scholarly journals Acquisition and Processing of Data in CPS for Remote Monitoring of the Human functional State

2021 ◽  
Vol 6 (1) ◽  
pp. 14-20
Author(s):  
Petro Hupalo ◽  
◽  
Anatoliy Melnyk
Keyword(s):  
Machine Learning ◽  
Functional State ◽  
Remote Monitoring ◽  
Physical System ◽  
Machine Learning Algorithms ◽  
Cyber Physical System ◽  
Health Changes ◽  
Forecasting Evaluation ◽  
Data Acquisition And Processing ◽  
Processing Steps

Data acquisition and processing in cyber-physical system for remote monitoring of the human functional state have been considered in the paper. The data processing steps, strategies for multi-step forecasting evaluation metrics and machine learning algorithms to be implemented have been analysed and described. What is important, this way it will be possible to track the condition of the sick and response to the health changes in advance.

scholarly journals Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1945
Author(s):  
Hsu-Chih Huang ◽  
Jing-Jun Xu
Keyword(s):  
Machine Learning ◽  
Fuzzy Control ◽  
Physical System ◽  
Fuzzy Systems ◽  
Online Control ◽  
Polar Space ◽  
Robotic System ◽  
Lyapunov Theory ◽  
Flower Pollination Algorithm ◽  
Cyber Physical System

This paper contributes to the development of evolutionary machine learning (EML) for optimal polar-space fuzzy control of cyber-physical Mecanum vehicles using the flower pollination algorithm (FPA). The metaheuristic FPA is utilized to design optimal fuzzy systems, called FPA-fuzzy. In this hybrid computation, both the fuzzy structure and the number of IF–THEN rules are optimized through the FPA evolutionary process. This approach overcomes the drawback of the structure tuning problem in conventional fuzzy systems. After deriving the polar-space kinematics model of Mecanum vehicles, an optimal EML FPA-fuzzy online control scheme is synthesized, and the global stability is proven via Lyapunov theory. An embedded cyber-physical robotic system is then constructed using the typical 5C strategy. The proposed FPA-fuzzy computation collaborates with the advanced sensors and actuators of Mecanum vehicles to design a cyber-physical robotic system. Compared with conventional Cartesian-space control methods, the proposed EML FPA-fuzzy has the advantages of metaheuristics, fuzzy online control, and cyber-physical system design in polar coordinates. Finally, the mechatronic design and experimental setup of a Mecanum vehicle cyber-physical system is constructed. Through experimental results and comparative works, the effectiveness and merit of the proposed methods are validated. The proposed EML FPA-fuzzy control approach has theoretical and practice significance in terms of its real-time capability, online parameter tuning, convergent behavior, and hybrid artificial intelligence.

scholarly journals Hardware and Software Infrastructure of Digital Twin Technology

2020 ◽  
Author(s):  
Zakharov L.A ◽  
Derksen L.A.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Physical System ◽  
Digital Model ◽  
Cyber Physical System ◽  
Product Lifecycle ◽  
Software Infrastructure ◽  
Digital Twin ◽  
Data Product ◽  
Basic Approaches

This article describes of hardware and software infrastructure that provides the implementation of digital double technology. The basic approaches to determining the technologies that make up the infrastructure for the implementation of the digital twin, as well as the benefits of implementing this technology are considered. The need for processing and storing big data, as well as the benefits of implementing this technology, is substantiated. Keywords: digital twin, digital model, big data, product lifecycle, cyber-physical system, automation, machine learning, smart maintenance.

scholarly journals Development of An Integrated Framework for Cyber Physical System (CPS)-Enabled Rehabilitation System

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Jianshe Feng ◽  
Feng Zhu ◽  
Pin Li ◽  
Hossein Davari ◽  
Jay Lee
Keyword(s):  
Physical System ◽  
Enhanced Recovery ◽  
Treatment Strategies ◽  
Gait Training ◽  
Machine Learning Algorithms ◽  
Personalized Treatment ◽  
Physiological Data ◽  
Cyber Physical System ◽  
Human Machine Interaction ◽  
Rehabilitation System

A Cyber-Physical System (CPS)-enabled rehabilitation system framework for enhanced recovery rate in gait training systems is presented in this paper. Recent advancements in sensing and data analytics have paved the way for the transformation of healthcare systems from experience-based to evidence-based. To this end, this paper introduces a CPS-enabled rehabilitation system that collects, processes, and models the data from patient and rehabilitative training machines. This proposed system consists of a set of sensors to collect various physiological data as well as machine parameters. The sensors and data acquisition systems are connected to an edge computing unit that handles the data preprocessing, analytics, and results visualization. Advanced machine learning algorithms are used to analyze data from physiological data, machine parameters, and patients’ metadata to quantify each patient’s recovery progress, devise personalized treatment strategies, adjust machine parameters for optimized performance, and provide feedback regarding patient’s adherence to instructions. Moreover, the accumulation of the knowledge gathered by patients with different conditions can provide a powerful tool for better understanding the human-machine interaction and its impact on patient recovery. Such system can eventually serve as a ‘Virtual Doctor’, providing accurate feedback and personalized treatment strategies for patients.

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ming-Chuan Chiu ◽  
Chien-De Tsai ◽  
Tung-Lung Li
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Fault Detection ◽  
Real Time ◽  
Physical System ◽  
Shop Floor ◽  
Cyber Physical System ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

Reverse Mentoring the Editing Edge in Management 4.0

2020 ◽  
pp. 185-193
Author(s):  
Swati Sisodia ◽  
Neetima Agarwal
Keyword(s):  
Machine Learning ◽  
Generation X ◽  
Physical System ◽  
Industry 4.0 ◽  
Business Processes ◽  
Cyber Physical System ◽  
Logistic Management ◽  
Ingenious Method

Industry 4.0 is based on the implementation of a cyber-physical system, which includes sensors, networks, computers, offering digital enhancement and well-coordinated activities. This would create a great pool of all the workforce generations, having diverse experience, agility, and different modes of working. Millennials would add more of machine learning and Generation X and Y would be the richest source of tacit and operational knowledge. Together, they would develop solutions for catering complex and networked production and aggressive logistic management, meeting the challenges of the Industry 4.0. However, the benefits of digitization and automation can be achieved, if the different generations of workforce collaborate, cooperate, and postulate together in all the business processes. Reverse mentoring is a pristine concept and ingenious method to empower learning and encourage cross-generational connections. This chapter would elaborate on the advantage of reverse mentoring in crafting Industry 4.0 more acrobatic and quick-moving.

A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications

2018 ◽  
Vol 15 ◽  
pp. 139-142 ◽  
Author(s):  
Peter O'Donovan ◽  
Colm Gallagher ◽  
Ken Bruton ◽  
Dominic T.J. O'Sullivan
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Industry 4.0 ◽  
Fog Computing ◽  
Low Latency ◽  
Cyber Physical System

Machine Learning and Big Data in Cyber‐Physical System: Methods, Applications and Challenges

2021 ◽  
pp. 49-91
Author(s):  
Janmenjoy Nayak ◽  
P. Suresh Kumar ◽  
Dukka Karun Kumar Reddy ◽  
Bighnaraj Naik ◽  
Danilo Pelusi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Physical System ◽  
Cyber Physical System
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