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ADAS and automated driving : a practical approach to verification and validation / / by Plato Pathrose
| ADAS and automated driving : a practical approach to verification and validation / / by Plato Pathrose |
| Autore | Pathrose Plato |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Warrendale, Pennsylvania : , : SAE International, , [2022] |
| Descrizione fisica | 1 online resource (1 PDF (xxi, 255 pages)) : illustrations ; ; cm |
| Disciplina | 629.2 |
| Soggetto topico |
Automated vehicles
Driver assistance systems TRANSPORTATION / Automotive / General TECHNOLOGY & ENGINEERING / Automation TECHNOLOGY & ENGINEERING / Automotive Road and motor vehicles: general interest Automatic control engineering Automotive technology and trades |
| ISBN |
9781523149544
152314954X 9781468604146 1468604147 9781468604139 1468604139 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword -- Introduction -- About this book -- Assumptions -- Acknowledgments -- Chapter 1: Introduction to advanced driver assistance systems and automated driving -- Chapter 2: Design approaches for automated driving systems -- Chapter 3: Different test approaches -- Chapter 4: Scenario-based testing -- Chapter 5: Simulation environment for ADAS and automated driving systems -- Chapter 6: Ground truth generation and testing neural network-based detection -- Chapter 7: Testing and qualification of perception software -- Chapter 8: Calibration of ADAS and automated driving features -- Chapter 9: Introduction to functional safety and cybersecurity testing -- Chapter 10: Verification and validation strategy Chapter 11: Acceptance criteria and maturity evaluation -- Chapter 12: Data flow and management in automated driving -- Chapter 13: Challenges and gaps in testing automated driving features -- Index -- About the author. |
| Altri titoli varianti | ADAS and Automated Driving |
| Record Nr. | UNINA-9911007247603321 |
Pathrose Plato
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| Warrendale, Pennsylvania : , : SAE International, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Design of linear multivariable feedback control systems : the Wiener-Hopf approach using transforms and spectral factorization / / Joseph J. Bongiorno Jr., Kiheon Park
| Design of linear multivariable feedback control systems : the Wiener-Hopf approach using transforms and spectral factorization / / Joseph J. Bongiorno Jr., Kiheon Park |
| Autore | Bongiorno Jr Joseph J |
| Edizione | [1st edition 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xi, 453 pages) : illustrations |
| Disciplina | 629.83 |
| Soggetto topico |
Automatic control
System theory Automatic control engineering |
| ISBN | 3-030-44356-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Stabilizing Controllers, Tracking, and Disturbance Rejection -- Chapter 3. H2 Design of Multivariable Control Systems -- Chapter 4. H2 Design of Multivariable Control Systems with Decoupling -- Chapter 5. Numerical Calculation of Wiener-Hopf Controllers. |
| Record Nr. | UNINA-9910483837203321 |
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Bongiorno Jr Joseph J
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fundamentals of connected and automated vehicles / / by Jeffrey Wishart and Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu and Yezhou Yang
| Fundamentals of connected and automated vehicles / / by Jeffrey Wishart and Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu and Yezhou Yang |
| Autore | Wishart Jeffrey |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Warrendale, Pennsylvania : , : SAE International, , [2022] |
| Descrizione fisica | 1 online resource (1 PDF (xiii, 257 pages)) : illustrations |
| Disciplina | 629.046 |
| Soggetto topico |
Automated vehicles
Automated vehicles - Technological innovations Deep learning (Machine learning) Multisensor data fusion TECHNOLOGY & ENGINEERING / Automation TRANSPORTATION / Automotive / General TECHNOLOGY & ENGINEERING / Automotive COMPUTERS / Artificial Intelligence / General Automatic control engineering Road and motor vehicles: general interest Automotive technology and trades Artificial intelligence |
| ISBN |
9781523149483
1523149485 9780768099829 076809982X 9780768099843 0768099846 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction and history of connected and automated vehicles -- Chapter 2. Localization -- Chapter 3. Connectivity -- Chapter 4. Sensor and actuator hardware -- Chapter 5. Computer vision -- Chapter 6. Sensor fusion -- Chapter 7. Path planning and motion control -- Chapter 8. Verification and validation -- Chapter 9. Outlook. |
| Record Nr. | UNINA-9911007131603321 |
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Wishart Jeffrey
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| Warrendale, Pennsylvania : , : SAE International, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fundamentals of engineering high-performance actuator systems / / by Kenneth W. Hummel
| Fundamentals of engineering high-performance actuator systems / / by Kenneth W. Hummel |
| Autore | Hummel Kenneth W. |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Warrendale, Penn. : , : Society of Automotive Engineers, , [2017] |
| Descrizione fisica | 1 online resource (ix, 214 pages) : illustrations |
| Disciplina | 629.8 |
| Collana | Society of Automotive Engineers. Electronic publications |
| Soggetto topico |
Actuators
Automatic control TECHNOLOGY & ENGINEERING / Automation Automatic control engineering |
| ISBN |
0-7680-8866-6
0-7680-8363-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Project management -- Chapter 3. Requirements analysis -- Chapter 4. Design to requirements -- Chapter 5. Power sources --Chapter 6. Prototyping -- Chapter 7. Verification and validation -- Chapter 8. Production -- Bibliography -- Appendix A: Hydraulic symbols -- Training supplement - problems by chapter -- About the author -- Index. |
| Record Nr. | UNINA-9910886187803321 |
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Hummel Kenneth W.
