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Digital twin development : an introduction to Simcenter Amesim / / Frank U. Rückert [and three others]
| Digital twin development : an introduction to Simcenter Amesim / / Frank U. Rückert [and three others] |
| Autore | Rückert Frank U. |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (129 pages) |
| Disciplina | 381 |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN | 3-031-25692-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Mathematics, Signals and Control Library -- 3. The Mechanical Twin -- 4. The Thermal Twin -- 5. The Hydraulic Twin -- 6. The Pneumatic Twin -- 7. The Electric Twin -- 8. Analysis of Complex Technical Systems -- 9. Digital Twins and Artificial Intelligence -- 10. Conclusions. |
| Record Nr. | UNINA-9910682591503321 |
Rückert Frank U.
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| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Twin Technologies for Healthcare 4. 0
| Digital Twin Technologies for Healthcare 4. 0 |
| Autore | Dhanaraj Rajesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2023 |
| Descrizione fisica | 1 online resource (217 pages) |
| Disciplina | 610.285 |
| Altri autori (Persone) |
MurugesanSanthiya
BalusamyBalamurugan BalasValentina E |
| Collana | Healthcare Technologies Series |
| Soggetto topico |
Digital twins (Computer simulation)
Medical telematics Artificial intelligence - Medical applications |
| ISBN |
1-83724-477-4
1-5231-5539-6 1-83953-580-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title -- Copyright -- Contents -- About the editors -- 1 Introduction: digital twin technology in healthcare -- 1.1 Introduction -- 1.2 Digital twin - background study -- 1.3 Research on digital twin technologies -- 1.4 Digital twin sectors in healthcare -- 1.4.1 Digital patient -- 1.4.2 Pharmaceutical industry -- 1.4.3 Hospital -- 1.4.4 Wearable technologies -- 1.5 Challenges and issues in implementation -- 1.5.1 Trust -- 1.5.2 Security and privacy -- 1.5.3 Standardization -- 1.5.4 Diversity and multisource -- References -- 2 Convergence of Digital Twin, AI, IOT, and machine learning techniques for medical diagnostics -- 2.1 Introduction -- 2.2 DT technology -- 2.2.1 Steps in DT creation -- 2.2.2 DT types and functions -- 2.3 DT and its supporting technologies - AI, Cloud computing, DL, Big Data analytics, ML, and IoT -- 2.4 DT integration with other technologies for medical diagnosis and health management -- 2.5 DT technology and its application -- 2.5.1 DT application in manufacturing industry -- 2.5.2 Applications of DT in automotive & -- aerospace -- 2.5.3 Medicine diagnosis and device development -- 2.5.4 Wind twin technology -- 2.6 Conclusion -- References -- 3 Application of digital twin technology in model-based systems engineering -- 3.1 Evolution of DTT -- 3.2 Basic concepts of DTT -- 3.3 DTT implementation in power system -- 3.3.1 Characteristics of DTT in power systems -- 3.4 Power system network modeling using DTT -- 3.4.1 Model-based approach -- 3.4.2 Data-driven approach -- 3.4.3 Combination of both -- 3.5 Integration of power system with DTT -- 3.6 Future scope of DTT in power systems -- 3.7 Conclusion -- References -- 4 Digital twins in e-health: adoption of technology and challenges in the management of clinical systems -- 4.1 Introduction -- 4.2 Digital twin -- 4.3 Evolution of healthcare services.
4.4 Elderly medical services and demands -- 4.5 Cloud computing -- 4.6 Cloud computing DT in healthcare -- 4.6.1 Use cases -- 4.7 Digital healthcare modeling process -- 4.8 Cloud-based healthcare facility platform -- 4.9 Applications of DT technology -- 4.9.1 Cardiovascular application -- 4.9.2 Cadaver high temperature -- 4.9.3 Diabetes meters -- 4.9.4 Stress monitoring -- 4.10 Benefits of DT technology -- 4.10.1 Remote monitoring -- 4.10.2 Group cooperation -- 4.10.3 Analytical maintenance -- 4.10.4 Transparency -- 4.10.5 Future prediction -- 4.10.6 Information -- 4.10.7 Big data analytics and processing -- 4.10.8 Cost effectiveness -- 4.11 DT challenges in healthcare -- 4.11.1 Cost effectiveness -- 4.11.2 Data collection -- 4.11.3 Data protection -- 4.11.4 Team collaboration -- 4.11.5 Monitoring -- 4.11.6 Software maintenance and assurance -- 4.11.7 Regulatory complications -- 4.11.8 Security and privacy-related issues -- 4.11.9 Targets of attackers -- 4.12 Conclusion -- References -- 5 Digital twin and big data in healthcare systems -- 5.1 Introduction -- 5.1.1 Working of DT technology -- 5.2 Need for DT and big data in healthcare -- 5.3 DT and big data benefits for healthcare -- 5.3.1 Monitoring of patients -- 5.3.2 Individualized medical care -- 5.3.3 Patient individuality and freedom -- 5.4 Applications of DT in healthcare -- 5.4.1 Diagnosis and decision support -- 5.4.2 Patient monitoring -- 5.4.3 Drug and medical device development -- 5.4.4 Personalized medicine -- 5.4.5 Medical imaging and wearables -- 5.5 Enabling technologies for DT and data analytics in healthcare -- 5.5.1 Technologies for DT in healthcare -- 5.5.2 Technologies for data analytics in healthcare -- 5.6 Research challenges of DT and big data in healthcare -- 5.6.1 Problem complexities and challenges -- 5.6.2 Research challenges for DT in healthcare. 