scholarly journals Design of IoT-based Cyber–Physical Systems: A Driverless Bulldozer Prototype

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 343 ◽  
Author(s):  
Nelson H. Carreras Guzman ◽  
Adam Gergo Mezovari
Keyword(s):  
Autonomous Vehicles ◽  
Industrial Revolution ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Transportation Systems ◽  
Small Scale ◽  
Security Risk ◽  
Physical Systems ◽  
Wireless Communication Network ◽  
Enhance Efficiency

From autonomous vehicles to robotics and machinery, organizations are developing autonomous transportation systems in various domains. Strategic incentives point towards a fourth industrial revolution of cyber–physical systems with higher levels of automation and connectivity throughout the Internet of Things (IoT) that interact with the physical world. In the construction and mining sectors, these developments are still at their infancy, and practitioners are interested in autonomous solutions to enhance efficiency and reliability. This paper illustrates the enhanced design of a driverless bulldozer prototype using IoT-based solutions for the remote control and navigation tracking of the mobile machinery. We illustrate the integration of a cloud application, communication protocols and a wireless communication network to control a small-scale bulldozer from a remote workstation. Furthermore, we explain a new tracking functionality of work completion using maps and georeferenced indicators available via a user interface. Finally, we provide a preliminary safety and security risk assessment of the system prototype and propose guidance for application in real-scale machinery.

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.

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 Graceful extensibility in asset management: extending the capacity to adapt in managing cyber-physical railway systems

2021 ◽  
Author(s):  
Jan-jaap Moerman ◽  
Jan Maarten Schraagen ◽  
Jan Braaksma ◽  
Leo van Dongen
Keyword(s):  
Asset Management ◽  
Adaptive Systems ◽  
New Technologies ◽  
Autonomous Driving ◽  
Cyber Physical Systems ◽  
Transportation Systems ◽  
Rolling Stock ◽  
Railway Transportation ◽  
Physical Systems ◽  
Railway Systems

AbstractGraceful extensibility has been recently introduced and can be defined as the ability of a system to extend its capacity to adapt when surprise events challenge its boundaries. It provides basic rules that govern adaptive systems. Railway transportation systems can be considered cyber-physical systems that comprise interacting digital, analog, physical, and human components engineered for safe and reliable railway transport. This enables autonomous driving, new functionalities to achieve higher capacity, greater safety, and real-time health monitoring. New rolling stock introductions require continuous adaptations to meet the challenges of these complex railway systems as an introduction takes several years to complete and deals with changing stakeholder demands, new technologies, and technical constraints which cannot be fully predicted in advance. To sustain adaptability when introducing new rolling stock, the theory of graceful extensibility might be valuable but needs further empirical testing to be useful in the field. This study contributes by assessing the proto-theorems of graceful extensibility in a case study in the railway industry by means of adopting pattern-matching analysis. The results of this study indicate that the majority of theoretical patterns postulated by the theory are corroborated by the data. Guidelines are proposed for further operationalization of the theory in the field. Furthermore, case results indicate the need to adopt management approaches that accept indeterminism as a complement to the prevailing deterministic perspective, to sustain adaptability and deal effectively with surprise events. As such, this study may serve other critical asset introductions dealing with cyber-physical systems in their push for sustained adaptability.

scholarly journals Effects of Industry 4.0 on the Labor Markets of Iran and Japan

Economies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 39 ◽  
Author(s):  
Majid Ziaei Nafchi ◽  
Hana Mohelská
Keyword(s):  
Labor Markets ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Developed Countries ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Science Technology ◽  
Levels Of Automation ◽  
Technology And Economy ◽  
The Impact

Industry 4.0 is the essence of the fourth Industrial revolution and is happening right now in manufacturing by using cyber-physical systems (CPS) to reach high levels of automation. Industry 4.0 is especially beneficial in highly developed countries in terms of competitive advantage, but causes unemployment because of high levels of automation. The aim of this paper is to find out if the impact of adopting Industry 4.0 on the labor markets of Iran and Japan would be the same, and to make analysis to find out whether this change is possible for Iran and Japan with their current infrastructures, economy, and policies. With the present situation of Iran in science, technology, and economy, it will be years before Iran could, or better say should, implement Industry 4.0. Japan is able to adopt Industry 4.0 much earlier than Iran and with less challenges ahead; this does not mean that the Japanese labor market would not be affected by this change but it means that those effects would not cause as many difficulties as they would for Iran.

scholarly journals How connectivity and search for producers impact production in Industry 4.0 networks

2018 ◽  
Vol 15 (4) ◽  
pp. 528-534
Author(s):  
Adriano Pereira ◽  
Eugênio De Oliveira Simonetto ◽  
Goran Putnik ◽  
Helio Cristiano Gomes Alves de Castro
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Cyber Physical Systems ◽  
Average Degree ◽  
Collaborative Networks ◽  
Production Network ◽  
Simulation Environment ◽  
Physical Systems ◽  
Study Results ◽  
The Internet Of Things

Technological evolutions lead to changes in production processes; the Fourth Industrial Revolution has been called Industry 4.0, as it integrates Cyber-Physical Systems and the Internet of Things into supply chains. Large complex networks are the core structure of Industry 4.0: any node in a network can demand a task, which can be answered by one node or a set of them, collaboratively, when they are connected. In this paper, the aim is to verify how (i) network's connectivity (average degree) and (ii) the number of levels covered in nodes search impacts the total of production tasks completely performed in the network. To achieve the goal of this paper, two hypotheses were formulated and tested in a computer simulation environment developed based on a modeling and simulation study. Results showed that the higher the network's average degree is (their nodes are more connected), the greater are the number of tasks performed; in addition, generally, the greater are the levels defined in the search for nodes, the more tasks are completely executed. This paper's main limitations are related to the simulation process, which led to a simplification of production process. The results found can be applied in several Industry 4.0 networks, such as additive manufacturing and collaborative networks, and this paper is original due to the use of simulation to test this kind of hypotheses in an Industry 4.0 production network.

ROBIN: An open-source middleware for plug‘n’produce of Cyber-Physical Systems

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091031
Author(s):  
Rafael Arrais ◽  
Paulo Ribeiro ◽  
Henrique Domingos ◽  
Germano Veiga
Keyword(s):  
Open Source ◽  
Industrial Revolution ◽  
Cyber Physical Systems ◽  
Industrial Applications ◽  
Mobile Manipulator ◽  
Battery Management ◽  
Efficient Manner ◽  
Physical Systems ◽  
Data Interchange ◽  
Automation Equipment

Motivated by the Fourth Industrial Revolution, there is an ever-increasing need to integrated Cyber-Physical Systems in industrial production environments. To address the demand for flexible robotics in contemporary industrial environments and the necessity to integrate robots and automation equipment in an efficient manner, an effective, bidirectional, reliable and structured data interchange mechanism is required. As an answer to these requirements, this article presents ROBIN, an open-source middleware for achieving interoperability between the Robot Operating System and CODESYS, a softPLC that can run on embedded devices and that supports a variety of fieldbuses and industrial network protocols. The referred middleware was successfully applied and tested in various industrial applications such as battery management systems, motion, robotic manipulator and safety hardware control, and horizontal integration between a mobile manipulator and a conveyor system.

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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