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| Warrendale, Penn. : , : Society of Automotive Engineers, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Kommunikation und Bildverarbeitung in der Automation : Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020
| Kommunikation und Bildverarbeitung in der Automation : Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020 |
| Autore | Jasperneite Jürgen |
| Pubbl/distr/stampa | Berlin, Heidelberg, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (333 pages) |
| Altri autori (Persone) | LohwegVolker |
| Collana | Technologien Für Die Intelligente Automation |
| Soggetto topico |
Communications engineering / telecommunications
Imaging systems & technology Automatic control engineering Robotics |
| Soggetto non controllato |
Industrielle Kommunikationstechnik
Industrielle Bildverarbeitung Network reliability and redundancy methods Networked Control Systems Wireless real-time communication |
| ISBN | 3-662-64283-2 |
| Classificazione | TEC004000TEC008000TEC037000TEC041000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organisation -- Communication in Automation - KommA 2020 -- Conference Chairs -- Program Committee -- Organising Committee -- Organisation -- Image Processing in Automation - BVAu 2020 -- Conference Chair -- Program Committee -- Inhaltsverzeichnis -- Contributors -- Part I Communication in Automation -- A Remote Attack Tool Against Siemens S7-300 Controllers: A Practical Report -- 1 Introduction -- 2 Related Work -- 3 Experimental Set-up -- 3.1 The Physical Process to Be Controlled -- 3.2 Hardware Equipment -- 3.3 Attacker Model and Attack Surface -- 4 Attack Details, Implementation and Results -- 4.1 Reconnaissance Attack -- 4.2 Scanning the PLC In-depth -- 4.3 Authentication Bypass Attack -- 4.4 Replay Attacks -- 4.4.1 Set/Update the password of PLCs -- 4.4.2 Clear PLC's Memory Blocks -- 4.4.3 Start/Stop the PLC -- 4.5 Control Hijacking Attack -- 5 Possible Mitigation Solutions -- 6 Conclusion and Future Work -- References -- Konzept und Implementierung einer kommunikationsgetriebenen Verwaltungsschale auf effizienten Geräten in Industrie 4.0 Kommunikationssystemen -- 1 Einleitung -- 2 Industrieller Use Case -- 3 Stand der Technik -- 3.1 Time-sensitive Networking -- 3.2 OPC UA -- 4 Related Work -- 4.1 Administration Shell -- 4.2 OPC UA und TSN -- 5 Konzept und Implementierung -- 5.1 Konzept Communication Administration Shell -- 5.2 Implementierung der CAS und Datenservices für Produktionsgeräte -- 5.3 Integration in industriellen Use Case -- 6 Validierung -- 7 Fazit -- Literatur -- Device Management in Industrial IoT -- 1 Introduction -- 2 Aufgaben von und Anforderungen an IoT Gerätemanagement -- 2.1 Gruppe 1: Bereitstellung und Registrierung -- 2.2 Gruppe 2: Konfiguration und Steuerung -- 2.3 Gruppe 3: Aktualisierung und Wartung -- 2.4 Gruppe 4: Monitoring und Diagnose -- 2.5 Gruppe 5: Hilfsfunktionen.