5.6.3 Useful information -- 5.7 Future research directions -- 5.8 Conclusion -- References -- 6 Digital twin data visualization techniques -- 6.1 Introduction - twin digital -- 6.2 Invention of DT -- 6.2.1 Function of DT technology -- 6.2.2 What problems has it solved? -- 6.3 DT types -- 6.3.1 Parts twinning -- 6.3.2 Product twinning -- 6.3.3 System twinning -- 6.3.4 Process twinning -- 6.4 When to use -- 6.5 Design DT -- 6.5.1 Digital data -- 6.5.2 Models -- 6.5.3 Linking -- 6.5.4 Examples -- 6.5.5 How has it impacted the industry? -- 6.5.6 DT usage -- 6.6 DT technology's characteristics -- 6.6.1 Connectivity -- 6.6.2 Homogenization -- 6.6.3 Reprogrammable -- 6.6.4 Digital traces -- 6.6.5 Modularity -- 6.7 Twin data to data -- 6.7.1 Requirements for obtaining complete data -- 6.7.2 Requirements on knowledge mining -- 6.7.3 Data fusion in real time -- 6.7.4 Data interaction in real time -- 6.7.5 Optimization in phases -- 6.7.6 On-demand data usage -- 6.7.7 Data composed of DTs -- 6.8 Data principles for DTs -- 6.8.1 Principle of complementary -- 6.8.2 The principle of standardization -- 6.8.3 The principle of timeliness -- 6.8.4 The association principle -- 6.8.5 Fusion principle -- 6.8.6 Information growth principle -- 6.8.7 The principle of servitization -- 6.9 DTD methodology -- 6.9.1 Information gathering for the DT -- 6.9.2 Data storage of DTs -- 6.9.3 DT data interaction -- 6.9.4 Association of DT data -- 6.9.5 Fusion of data from DTs -- 6.9.6 Data evolution in the DT -- 6.9.7 Data servitization for the DT -- 6.9.8 DT data's key enabler technologies -- 6.9.9 Advantages of DT -- 6.9.10 Disadvantages of DT -- 6.10 Conclusion -- References -- 7 Healthcare cyberspace: medical cyber physical system in digital twin -- 7.1 Introduction -- 7.2 Cyber physical systems -- 7.3 Digital twin -- 7.4 DT in healthcare -- 7.4.1 Patient monitoring using DT. 7.4.2 Operational efficiency in hospital using DT -- 7.4.3 Medical equipment and DT -- 7.4.4 DT in device development -- 7.5 Applications of DT in healthcare -- 7.5.1 Patient monitoring using DT -- 7.5.2 Medical wearables -- 7.5.3 Medical tests and procedures -- 7.5.4 Medical device optimization -- 7.5.5 Drug development -- 7.5.6 Regulatory services -- 7.6 DT framework in healthcare -- 7.6.1 Prediction phase -- 7.6.2 Monitoring phase -- 7.6.3 Comparison phase -- 7.7 Cyber resilience in healthcare DT -- 7.8 Cyber physical system and DT -- 7.8.1 Mapping in CPS and DTs -- 7.8.2 Unit level -- 7.8.3 System level -- 7.8.4 SoS level -- 7.9 Advantages of DT -- 7.10 Summary -- References -- 8 Cloud security-enabled digital twin in e-healthcare -- 8.1 Introduction -- 8.2 E-healthcare and cloud security-enabled digital twin -- 8.2.1 ICT facilities -- 8.2.2 Cloud security-enabled digital twin -- 8.3 Cloud healthcare service platform with digital twin -- 8.3.1 Wearable technologies -- 8.3.2 Pharmaceutical industry -- 8.3.3 Digital patients -- 8.3.4 Hospital -- 8.4 Security and privacy requirements for cloud security-enabled digital twin in e-healthcare -- 8.4.1 Security requirements for cloud security-enabled digital twin in e-healthcare -- 8.4.2 Privacy requirements for cloud security-enabled digital twin in e-healthcare -- 8.5 Challenges in cloud-based digital twin in e-healthcare -- 8.6 Conclusion -- References -- 9 Digital twin in prognostics and health management system -- 9.1 Introduction -- 9.2 Pile of DT -- 9.2.1 Digital mirror (physical infrastructure) -- 9.2.2 Digital data flow -- 9.2.3 Digital virtual thread -- 9.3 A complete DT model -- 9.4 Phases of DT development -- 9.4.1 Developing a simulation -- 9.4.2 Fusion of data -- 9.4.3 Interaction -- 9.4.4 Service -- 9.5 DT applications in healthcare -- 9.5.1 Healthcare system. 9.5.2 Recovery of the patient -- 9.5.3 Precision medicine -- 9.5.4 Research in pharmaceutical development -- 9.5.5 Drug administration -- 9.5.6 Disease treating ways -- 9.6 Challenges in DT implementation -- 9.6.1 Infrastructure for information technology -- 9.6.2 Data utilization -- 9.6.3 Consistent modeling -- 9.6.4 Modeling of domains -- 9.7 Role of DT in healthcare -- 9.7.1 Medicine that is tailored to the individual -- 9.7.2 Development of virtual organs -- 9.7.3 Medicine based on genomic data -- 9.7.4 Healthcare apps -- 9.7.5 Surgery scheduling -- 9.7.6 Increasing the effectiveness of healthcare organizations -- 9.7.7 Improving the experience of caregivers -- 9.7.8 Increasing productivity -- 9.7.9 Critical treatment window shrinking -- 