Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systemat...Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systematic literature review examines the status, enabling technologies, case studies, and frameworks for DT applications in railway systems with 91 selected papers from Scopus, Web of Science, IEEE, and the Snowballing Technique. The review focuses on four primary subsystems: tracks, civil structures, vehicles, and overhead contact line structures. Key findings reveal that DT has successfully optimized maintenance strategies, improved operational efficiency, and enhanced system safety. Internet of Things (IoT) devices, Artificial Intelligence (AI), machine learning, and cloud computing are critical in implementing DT models. However, challenges like data integration, high implementation costs, and cybersecurity risks remain, necessitating the discussed implications. Future research should focus on improving data interoperability, reducing costs through scalable cloud-based solutions, and addressing cybersecurity vulnerabilities. DT technology has the potential to revolutionize railway infrastructure management, ensuring greater efficiency, safety, and sustainability.展开更多
Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(D...Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive analytics.CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains.This survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation capabilities.The layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless integration.The study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these obstacles.Applications in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT integration.In addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT systems.By synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and practitioners.The findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.展开更多
The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers.Specific lightweight cryptosystems are needed to protec...The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers.Specific lightweight cryptosystems are needed to protect those links,as the hardware node tends to be resource-constrained.Then Pseudo Random Number Generators are employed to produce random keys,whose final behavior depends on the initial seed.To guarantee good mathematical behavior,most key generators need an unpredictable voltage signal as input.However,physical signals evolve slowly and have a significant autocorrelation,so they do not have enough entropy to support highrandomness seeds.Then,electronic mechanisms to generate those high-entropy signals artificially are required.This paper proposes a robust hyperchaotic circuit to obtain such unpredictable electric signals.The circuit is based on a hyperchaotic dynamic system,showing a large catalog of structures,four different secret parameters,and producing four high entropy voltage signals.Synchronization schemes for the correct secret key calculation and distribution among all remote communicating modules are also analyzed and discussed.Security risks and intruder and attacker models for the proposed solution are explored,too.An experimental validation based on circuit simulations and a real hardware implementation is provided.The results show that the random properties of PRNG improved by up to 11%when seeds were calculated through the proposed circuit.展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, ma...Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution Systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.展开更多
The changes in time have made divergences of endless possibilities in localization technology.Localization in an indoor environment is surely a concerning matter as several shortcomings always arise when dealing with ...The changes in time have made divergences of endless possibilities in localization technology.Localization in an indoor environment is surely a concerning matter as several shortcomings always arise when dealing with indoor localization.To optimize localization in an indoor environment,tracking a subject’s position in real time is a certainly vital interest.Challenges in obtaining an accurate position in precise millimeter accuracy while the subject perceives visual information rendered in real time are somewhat always a matter in hand to address in an indoor environment.The main objective of this research is to implement a positioning method in an indoor environment based on ultra-wideband(UWB)technology to obtain position accuracy in millimeters by rapidly integrating with Unity three-dimensional(3D)engine,hence obtaining a detailed inertial measurement unit(IMU)data via wireless Message Queuing Telemetry Transport(MQTT)network protocol.The key results of this research should ensure the establishment of an indoor positioning system based on providing the finest selection of positioning and UWB parameters.These fine selections are an important design choice impacting the system’s performance in obtaining an accurate position within the range of 0.15 mm to 115 mm.The technological benefits involve the innovation of wireless communication based on the internet of things(IoT)concept relevant to the Industrial Revolution 4.0(IR 4.0),enabling participants to move freely in an indoor environment while obtaining accurate and precise positioning coordinates.Future recommendations comprise a real-time 2D Pozyx Creator Controller integrated with Unity 3D user graphical user interface(GUI)purposes intended for mobile mapping navigations.展开更多
文摘Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systematic literature review examines the status, enabling technologies, case studies, and frameworks for DT applications in railway systems with 91 selected papers from Scopus, Web of Science, IEEE, and the Snowballing Technique. The review focuses on four primary subsystems: tracks, civil structures, vehicles, and overhead contact line structures. Key findings reveal that DT has successfully optimized maintenance strategies, improved operational efficiency, and enhanced system safety. Internet of Things (IoT) devices, Artificial Intelligence (AI), machine learning, and cloud computing are critical in implementing DT models. However, challenges like data integration, high implementation costs, and cybersecurity risks remain, necessitating the discussed implications. Future research should focus on improving data interoperability, reducing costs through scalable cloud-based solutions, and addressing cybersecurity vulnerabilities. DT technology has the potential to revolutionize railway infrastructure management, ensuring greater efficiency, safety, and sustainability.
文摘Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive analytics.CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains.This survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation capabilities.The layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless integration.The study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these obstacles.Applications in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT integration.In addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT systems.By synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and practitioners.The findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.
基金supported by Comunidad de Madrid within the framework of the Multiannual Agreement with Universidad Politecnica de Madrid to encourage research by young doctors(PRINCE).
文摘The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers.Specific lightweight cryptosystems are needed to protect those links,as the hardware node tends to be resource-constrained.Then Pseudo Random Number Generators are employed to produce random keys,whose final behavior depends on the initial seed.To guarantee good mathematical behavior,most key generators need an unpredictable voltage signal as input.However,physical signals evolve slowly and have a significant autocorrelation,so they do not have enough entropy to support highrandomness seeds.Then,electronic mechanisms to generate those high-entropy signals artificially are required.This paper proposes a robust hyperchaotic circuit to obtain such unpredictable electric signals.The circuit is based on a hyperchaotic dynamic system,showing a large catalog of structures,four different secret parameters,and producing four high entropy voltage signals.Synchronization schemes for the correct secret key calculation and distribution among all remote communicating modules are also analyzed and discussed.Security risks and intruder and attacker models for the proposed solution are explored,too.An experimental validation based on circuit simulations and a real hardware implementation is provided.The results show that the random properties of PRNG improved by up to 11%when seeds were calculated through the proposed circuit.
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
文摘Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution Systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.
文摘The changes in time have made divergences of endless possibilities in localization technology.Localization in an indoor environment is surely a concerning matter as several shortcomings always arise when dealing with indoor localization.To optimize localization in an indoor environment,tracking a subject’s position in real time is a certainly vital interest.Challenges in obtaining an accurate position in precise millimeter accuracy while the subject perceives visual information rendered in real time are somewhat always a matter in hand to address in an indoor environment.The main objective of this research is to implement a positioning method in an indoor environment based on ultra-wideband(UWB)technology to obtain position accuracy in millimeters by rapidly integrating with Unity three-dimensional(3D)engine,hence obtaining a detailed inertial measurement unit(IMU)data via wireless Message Queuing Telemetry Transport(MQTT)network protocol.The key results of this research should ensure the establishment of an indoor positioning system based on providing the finest selection of positioning and UWB parameters.These fine selections are an important design choice impacting the system’s performance in obtaining an accurate position within the range of 0.15 mm to 115 mm.The technological benefits involve the innovation of wireless communication based on the internet of things(IoT)concept relevant to the Industrial Revolution 4.0(IR 4.0),enabling participants to move freely in an indoor environment while obtaining accurate and precise positioning coordinates.Future recommendations comprise a real-time 2D Pozyx Creator Controller integrated with Unity 3D user graphical user interface(GUI)purposes intended for mobile mapping navigations.