2.6 Gruppe 6: Interoperabilität -- 3 Ansätze von IoT Geräte Management -- 4 Evaluation -- 4.1 Bewertungskriterien -- 4.2 Ergebnisse -- 5 Zusammenfassung und Ausblick -- Literatur -- Cross-Company Data Exchange with Asset Administration Shells and Distributed Ledger Technology -- 1 Introduction -- 2 Background -- 2.1 Asset Administration Shell: Fundamentals -- 2.2 Distributed Ledger Technology -- 3 Model Architecture -- 3.1 Current State -- 3.2 Proposed Idea -- 4 Implementation -- 5 Evaluation -- 6 Discussion -- 7 Conclusions -- References -- Plug and Work with OPC UA at the Field Level: Integration of Low-Level Devices -- 1 Introduction -- 2 Review Focus -- 2.1 QoS Requirements of Distributed Applications -- 2.2 System Requirements of Automation Ecosystems -- 3 Specification Review -- 3.1 Field Level Communications Initiative -- 3.2 IEC/IEEE 60802 Profile for Industrial Automation -- 3.3 Reflection -- 4 Impact on Low-Level System Engineering -- 4.1 Device-Oriented Engineering -- 4.2 Function-Oriented Engineering -- 4.3 Identified Effects -- 5 Summary -- References -- Concept for Rule-Based Information Aggregation in Modular Production Plants -- 1 Introduction -- 2 State of the Art -- 3 Concept for Rule-Based Information Aggregation -- 3.1 Structure of the Concept -- 3.2 Classification Method -- 3.3 Rule Engine -- 4 Concept Implementation for a Specific Use Case -- 4.1 Use Case: Fidget Spinner Production -- 4.2 Applying Classification -- 4.3 Applying the Rule Engine -- 5 Conclusion and Future Work -- References -- Towards Real-Time Human-Machine Interfaces for Robot Cells Using Open Standard Web Technologies -- 1 Motivation -- 2 Implementation -- 3 Results -- 4 Summary -- References -- Interoperabilität von Cyber Physical Systems -- 1 New Requirements for Interoperability -- 2 What Is Interoperability? -- 3 General Interoperability Concept. 4 State of the Art of the Interoperability Levels -- 4.1 Technical and Syntactical Interoperability Levels -- 4.2 Semantical Interoperability Level -- 4.3 Organizational Interoperability Level -- 5 Relation Between Technologies and Interoperability Levels -- 5.1 Interoperability Aspects of Asset Administration Shells -- 5.2 Mapping of Selected Technologies into Interoperability Levels -- 6 Summary -- References -- Automatische Bewertung und Uberwachung von Safety Security Eigenschaften: Strukturierung und Ausblick -- 1 Einleitung -- 2 Problemstellung -- 3 Stand der Technik -- 3.1 Safety -- 3.2 Security -- 3.3 Anwendungsfälle während einer Sicherheitsbetrachtung -- 3.4 Forschungsfragen -- 4 Konzeptvorstellung -- 5 Zusammenfassung -- Literatur -- The Implementation of Proactive Asset Administration Shells: Evaluation of Possibilities and Realization in an Order Driven Production -- 1 Introduction -- 2 Types of AASs and the Bidding Procedure -- 2.1 The Types of AASs -- 2.2 The VDI/VDE 2193-Interaction Protocol -- 3 Implementation of Proactive AASs -- 3.1 Requirements for Proactive AASs -- 3.2 Type 1: Proactive Part as AAS-Server Functionality -- 3.3 Type 2: AAS-Application Outside the AAS-Server -- 3.4 Future Possibility: JSON-Function Description -- 3.5 Selection of the Appropriate Type and Their Coexistence -- 4 Infrastructure in an Order Driven Production System -- 4.1 The Initialization of a Production Process -- 4.2 The Execution of a Production Process: The Proactive AASs -- 4.3 The Completion of a Production Process -- 5 The Bidding-App: Detailed Specification -- 5.1 Requirements -- 5.2 Required Submodels -- 5.3 Procedure -- 5.4 Evaluation of the App -- 6 Conclusion -- References -- Configuration Solution for SDN-Based Networks Interacting with Industrial Applications -- 1 Introduction -- 2 Industrial Use Case -- 3 Basics. 