9.7.10 Healthcare delivery system based on value -- 9.7.11 Rapid hospital erection -- 9.7.12 Streamlining interactions in call center -- 9.7.13 Development of pharmaceuticals and medical devices -- 9.7.14 Detecting the dangers in drugs -- 9.7.15 Simulating the new production lines -- 9.7.16 Improving the device availability -- 9.7.17 Post-sales surveillance -- 9.7.18 Human variability simulation -- 9.7.19 A lab's DT -- 9.7.20 Improving drug distribution -- 9.8 Benefits -- References -- 10 Deep learning in Covid-19 detection and diagnosis using CXR images: challenges and perspectives -- 10.1 Introduction -- 10.1.1 CNN -- 10.1.2 ANN -- 10.1.3 RNN -- 10.1.4 LSTM -- 10.1.5 GRU -- 10.1.6 Deep autoencoders -- 10.1.7 Deep Boltzmann's machine -- 10.2 Related work -- 10.2.1 Detection/localization -- 10.2.2 Segmentation -- 10.2.3 Registration -- 10.2.4 Classification -- 10.2.5 Application -- 10.3 Proposed model -- 10.3.1 Image pre-processing -- 10.3.2 Data augmentation -- 10.3.3 CNN with transfer learning -- 10.3.4 ChestXRay20 dataset -- 10.4 Experiments and result discussion -- Case 1: Covid-19 vs. healthy. Case 2: Covid-19 vs. pneumonia. |
| Record Nr. | UNINA-9911006696603321 |
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Dhanaraj Rajesh Kumar
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| Stevenage : , : Institution of Engineering & Technology, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital twins : basics and applications / / Zhihan Lv, Elena Fersman, editors
| Digital twins : basics and applications / / Zhihan Lv, Elena Fersman, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (102 pages) |
| Disciplina | 003.3 |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN | 3-031-11401-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Digital Twins Architecture -- 1 Why to Talk About Digital Twins? -- 2 The Main Digital Twin's Components -- 2.1 Physical System (PS) -- 2.2 Virtual System (VS) -- 2.3 Systems Data (SD) -- 2.4 Communication Interface (CI) -- 3 Is This a Digital Twin? -- 4 Practical Case Studies -- 4.1 Case Study I -- 4.2 Case Study II -- References -- Digital Twins for Physical Asset Lifecycle Management -- 1 Introduction -- 2 Digital Twin Asset Lifecycle Management (DTALM) -- 3 Digital Twin Essence -- 4 Digital Twin Systems -- 4.1 Physical Domain -- 4.2 Digital Domain -- 4.3 Physics-Based Generative Models for Digital Twins -- 4.4 Advances in Parameter Identifiability -- 5 Data-Driven Digital Twins -- 5.1 Statistical Learning Models -- 5.2 Machine Learning Models -- 5.3 Deep Learning Models -- 5.4 Industrial Digital Twin Applications for PALM -- References -- Digital Twins and Additive Manufacturing -- 1 Additive Manufacturing -- 2 Digital Twins -- 3 DTs for AM Needs and Challenges -- 3.1 Real Time Monitoring -- 3.2 Database and Models -- 3.3 Machine Learning -- 3.4 Internet of Things -- 4 Conclusions and Outlook -- References -- Agricultural Digital Twins -- 1 The Digital Twins of Agriculture -- 2 Digital Twins Build Smart Farms -- 2.1 Artificial Intelligence Predicts Plant Growth -- 2.2 Virtual Reality Simulation of 3D Digital Farm -- 2.3 Blockchain Technology Realizes Supply Chain Management -- 2.4 Problems that Still Exist in the Application of Digital Twins in the Agricultural Field -- 3 Conclusion -- References -- The Application of Digital Twins in the Field of Fashion -- 1 Digital Twins of Human Bodies -- 1.1 Virtual Human Models in Fashion Industry -- 1.2 Source Information for Generating Virtual Human Model -- 1.3 Tools for Virtual Body Model Digitalization -- 1.4 Virtual Fit Mannequin Generating -- 2 Digital Twins of Garment.
2.1 Structure of Virtual Fitting System -- 2.2 Generating Virtual Garment from Virtual Patterns -- 2.3 Generating Virtual Garment Directly on Virtual Human Model -- 3 Future Development -- References -- Digital Twins Collaboration in Industrial Manufacturing -- 1 Introduction -- 1.1 Contribution -- 1.2 Chapter Organization -- 2 Lightweight Framework of Digital Twins Collaboration for Industrial Manufacturing -- 2.1 Physical Layer -- 2.2 Digital Twins Layer -- 2.3 Industrial Technologies Layer -- 2.4 Application Layer -- 3 Digital Twins Collaboration in Industrial Manufacturing Use Cases -- 3.1 Energy Industry-Fault Diagnosis of Wind Turbines -- 3.2 Railway Industry-Predictive Maintenance -- 3.3 Logistics Industry-Dynamic Routing -- 4 Future Directions -- 4.1 Security and Privacy -- 4.2 Connectivity -- 4.3 Timing, Speed, and Response -- 4.4 Data Modelling -- 5 Conclusion -- References -- Social Media Perspectives on Digital Twins and the Digital Twins Maturity Model -- 1 Defining Digital Twins -- 2 Use of Social Media Analytics in Research -- 2.1 Social Media Analytics Methodology -- 2.2 Time Series Analysis of Tweets About Digital Twins -- 2.3 Unsupervised Clustering of the Digital Twin Tweets -- 2.4 Twitter Analysis by Industry -- 3 Background on Maturity Models -- 4 The Digital Twin Maturity Model -- 5 Conclusion and Future Work -- References. |
