Industrie 4.0 ◽  
Internet Der Dinge ◽  
Physical Systems

ZusammenfassungIn diesem Beitrag wird die Notwendigkeit einer sinnvollen Definition und Klarstellung der Disziplin Energieinformatik aufgezeigt. Der Beitrag diskutiert verschiedene bestehende Definitionen und stellt sie in den Kontext des Anforderungsmanagements und der Lösungsfindung. Er motiviert die Notwendigkeit eines strukturierten disziplinären Ansatzes in der Energieinformatik auf der Grundlage bestehender Probleme und skizziert den aktuellen Stand des Stands der Wissenschaft und Technik, der hauptsächlich den systemtechnischen Anwendungsbereich für Smart Grids umfasst. Synergien mit anderen aktuellen Schwerpunktthemen wie Internet der Dinge (IoT), Industrie 4.0 (Digitalisierung der Produktion) und Cyber-Physical Systems (CPS) werden aus Anforderungssicht motiviert. Auf der Grundlage der aufgeworfenen Fragen und Herausforderungen werden neue sinnvolle Forschungsthemen für ein durchgängiges Anforderungsmanagement im Kontext Smart Grid diskutiert.

Adoption of Machine Learning With Adaptive Approach for Securing CPS

2020 ◽  
pp. 388-415
Author(s):  
Rama Mercy Sam Sigamani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Smart Grids ◽  
False Negative ◽  
False Negative Rate ◽  
Cyber Physical Systems ◽  
Detection Accuracy ◽  
Physical Systems ◽  
Positive Rate ◽  
Iot Devices

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.

scholarly journals Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 59
Author(s):  
Fabrizio De Vita ◽  
Dario Bruneo
Keyword(s):  
Intelligent Systems ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Training Data ◽  
Cloud Platform ◽  
Model Accuracy ◽  
Training Time ◽  
Physical Systems ◽  
The Internet Of Things ◽  
And Training

During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.

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