3.1 Software-Defined Networking -- 3.2 OPC UA -- 3.3 Combined Usage -- 4 Related Work -- 5 Architecture -- 6 Implementation -- 6.1 Topology and Network Configuration -- 6.2 Configuration Example -- 7 Discussion -- 8 Conclusion -- References -- Skalierbarkeit von PROFINET over TSN fr ressourcenbeschrnkte Gerte -- 1 Einleitung -- 2 Stand der Technik -- 2.1 Entwicklung der Anforderungen an die Industriellen Kommunikation -- 2.2 Entwicklung der Industriellen Kommunikation hin zu Ethernet TSN-basierten Systemen -- 2.3 Single Pair Ethernet -- 2.4 Möglichkeiten und Maßnahmen zur Optimierung von Softwarecode -- 3 Untersuchung des Ressourcenbedarf PROFINET-Profile und PROFINET-Stack -- 3.1 PROFINET-Stack mit den Profilen RT und IRT -- 3.2 PROFINET over TSN -- 4 Protokolle für ressourcenbeschränkte Feldgeräte -- 4.1 Vorschlag für ein PROFINET Nano-Profil (Sensorprofil) -- 4.2 OPC UA Nano-Profil -- 5 Zusammenfassung und Ausblick -- Literatur -- Vergleich von Ethernet TSN-Nutzungskonzepten -- 1 Einleitung -- 2 Stand der Technik -- 2.1 Entwicklung der Anforderungskriterien an die industrielle Kommunikation -- 2.2 Ethernet TSN -- 2.3 Anforderungs- und Bewertungskriterien -- 3 Ethernet TSN-Nutzungskonzepte -- 3.1 Preemption-basiertes Nutzungskonzept -- 3.2 TAS-basiertes Nutzungskonzept -- 4 Veranschaulichung der Anforderungen und Kriterien durch Messungen an einer Beispieltopologie und Vergleich -- 4.1 Beschreibung der Testumgebung -- 4.2 Messergebnisse Scheduled Traffic in einem Netzwerk mit gemischten Datenraten -- 4.3 Vergleich der Nutzungskonzepte anhand der Kriterien -- 5 Zusammenfassung und Ausblick -- Literatur -- Feasibility and Performance Case Study of a Private Mobile Cell in the Smart Factory Context -- 1 Introduction -- 2 5G Non Public Networks (NPN) in Industry -- 3 System Application in the Smart Factory -- 3.1 Setup and Configuration. 3.2 Initial Measurements -- 3.3 Measurements Under Industrial Conditions -- 4 Layer 2 Tunnel Integration -- 4.1 Setup -- 4.2 Measurements -- 5 Outlook on Future 5G Mechanisms -- 6 Conclusion and Future Work -- References -- Vergleichende Untersuchung von PROFINET-Redundanzkonzepten für hochverfügbare Automatisierungssysteme -- 1 Grundlagen der Verfügbarkeit -- 1.1 Kenngrößen der Verfügbarkeit -- 1.2 Verfügbarkeitsberechnung -- 1.3 Verfügbarkeitsklassen -- 2 Topologiekonzepte für hochverfügbare Netzwerke und Systeme -- 2.1 Topologie 1: Nicht-redundantes PROFINET-Netzwerk -- 2.2 Topologie 2: Kombination von Medien- und S2 Systemredundanz -- 2.3 Topologie 3: Kombination von Medien- und R1 Systemredundanz -- 2.4 Topologie 4: Linientopologie mit Systemredundanz R2 -- 2.5 Prognostizierte Ausfallzeiten der Topologien -- 3 Fazit -- Literatur -- Sichere Kommunikation fur kollaborative Systeme -- 1 Einleitung -- 2 Betrachtete Use Cases und Architektur -- 2.1 Use Cases -- 2.2 Architektur -- 3 Zugehörige Arbeiten -- 4 STRIDE Analyse -- 4.1 Analyse -- 4.2 Sicherheitsanforderungen -- 4.3 Klassifikation von Verbindungen -- 5 Sicherheitskonzept -- 5.1 Geräte-Authentifizierung -- 5.2 Bedienerauthentifizierung -- 5.3 Widerruf von Zertifikaten -- 6 Zusammenfassung -- Literatur -- Systematic Test Environment for Narrowband IoT Technologies -- 1 Introduction -- 2 State of the Art -- 3 Systematic Test Environment for NB-IoT -- 3.1 Challenges and Requirements for Systematic Test Environment -- 3.2 Structure of Systematic Test Environment for NB-IoT -- 4 NB-IoT Performance Evaluation Results -- 4.1 System Tests -- 4.2 Protocol Tests -- 5 Conclusion and Outlook -- References -- CANopen Flying Master Over TSN -- 1 Introduction -- 2 State of the Art -- 2.1 CANopen Flying Master -- 2.2 PROFINET IO Redundancy -- 2.3 IEEE 802.1CB -- 2.4 Industrial 5G. 3 Concept of Flying Master Over TSN. |
| Record Nr. | UNINA-9910588786903321 |
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Jasperneite Jürgen
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| Berlin, Heidelberg, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nonlinear system identification : from classical approaches to neural networks, fuzzy models, and Gaussian processes / / Oliver Nelles