| Record Nr. | UNISA-996499855103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Digital Twins and Cybersecurity : Safeguarding the Future of Connected Systems
| Digital Twins and Cybersecurity : Safeguarding the Future of Connected Systems |
| Autore | Naveen Palanichamy |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (503 pages) |
| Disciplina | 003/.3 |
| Altri autori (Persone) |
MaheswarR
RagupathyU. S |
| Collana | Next-generation computing and communication engineering |
| Soggetto topico |
Digital twins (Computer simulation)
Computer security |
| ISBN |
9781394272488
1394272480 9781394272501 1394272502 9781394272495 1394272499 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 Introduction to the Concept of Digital Twins and Cybersecurity -- 1.2 Significance of Integrating Digital Twins and Cybersecurity -- 1.2.1 Protection of Physical Assets -- 1.2.2 Mitigation of Operational Risks -- 1.2.3 Prevention of Data Breaches -- 1.2.4 Prevention of Cyber-Physical Attacks -- 1.2.5 Facilitation of Trust and Adoption -- 1.2.6 Compliance with Regulations and Standards -- 1.2.7 Future-Proofing and Resilience -- 1.2.8 An Overview of the Book's Structure and Content -- Chapter 2 Understanding Digital Twins -- 2.1 Definition of Digital Twins -- 2.2 Evolution of Digital Twins -- 2.3 Various Types of Digital Twins -- 2.3.1 Product Digital Twins -- 2.3.2 Process Digital Twins -- 2.3.3 System Digital Twins -- 2.3.4 Human Digital Twins -- 2.4 Applications in Different Industries -- 2.4.1 Manufacturing Industry -- 2.4.2 Healthcare Industry -- 2.4.3 Energy and Utilities Industry -- 2.4.4 Transportation Industry -- Chapter 3 The Importance of Cybersecurity -- 3.1 Growing Threats in the Digital Landscape -- 3.1.1 Impact and Consequences -- 3.1.2 Emerging Threats -- 3.2 Significance of Cybersecurity in Protecting Digital Twins -- 3.2.1 Introduction to Digital Twins and Cybersecurity -- 3.2.2 Best Practices for Cybersecurity in Protecting Digital Twins -- 3.3 Potential Consequences of Cyberattacks on Digital Twins -- 3.3.1 Case Studies and Examples -- 3.3.2 Mitigating the Consequences -- Chapter 4 Digital Twin Architecture -- 4.1 Key Components and Infrastructure of Digital Twins -- 4.1.1 Data Collection and Sensors -- 4.1.2 Communication Networks -- 4.1.3 Data Storage and Processing -- 4.1.4 Modeling and Simulation Engines -- 4.1.5 Visualization and User Interface -- 4.1.6 Analytics and Decision Support.
4.1.7 Integration with Physical Systems -- 4.1.8 Cybersecurity Infrastructure -- 4.1.9 Scalable and Resilient Architecture -- 4.1.10 Data Governance and Standards -- 4.2 Data Flow and Communication Channels -- 4.2.1 Data Collection -- 4.2.2 Data Transmission -- 4.2.3 Data Pre-Processing -- 4.2.4 Data Storage -- 4.2.5 Data Processing and Analysis -- 4.2.6 Simulation and Optimization -- 4.2.7 Visualization and User Interfaces -- 4.2.8 Control and Actuation -- 4.2.9 Feedback and Iteration -- 4.2.10 Cybersecurity Considerations -- 4.3 Vulnerabilities and Security Considerations in the Architecture -- 4.3.1 Data Collection and Sensors -- 4.3.2 Communication Networks -- 4.3.3 Data Storage and Processing -- 4.3.4 Integration with Physical Systems -- 4.3.5 Visualization and User Interfaces -- 4.3.6 Third-Party Integrations -- 4.3.7 Insider Threats -- 4.3.8 Scalability and Resilience -- 4.3.9 Continuous Monitoring and Incident Response -- 4.3.10 Compliance and Standards -- Chapter 5 Cybersecurity Framework for Digital Twins -- 5.1 Introduction -- 5.1.1 Risk Assessment and Threat Modeling -- 5.1.2 Secure Architecture Design -- 5.1.3 Identity and Access Management -- 5.1.4 Data Security and Privacy -- 5.1.5 Secure Communication Channels -- 5.1.6 Vulnerability Management -- 5.1.7 Incident Response and Recovery -- 5.1.8 Continuous Monitoring and Threat Intelligence -- 5.1.9 Security Awareness and Training -- 5.1.10 Third-Party Risk Management -- 5.2 Key Principles and Best Practices -- 5.2.1 Defense in Depth -- 5.2.2 Least Privilege -- 5.2.3 Secure Configuration -- 5.2.4 Patch Management -- 5.2.5 Secure Development Life Cycle -- 5.2.6 Continuous Monitoring -- 5.2.7 Encryption -- 5.2.8 Access Control -- 5.2.9 Incident Response -- 5.2.10 Employee Awareness and Training -- 5.2.11 Third-Party Risk Management -- 5.2.12 Compliance. 