Cyber physical systems design, methodology, and integration: the current status and future outlook

Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1007/s12652-017-0575-4

Cited by 48 publications

(29 citation statements)

References 72 publications

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“…At the same time, intelligent autonomous cyber system requires development of extensive and advanced physical architecture-based information system [25]. The Eindhoven Institute for Research on ICT (EIRCT) identified six components of CPS architecture: physical world, transducers, control components, data analytics elements, computation elements, and communication components [16,33]. Physical part of CPS in such grouping is limited to physical world and transducers; however, some authors separate physical world (devices or hardware) and physical environment (environment where the physical objects are located) [46,61].…”

Section: The Concepts Of Cyber-physical Systems (Cpss)mentioning

confidence: 99%

Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

2020

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Current food system evolved in a great degree because of the development of processing and food engineering technologies: people learned to bake bread long before the advent of agriculture; salting and smoking supported nomad lifestyles; canning allowed for longer military marches; etc. Food processing technologies went through evolution and significant optimization and currently rely on minor fraction of energy comparing with initial prototypes. Emerging processing technologies (high-pressure, pulsed electric fields, ohmic heating, ultrasound) and novel food systems (cultured biomass, 3-D bioprinting, cyber-physical chains) try to challenge the existing chains by developing potentially more nutritious and sustainable food solutions. However, new food systems rely on low technology readiness levels and estimation of their potential future benefits or drawbacks is a complex task mostly due to the lack of integrated data. The research is aimed for the development of conceptual guidelines of food production system structuring as cyber-physical systems. The study indicates that cyber-physical nature of modern food is a key for the engineering of more nutritious and sustainable paths for novel food systems. Implementation of machine learning methods for the collection, integration, and analysis of data associated with biomass production and processing on different levels from molecular to global, leads to the precise analysis of food systems and estimation of upscaling benefits, as well as possible negative rebound effects associated with societal attitude. Moreover, such data-integrated assessment systems allow transparency of chains, integration of nutritional and environmental properties, and construction of personalized nutrition technologies.

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Section: The Concepts Of Cyber-physical Systems (Cpss)mentioning

confidence: 99%

Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

2020

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“…For aeroengine, the fan blade machining (FBM) can take more than a third of time. 42 It is particularly important and urgent for aeroengine manufacturers to improve the machining quality and processing efficiency by smart manufacturing mode. At present, the blade manufacturing methods mainly include electrolytic machining, milling machining, precision forging, and so on.…”

Section: Project Backgroundmentioning

confidence: 99%

Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing

Zhang

Zhu

2019

International Journal of Advanced Robotic Systems

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In the wake of the continuous deepening of the application of new generation information technology in the manufacturing field, digital twin, as a most new active factors for smart manufacturing, has become a new research hot spot. Based on such a background, the article proposes a novel application framework of digital twin-driven product smart manufacturing system and analyzes its operation mechanism. Key enabling technologies such as digital twin mapping technology with manufacturing entity, twinning of cyber and physical manufacturing system, as well as twining data-driven machining parameter optimization are also illustrated in detail. Finally, a case of the aeroengine fan blade manufacturing is given to demonstrate the feasibility and effectiveness of the implementation method mentioned above. Meanwhile, potential industrial applications and limitations are discussed as well to provide valuable insights to aeroengine blade manufacturers.

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“…Cyber-physical systems (CPS) promote the integration of IoT-enabled physical world with the computation-powered cyber world through seamless communication between them [1] [2] [3]. The interconnection between cyber and physical world are usually assisted by a feedback-loop control system [4] [3] [5], which enables CPS to be more adaptive to the changes in the physical world as sensed by various IoT sensing devices. The processing of diverse and dynamic data sources using different machine learning (ML) algorithms enable the CPS to exhibit higher intelligence [6] [7], which supports various application domains, including smart healthcare [8], transportation and others.…”

Section: Introductionmentioning

confidence: 99%

Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

2021

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Cyber-Physical Systems (CPS) embed computation and communication capability into its core to regulate physical processes and seamlessly mediate between the cyber and the physical world for various control and monitoring tasks. Health CPS, a variant of CPS in the healthcare sector, acts as a health monitoring system to dynamically capture, process, and analyze health sensor data through integrated internet of things (IoT)-enabled cyber-physical processes. These systems can suitably support patients suffering from non-communicable diseases (NCDs) or who are at risk of suffering from those. Identifying the risk of NCDs, such as heart disease and diabetes, requires artificial intelligence (AI) techniques into the core of health CPS. Recently, there has been growing interest to incorporate machine learning into CPS, which can facilitate the disease classification, detection, monitoring, and prediction of several NCDs. However, there is a shortage of visible work that focus on early-stage risk prediction of these diseases. In this work, we propose a novel machine learning based health CPS framework that addresses the challenge of effectively processing the wearable IoT sensor data for early risk prediction of diabetes as an example of NCDs. In the experiment, a verified diabetic dataset has been used for training, while the testing has been performed on an artificially generated data collection from sensors. The experiment with several machine learning algorithms shows the effectiveness of the proposed approach in achieving the maximum precision from the Random Tree algorithm, which requires a minimum time of 0.01s to construct a model and obtains 94% accuracy to predict the probability of diabetes at an early point.

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Cyber physical systems design, methodology, and integration: the current status and future outlook

Cited by 48 publications

References 72 publications

Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing

Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

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