| Nonlinear system identification : from classical approaches to neural networks, fuzzy models, and Gaussian processes / / Oliver Nelles |
| Autore | Nelles Oliver <1969-> |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XXVIII, 1225 p. 670 illus., 179 illus. in color.) |
| Disciplina | 003 |
| Soggetto topico |
System identification
Nonlinear systems Automatic control engineering |
| ISBN | 3-030-47439-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part One Optimization -- Introduction to Optimization -- Linear Optimization -- Nonlinear Local Optimization -- Nonlinear Global Optimization -- Unsupervised Learning Techniques -- Model Complexity Optimization -- Summary of Part 1 -- Part Two Static Models -- Introduction to Static Models -- Linear, Polynomial, and Look-Up Table Models -- Neural Networks -- Fuzzy and Neuro-Fuzzy Models -- Local Linear Neuro-Fuzzy Models: Fundamentals -- Local Linear Neuro-Fuzzy Models: Advanced Aspects -- Input Selection for Local Model Approaches -- Gaussian Process Models (GPMs) -- Summary of Part Two -- Part Three Dynamic Models -- Linear Dynamic System Identification -- Nonlinear Dynamic System Identification -- Classical Polynomial Approaches.-Dynamic Neural and Fuzzy Models -- Dynamic Local Linear Neuro-Fuzzy Models -- Neural Networks with Internal Dynamics -- Part Five Applications -- Applications of Static Models -- Applications of Dynamic Models -- Design of Experiments -- Input Selection Applications -- Applications of Advanced Methods -- LMN Toolbox -- Vectors and Matrices -- Statistics -- Reference -- Index. |
| Record Nr. | UNINA-9910427687103321 |
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Nelles Oliver <1969->
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| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nonlinear system identification : from classical approaches to neural networks, fuzzy models, and Gaussian processes / / Oliver Nelles
| Nonlinear system identification : from classical approaches to neural networks, fuzzy models, and Gaussian processes / / Oliver Nelles |
| Autore | Nelles Oliver <1969-> |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XXVIII, 1225 p. 670 illus., 179 illus. in color.) |
| Disciplina | 003 |
| Soggetto topico |
System identification
Nonlinear systems Automatic control engineering |
| ISBN | 3-030-47439-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part One Optimization -- Introduction to Optimization -- Linear Optimization -- Nonlinear Local Optimization -- Nonlinear Global Optimization -- Unsupervised Learning Techniques -- Model Complexity Optimization -- Summary of Part 1 -- Part Two Static Models -- Introduction to Static Models -- Linear, Polynomial, and Look-Up Table Models -- Neural Networks -- Fuzzy and Neuro-Fuzzy Models -- Local Linear Neuro-Fuzzy Models: Fundamentals -- Local Linear Neuro-Fuzzy Models: Advanced Aspects -- Input Selection for Local Model Approaches -- Gaussian Process Models (GPMs) -- Summary of Part Two -- Part Three Dynamic Models -- Linear Dynamic System Identification -- Nonlinear Dynamic System Identification -- Classical Polynomial Approaches.-Dynamic Neural and Fuzzy Models -- Dynamic Local Linear Neuro-Fuzzy Models -- Neural Networks with Internal Dynamics -- Part Five Applications -- Applications of Static Models -- Applications of Dynamic Models -- Design of Experiments -- Input Selection Applications -- Applications of Advanced Methods -- LMN Toolbox -- Vectors and Matrices -- Statistics -- Reference -- Index. |
| Record Nr. | UNISA-996418438803316 |
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Nelles Oliver <1969->
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| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Regularized System Identification : Learning Dynamic Models from Data
| Regularized System Identification : Learning Dynamic Models from Data |
| Autore | Pillonetto Gianluigi |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2022 |