5.3 Guidelines for Implementing Security Measures -- 5.3.1 Establish a Security Policy -- 5.3.2 Implement Access Controls -- 5.3.3 Encrypt Data -- 5.3.4 Secure Network Infrastructure -- 5.3.5 Regularly Update and Patch Systems -- 5.3.6 Implement Monitoring and Logging -- 5.3.7 Conduct Regular Security Assessments -- 5.3.8 Establish an Incident Response Plan -- 5.3.9 Train Employees on Security Best Practices -- 5.3.10 Implement Vendor Risk Management -- 5.3.11 Regularly Review and Improve Security Measures -- Chapter 6 Securing Data in Digital Twins -- 6.1 Challenges of Securing Data Within Digital Twins -- 6.1.1 Data Privacy -- 6.1.2 Data Integrity -- 6.1.3 Data Access Control -- 6.1.4 Data Integration and Interoperability -- 6.1.5 Data Storage and Retention -- 6.1.6 Data Sharing and Collaboration -- 6.1.7 Data Governance and Compliance -- 6.1.8 Data Life Cycle Management -- 6.1.9 Insider Threats -- 6.1.10 Emerging Technologies and Risks -- 6.2 Encryption Techniques and Data Protection Mechanisms -- 6.2.1 Symmetric Encryption -- 6.2.2 Asymmetric Encryption -- 6.2.3 Hash Functions -- 6.2.4 Digital Signatures -- 6.2.5 Transport Layer Security (TLS) -- 6.2.6 Virtual Private Networks (VPNs) -- 6.2.7 Data Masking -- 6.2.8 Access Control and Authentication -- 6.2.9 Data Loss Prevention (DLP) -- 6.2.10 Secure Key Management -- 6.2.11 Data Backup and Disaster Recovery -- 6.2.12 Data Retention and Destruction -- 6.3 Strategies for Ensuring Data Integrity and Confidentiality -- 6.3.1 Encryption -- 6.3.2 Access Controls -- 6.3.3 Secure Key Management -- 6.3.4 Secure Data Transmission -- 6.3.5 Data Anonymization and Pseudonymization -- 6.3.6 Data Loss Prevention (DLP) -- 6.3.7 Regular Audits and Monitoring -- 6.3.8 Data Backup and Recovery -- 6.3.9 Data Retention and Destruction Policies -- 6.3.10 Employee Training and Awareness. 6.3.11 Vendor and Third-Party Management -- Chapter 7 Authentication and Access Control -- 7.1 Importance of Robust Authentication Mechanisms -- 7.1.1 Prevent Unauthorized Access -- 7.1.2 Protect Sensitive Information -- 7.1.3 Mitigate Password-Related Risks -- 7.1.4 Multi-Factor Authentication (MFA) -- 7.1.5 Protection Against Credential Theft -- 7.1.6 Compliance with Regulatory Requirements -- 7.1.7 Safeguarding Remote Access -- 7.1.8 User Accountability and Auditing -- 7.1.9 Enhancing Trust and User Confidence -- 7.1.10 Future-Proofing Security -- 7.2 Access Control Models and Techniques -- 7.2.1 Access Control Models -- 7.2.2 Access Control Techniques -- 7.2.3 Challenges and Considerations -- 7.3 Multi-Factor Authentication and Biometrics in Digital Twins -- 7.3.1 Multi-Factor Authentication -- 7.3.2 Biometrics -- Chapter 8 Threat Detection and Incident Response -- 8.1 Importance of Proactive Threat Detection -- 8.1.1 Early Threat Identification -- 8.1.2 Mitigating Financial Losses -- 8.1.3 Protecting Sensitive Data -- 8.1.4 Maintaining Business Continuity -- 8.1.5 Enhancing Incident Response Capabilities -- 8.1.6 Meeting Regulatory and Compliance Requirements -- 8.1.7 Strengthening Cybersecurity Posture -- 8.1.8 Gaining Situational Awareness -- 8.2 Techniques for Identifying Security Breaches in Digital Twins -- 8.2.1 Intrusion Detection Systems (IDS) -- 8.2.2 Log Analysis and Security Information and Event Management (SIEM) -- 8.2.3 Behavioral Analytics -- 8.2.4 Threat Intelligence -- 8.2.5 Anomaly Detection -- 8.2.6 Penetration Testing -- 8.2.7 User and Entity Behavior Analytics -- 8.2.8 Endpoint Detection and Response -- 8.3 Guidelines for Incident Response and Recovery -- Chapter 9 Securing Communication in Digital Twins -- 9.1 Introduction -- 9.1.1 Importance of Secure Communication Protocols. 9.1.2 Commonly Used Secure Communication Protocols -- 9.1.3 Encryption Algorithms -- 9.2 The Role of Secure Gateways and Firewalls -- 9.2.1 Traffic Monitoring and Filtering -- 9.2.2 Access Control and Policy Enforcement -- 9.2.3 Network Segmentation and Isolation -- 9.2.4 Threat Prevention and Intrusion Detection/ Prevention -- 9.2.5 Virtual Private Network (VPN) Support -- 9.2.6 Application-Level Gateway and Proxy Services -- 9.2.7 Logging and Auditing -- 9.3 Importance of Network Segmentation and Isolation -- 9.3.1 Limiting Lateral Movement -- 9.3.2 Enhanced Security and Access Control -- 9.3.3 Compartmentalizing Sensitive Information -- 9.3.4 Compliance and Regulatory Requirements -- 9.3.5 Containment of Security Incidents -- 9.3.6 Improved Performance and Availability -- 9.3.7 Simplified Network Management -- Chapter 10 Privacy Considerations -- 10.1 Privacy Challenges Associated with Digital Twins -- 10.1.1 Data Collection and Retention -- 10.1.2 Informed Consent and Transparency -- 10.1.3 Data Ownership and Control -- 10.1.4 Data Security and Unauthorized Access -- 10.1.5 Data Anonymization and De-Identification -- 10.1.6 Cross-Border Data Transfer -- 10.1.7 Algorithmic Transparency and Bias -- 10.2 Privacy Regulations and Compliance Requirements -- 10.2.1 General Data Protection Regulation -- 10.2.2 California Consumer Privacy Act -- 10.2.3 Personal Information Protection and Electronic Documents Act -- 10.2.4 Health Insurance Portability and Accountability Act -- 10.2.5 Personal Data Protection Act -- 10.2.6 Australian Privacy Principles -- 10.2.7 Cross-Border Data Transfer Mechanisms -- 10.3 Recommendations for Ensuring Privacy in Digital Twin Deployments -- 10.3.1 Privacy by Design -- 10.3.2 Data Minimization and Purpose Limitation -- 10.3.3 Informed Consent -- 10.3.4 Data Security -- 10.3.5 Anonymization and De-Identification. 10.3.6 Transparency and Individual Rights. |
