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.

scholarly journals Application of cognitive technologies for the formation of speech dialogue management models

2019 ◽  
Vol 23 (6) ◽  
pp. 13-21 ◽  
Author(s):  
A. A. Solodov ◽  
Т. G. Trembach
Keyword(s):  
Industry 4.0 ◽  
Intelligent Systems ◽  
Cyber Physical Systems ◽  
Cognitive Mechanisms ◽  
Dialogue Management ◽  
Physical Systems ◽  
Management Models ◽  
Mental Operations ◽  
Voice Dialogue ◽  
The Internet Of Things

Purpose of research.  The aim of the study is the use of cognitive technologies for the formation of speech dialogue management models. At present, for the development of the Internet of things, the expansion of communicative opportunities for their interaction, it is important to improve speech dialogue management models in many areas. The need for dialogue can arise between cyber-physical systems, between a person and cyber-physical systems, between users, developers, and administrators. The speech dialogue management models covers many issues related to processing a speech signal, semantic analysis, understanding the meaning of speech, using cognitive mechanisms for interaction, and some others. A special place among them is occupied by the problem of building a speech dialogue between users, developers, and cyberphysical systems. This article is devoted to the consideration of the use of speech dialogue management models of intelligent systems, as well as users, developers, administrators.Materials and methods of research.  New approaches and methods are required to solve the problems within the framework of the Industry 4.0. The industry 4.0 concept represents a variety of technologies, including the creation of cyber-physical systems, a variety of different protocols for their interaction. One of its main directions is the Internet of things. Cognitive mechanisms associated with the formation and application of concrete sensory images, concepts-representations, concepts-frames are used to solve the problems of model formation. To form the abilities and speech skills for the communicative activities of the participants of the interaction within the framework of the Industry 4.0 concept, the methods associated with mastering foreign language technologies were used. The ultimate goal of their use is to achieve the ability to master spontaneous speech in both everyday and professional situations. Mastering a foreign language involves the use of cognitive technologies, which allows you to develop the structure of mental operations.Results.  Some features of the formation of a voice dialogue management model for intelligent systems and the preparation of interaction participants in the framework of the concept — users, developers, and industrial system administrators  –  are considered. The application of cognitive mechanisms for organizing and using the model of speech dialogue management is shown. The use of conceptual representations and scripting concepts in intelligent systems allows us to develop the structure of the world model. The application of cognitive mechanisms for training interaction participants, within the framework of the Industry 4.0 concept, improves their training by improving understanding of the material being studied by building logical connections and a mental model of the material. Using concepts allows you to build mental models of your own thoughts. At the end of all mental operations, participants in the interaction acquire the ability to form speech dialogue management models.Conclusion.  The use of cognitive technologies makes it possible, both for intelligent systems and for interaction participants, to use generalized static structures of concepts, situations, and dynamic structures for real and mental operations. The active use of such structures makes it possible to better understand the current situation and successfully formulate and use voice dialogue management models for solving problems arising during the development of the Industry 4.0 concept.

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 Artificial Intelligence and the Internet of Things in Industry 4.0

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.

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 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.

Internet of Things and Cyber-Physical Systems at the University

2020 ◽  
pp. 285-302
Author(s):  
Dmitry Namiot ◽  
Manfred Sneps-Sneppe
Keyword(s):  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Physical Systems ◽  
Information And Communication ◽  
The University ◽  
Educational Course ◽  
The Internet Of Things

This chapter describes proposals for organizing university programs on the internet of things (IoT) and cyber-physical systems. The final goal is to provide a structure for a basic educational course for the internet of things and related areas. This base (template) could be used both for direct training and for building other courses, including those that are more deeply specialized in selected areas. For related areas, the authors see, for example, machine-to-machine communications and data-driven cities (smart cities) development. Obviously, the internet of things skills are in high demand nowadays, and, of course, IoT models, architectures, as well as appropriate data proceedings elements should be presented in the university courses. The purpose of the described educational course is to cover information and communication technologies used in the internet of things systems and related areas. Also, the authors discuss big data and AI issues for IoT courses and highlight the importance of data engineering.

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