| Record Nr. | UNINA-9911020227803321 |
|
Naveen Palanichamy
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Twins For 6G : Fundamental Theory, Technology and Applications
| Digital Twins For 6G : Fundamental Theory, Technology and Applications |
| Autore | Ahmadi Hamed |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2024 |
| Descrizione fisica | 1 online resource (321 pages) |
| Disciplina | 621.38456 |
| Altri autori (Persone) |
DuongTrung Q
NagAvishek SharmaVishal CanberkBerk DobreOctavia A |
| Collana | Telecommunications Series |
| Soggetto topico |
Digital twins (Computer simulation)
6G mobile communication systems |
| ISBN |
9781523163120
1523163127 9781839537462 1839537469 9781839537455 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contents -- About the editors -- Preface -- 1. Digital twins for resilient and reliable 6G networks | Fahad Alaklabi, Ahmed Al-Tahmeesschi, Avishek Nag and Hamed Ahmadi -- 2. Digital twin-enabled aerial edge networks with ultra-reliable low-latency communications | Dang Van Huynh, Yijiu Li, Tan Do-Duy, Emi Garcia-Palacios and Trung Q. Duong -- 3. AI-enabled data management for digital twin networks | Elif Ak, Gökhan Yurdakul, Ahmed Al-Dubai and Berk Canberk -- 4. AI-based traffic analysis in digital twin networks | Sarah Al-Shareeda, Khayal Huseynov, Lal Verda Cakir, Craig Thomson, Mehmet Ozdem and Berk Canberk -- 5. Digital twin empowered Open RAN of 6G networks | Antonino Masaracchia, Vishal Sharma, Muhammad Fahim, Octavia A. Dobre and Trung Q. Duong -- 6. Potentials of the digital twin in 6G communication systems | Bin Han, Mohammad Asif Habibi, Nandish Kuruvatti, Sanket Partani, Amina Fellan and Hans D. Schotten -- 7. Digital twins for optical networks | Agastya Raj, Dan Kilper and Marco Ruffini -- 8. Dynamic decomposition of service function chain using a deep reinforcement learning approach | Swarna B. Chetty, Hamed Ahmadi, Massimo Tornatore and Avishek Nag -- 9. An Optimization-as-a-Service platform for 6G exploiting network digital twins | Oriol Sallent, José-Manuel Martínez-Caro, Javier Baliosian, Luis Diez, Luis M. Contreras, Jordi Pérez-Romero, Juan Luis Gorricho, Matías Richart, Ramón Agüero, Joan Serrat, Pablo Pavón-Mariño and Irene Vilà -- 10. Robotics digital twin for 6G | Milan Groshev, Carlos Guimarães and Antonio de la Oliva -- Index |
| Record Nr. | UNINA-9911006666903321 |
|
Ahmadi Hamed
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| Stevenage : , : Institution of Engineering & Technology, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital twins in construction and the built environment / / edited by Houtan Jebelli [and 4 others]
| Digital twins in construction and the built environment / / edited by Houtan Jebelli [and 4 others] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Reston : , : American Society of Civil Engineers, , 2024 |
| Descrizione fisica | 1 online resource (301 pages) |
| Altri autori (Persone) |
JebelliHoutan
AsadiSomayeh MutisIvan LiuRui |
| Soggetto topico |
Building - Technological innovations
Digital twins (Computer simulation) Building information modeling |
| ISBN |
9780784485613
0784485615 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Contributors -- Acknowledgments -- Preface -- Acronyms -- Chapter 1: State of the Art of Digital Twins for Built Environments -- 1.1 Introduction -- 1.2 Research Methodology -- 1.3 Data Analysis and Discussion -- 1.3.1 Digital Twin Conceptualization -- 1.3.1.1 Definitions of Digital Twin -- 1.3.1.2 Types of Digital Twin -- 1.3.1.3 Characteristics of Digital Twin -- 1.3.1.4 Components of Digital Twin -- 1.3.1.5 Digital Twin.’.s System Architecture -- 1.3.2 A Comparison between Building Information Modeling and Digital Twin -- 1.3.2.1 Data Requirements -- 1.3.2.2 Building Information Modeling Uses versus Digital Twin Enterprise Solution -- 1.3.2.3 Scales of Technology Deployment -- 1.3.3 Benefits and Challenges of Digital Twin.’.s Adoption -- 1.3.3.1 Digital Twin Adoption.’.s Perceived Benefits and Opportunities -- 1.3.3.2 Digital Twin Adoption.’.s Challenges and Barriers -- 1.3.4 Strategic Planning for Digital Twin Implementation -- 1.3.4.1 Digital Twin.’.s Stakeholders and Strategies -- 1.3.4.2 Digital Twin Technology Stack -- 1.3.4.3 Digital Twin.’.s Maturity Models |
| Record Nr. | UNINA-9911016149603321 |
| Reston : , : American Society of Civil Engineers, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Simulation Techniques of Digital Twin in Real-Time Applications : Design Modeling and Implementation
| Simulation Techniques of Digital Twin in Real-Time Applications : Design Modeling and Implementation |
| Autore | Anand Abhineet |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (372 pages) |
| Disciplina | 003/.3 |
| Altri autori (Persone) |
SardanaAnita
KumarAbhishek MohapatraSrikanta Kumar GuptaShikha |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN |
9781394257003
1394257007 9781394256990 139425699X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Part 1: A Guide to Simulated Techniques in Digital Twin -- Chapter 1 Introduction to Different Simulation Techniques of Digital Twin Development -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Digital Twin Simulation Techniques -- 1.3.1 Finite Element Analysis Simulation -- 1.3.2 Computational Fluid Dynamics Simulation -- 1.3.3 Discrete Event Simulation -- 1.3.4 Agent-Based Modeling Simulation -- 1.3.5 Multi-Body Dynamics Simulation -- 1.3.6 Monte Carlo Simulation -- 1.4 Conclusion -- References -- Chapter 2 Comprehensive Analysis of Error Rate and Channel Capacity of Fisher Snedecor Composite Fading Model -- 2.1 Introduction -- 2.2 Fisher Snedecor Composite Fading -- 2.3 Mathematical Analysis -- 2.3.1 Error Rate Analysis -- 2.3.1.1 NCBFSK and BDPSK -- 2.3.1.2 BPSK, BFSK, and QPSK -- 2.3.1.3 MQAM -- 2.3.1.4 MPSK -- 2.3.1.5 MDPSK -- 2.3.1.6 NCMFSK -- 2.3.1.7 DQPSK -- 2.3.2 Channel Capacity Analysis -- 2.3.2.1 ORA -- 2.3.2.2 OPRA -- 2.3.2.3 CIFR -- 2.3.2.4 TIFR -- 2.4 Numerical Results -- 2.5 Conclusion -- References -- Chapter 3 Implementation of Automatic Driving Car Test Approach Based on a Digital Twinning Technology and by Embedding Artificial Intelligence -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Comparative Analysis -- 3.4 Result -- 3.5 Concluding Remarks and Future Scope -- References -- Chapter 4 Intelligent Monitoring of Transformer Equipment in Terms of Earlier Fault Diagnosis Based on Digital Twins -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Arduino Uno -- 4.2.2 ESP32 Microcontroller -- 4.2.3 Data Acquisition -- 4.2.4 Blynk App -- 4.3 Machine Learning-Based Predictive Maintenance -- 4.4 Results and Discussion -- 4.5 Conclusion and Future Work -- References.
Chapter 5 Digital Twin System for Intelligent Construction of Large Span Assembly Type Steel Bridge -- 5.1 Introduction -- 5.1.1 Digital Twin Technology -- 5.1.2 Technologies Used -- 5.1.3 Why Digital Twin? -- 5.1.4 Types of Digital Twins -- 5.2 Deep Learning -- 5.2.1 Types of Deep Neural Networks -- 5.2.2 Learning or Training in Neural Networks -- 5.3 Simulation vs. Digital Twin Technology -- 5.3.1 Integrating Deep Learning in Simulation Models -- 5.3.2 Benefits of Deep Learning Digital Twin -- 5.3.3 Applications of Digital Twin Technology -- 5.4 Literature Review -- 5.5 Conclusion -- References -- Chapter 6 Digital Twin Application on System Identification and Control -- 6.1 Introduction -- 6.2 Digital Twin Technology and Its Application -- 6.2.1 Related Work on Digital Twin -- 6.2.2 DT Application -- 6.2.3 Different Levels of DT Models -- 6.2.3.1 Pre-Digital Twin -- 6.2.3.2 Model Design -- 6.2.3.3 Adaptive Model With DT Technology -- 6.2.3.4 The Process of Intelligent DT -- 6.2.4 Dynamic Model -- 6.2.5 Digital Twin and Machine Learning -- 6.3 Control and Identification: A Survey -- 6.3.1 Hierarchy of System Identification Methods -- 6.3.1.1 Parametric Methods -- 6.3.1.2 Nonparametric Methods -- 6.3.2 Machine Learning Approach -- 6.3.3 Deep Neural Network Approach -- 6.4 Proposed Methodology -- 6.4.1 DT Technology Application in Identification and Control -- 6.5 Result Analysis and Discussion -- 6.5.1 Case Study: Control Application -- 6.6 Conclusion and Future Work -- References -- Part 2: Real Time Applications of Digital Twin -- Chapter 7 Digital Twinning-Based Autonomous Take-Off, Landing, and Cruising for Unmanned Aerial Vehicles -- 7.1 Introduction -- 7.1.1 Problem Statement -- 7.1.2 Research Objectives -- 7.2 Digital Twinning for UAV Autonomy -- 7.3 Challenges and Limitations -- 7.3.1 Manual Control and Pre-Programmed Flight Paths. 7.3.2 Limited Adaptability to Dynamic Environments -- 7.3.3 Lack of Real-Time Decision-Making -- 7.3.4 Limited Perception and Situational Awareness -- 7.3.5 Computational Complexity and Processing Power -- 7.3.6 Calibration and Validation -- 7.4 Proposed Framework -- 7.4.1 Digital Twin Creation -- 7.4.2 Sensor Fusion and Data Acquisition -- 7.4.3 Environmental Analysis -- 7.4.4 Decision-Making and Control -- 7.4.5 Communication and Synchronization -- 7.4.6 Validation and Calibration -- 7.4.7 Iterative Improvement -- 7.5 Benefits and Feasibility -- 7.5.1 Improved Adaptability -- 7.5.2 Real-Time Decision-Making -- 7.5.3 Enhanced Safety -- 7.5.4 Feasibility Considerations -- 7.6 Conclusion and Future Directions -- References -- Chapter 8 Execution of Fully Automated Coal Mining Face With Transparent Digital Twin Self-Adaptive Mining System -- 8.1 Introduction -- 8.2 Simulation Methods in Digital Twins -- 8.2.1 Computational Fluid Dynamics -- 8.2.1.1 Software Tools That are Being Used in Today's Domain for CFD -- 8.2.1.2 Real-World Applications of CFD -- 8.2.2 Multibody Dynamics -- 8.2.3 Kinematics for Multibody Systems -- 8.3 Literature Review -- 8.3.1 Classification of MBD Simulations -- 8.3.2 Finite Element Analysis -- 8.4 Proposed Work -- 8.5 Conclusion -- References -- Chapter 9 MGF-Based BER and Channel Capacity Analysis of Fisher Snedecor Composite Fading Model -- 9.1 Introduction -- 9.2 Fisher Snedecor Composite Fading Model -- 9.3 Performance Analysis Using MGF -- 9.3.1 ABER -- 9.3.1.1 BDPSK and NBFSK -- 9.3.1.2 BPSK and BFSK -- 9.3.1.3 MAM -- 9.3.1.4 Square MQAM -- 9.3.1.5 MPSK -- 9.3.2 NMFSK -- 9.3.3 Adaptive Channel Capacity -- 9.3.3.1 ORA -- 9.3.3.2 CIFR -- 9.4 Numerical Results -- 9.5 Conclusion -- References. Chapter 10 Precision Agriculture: An Augmented Datasets and CNN Model-Based Approach to Diagnose Diseases in Fruits and Vegetable Crops -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Major Fruit Diseases in the Valley -- 10.4 Methodology -- 10.5 Results and Discussion -- 10.6 Extended Experiment -- 10.7 Concluding Remarks -- References -- Chapter 11 A Simulation-Based Study of a Digital Twin Model of the Air Purifier System in Chandigarh Using LabVIEW -- 11.1 Introduction -- 11.1.1 Background Information on Chandigarh's Air Pollution Problem -- 11.1.2 Digital Twin Technology and Its Relevance to Air Quality Monitoring -- 11.2 Literature Review -- 11.3 Methodology -- 11.4 Results -- 11.5 Discussion -- 11.6 Conclusion -- References -- Chapter 12 Use of Digital Twin in Predicting the Life of Aircraft Main Bearing -- 12.1 Introduction -- 12.1.1 Background -- 12.1.2 Importance of Predictive Maintenance -- 12.1.3 Challenges in Aircraft Main Bearing Life Prediction -- 12.1.4 Digital Twin Technology in Aviation -- 12.2 Fundamentals of Digital Twin Technology -- 12.2.1 Components of a Digital Twin -- 12.2.2 Enabling Technologies for Digital Twin -- 12.3 Benefits of Digital Twin Technology -- 12.3.1 Aircraft Main Bearings: Structure and Failure Modes -- 12.4 Developing a Digital Twin for Aircraft Main Bearings -- 12.5 Predictive Analytics for Main Bearing Life Prediction -- 12.5.1 Machine Learning Algorithms for Predictive Modeling -- 12.5.2 Challenges of Digital Twin for Aircraft Health -- 12.5.3 Security Threats of the Digital Twin in Aircraft Virtualization -- 12.6 Future Prospects and Conclusion of Digital Twin for Aircraft Health -- References -- Chapter 13 Power Energy System Consumption Analysis in Urban Railway by Digital Twin Method -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Method -- 13.4 Implementation -- 13.5 Conclusion. References -- Chapter 14 Based on Digital Twin Technology, an Early Warning System and Strategy for Predicting Urban Waterlogging -- 14.1 Introduction -- 14.1.1 Definition -- 14.1.2 Application Areas of Digital Twin Technology -- 14.2 Literature Review -- 14.3 Methodology -- 14.4 Discussion and Conclusion -- References -- Chapter 15 Advanced Real-Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms -- 15.1 Introduction -- 15.2 Introduction to Advanced Simulation Frameworks -- 15.2.1 The Evolution of Production Line Simulations -- 15.2.2 The Promise of Real-Time Analysis -- 15.3 Digital Twins: A Comprehensive Analysis -- 15.3.1 What Defines a Digital Twin? -- 15.3.2 The Architecture and Components of Digital Twins -- 15.3.3 Advantages of Integrating Digital Twins in Manufacturing -- 15.4 Physical Interaction Dynamics in Production Lines -- 15.4.1 The Nature of Physical Interactions -- 15.4.2 The Role of Dynamics in Production Efficiency -- 15.4.3 Challenges in Traditional Simulation Methods -- 15.5 Building the Advanced Real-Time Simulation Framework -- 15.5.1 Core Principles and Design Objectives -- 15.5.2 Data Integration and Processing -- 15.5.2.1 Role of Sensors and IoT -- 15.5.2.2 Algorithmic Foundations for Feedback -- 15.6 Types of Algorithms -- 15.6.1 Pseudocode for Real-Time Adjustments -- 15.6.1.1 Initialization -- 15.6.1.2 Data Collection and Pre-Processing -- 15.6.1.3 Analysis Using Bayesian Inference -- 15.6.1.4 Anomaly Detection and Root Cause Analysis -- 15.6.1.5 Corrective Action Using Gradient Boosting -- 15.6.1.6 Update and Implement -- 15.6.1.7 Continuous Monitoring -- 15.7 Practical Implementations and Case Studies -- 15.7.1 Implementing the Framework: A Step-by-Step Guide -- 15.7.2 Measurable Benefits and Outcomes -- 15.8 Overcoming Challenges and Limitations. 15.8.1 Potential Roadblocks in Framework Implementation. |
| Record Nr. | UNINA-9911020087903321 |
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Anand Abhineet
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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