| Descrizione fisica | 1 online resource (394 p.) |
| Altri autori (Persone) |
ChenTianshi
ChiusoAlessandro De NicolaoGiuseppe LjungLennart |
| Collana | Communications and Control Engineering |
| Soggetto topico |
Machine learning
Automatic control engineering Statistical physics Bayesian inference Probability & statistics Cybernetics & systems theory |
| Soggetto non controllato |
System Identification
Machine Learning Linear Dynamical Systems Nonlinear Dynamical Systems Kernel-based Regularization Bayesian Interpretation of Regularization Gaussian Processes Reproducing Kernel Hilbert Spaces Estimation Theory Support Vector Machines Regularization Networks |
| ISBN | 3-030-95860-4 |
| Classificazione | COM004000MAT029000MAT029010SCI055000SCI064000TEC004000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- Abbreviations and Notation -- Notation -- Abbreviations -- 1 Bias -- 1.1 The Stein Effect -- 1.1.1 The James-Stein Estimator -- 1.1.2 Extensions of the James-Stein Estimator -- 1.2 Ridge Regression -- 1.3 Further Topics and Advanced Reading -- 1.4 Appendix: Proof of Theorem 1.1 -- References -- 2 Classical System Identification -- 2.1 The State-of-the-Art Identification Setup -- 2.2 mathcalM: Model Structures -- 2.2.1 Linear Time-Invariant Models -- 2.2.2 Nonlinear Models -- 2.3 mathcalI: Identification Methods-Criteria -- 2.3.1 A Maximum Likelihood (ML) View -- 2.4 Asymptotic Properties of the Estimated Models -- 2.4.1 Bias and Variance -- 2.4.2 Properties of the PEM Estimate as Ntoinfty -- 2.4.3 Trade-Off Between Bias and Variance -- 2.5 X: Experiment Design -- 2.6 mathcalV: Model Validation -- 2.6.1 Falsifying Models: Residual Analysis -- 2.6.2 Comparing Different Models -- 2.6.3 Cross-Validation -- References -- 3 Regularization of Linear Regression Models -- 3.1 Linear Regression -- 3.2 The Least Squares Method -- 3.2.1 Fundamentals of the Least Squares Method -- 3.2.2 Mean Squared Error and Model Order Selection -- 3.3 Ill-Conditioning -- 3.3.1 Ill-Conditioned Least Squares Problems -- 3.3.2 Ill-Conditioning in System Identification -- 3.4 Regularized Least Squares with Quadratic Penalties -- 3.4.1 Making an Ill-Conditioned LS Problem Well Conditioned -- 3.4.2 Equivalent Degrees of Freedom -- 3.5 Regularization Tuning for Quadratic Penalties -- 3.5.1 Mean Squared Error and Expected Validation Error -- 3.5.2 Efficient Sample Reuse -- 3.5.3 Expected In-Sample Validation Error -- 3.6 Regularized Least Squares with Other Types of Regularizers -- 3.6.1 ell1-Norm Regularization -- 3.6.2 Nuclear Norm Regularization -- 3.7 Further Topics and Advanced Reading -- 3.8 Appendix.
3.8.1 Fundamentals of Linear Algebra -- 3.8.2 Proof of Lemma 3.1 -- 3.8.3 Derivation of Predicted Residual Error Sum of Squares (PRESS) -- 3.8.4 Proof of Theorem 3.7 -- 3.8.5 A Variant of the Expected In-Sample Validation Error and Its Unbiased Estimator -- References -- 4 Bayesian Interpretation of Regularization -- 4.1 Preliminaries -- 4.2 Incorporating Prior Knowledge via Bayesian Estimation -- 4.2.1 Multivariate Gaussian Variables -- 4.2.2 The Gaussian Case -- 4.2.3 The Linear Gaussian Model -- 4.2.4 Hierarchical Bayes: Hyperparameters -- 4.3 Bayesian Interpretation of the James-Stein Estimator -- 4.4 Full and Empirical Bayes Approaches -- 4.5 Improper Priors and the Bias Space -- 4.6 Maximum Entropy Priors -- 4.7 Model Approximation via Optimal Projection -- 4.8 Equivalent Degrees of Freedom -- 4.9 Bayesian Function Reconstruction -- 4.10 Markov Chain Monte Carlo Estimation -- 4.11 Model Selection Using Bayes Factors -- 4.12 Further Topics and Advanced Reading -- 4.13 Appendix -- 4.13.1 Proof of Theorem 4.1 -- 4.13.2 Proof of Theorem 4.2 -- 4.13.3 Proof of Lemma 4.1 -- 4.13.4 Proof of Theorem 4.3 -- 4.13.5 Proof of Theorem 4.6 -- 4.13.6 Proof of Proposition 4.3 -- 4.13.7 Proof of Theorem 4.8 -- References -- 5 Regularization for Linear System Identification -- 5.1 Preliminaries -- 5.2 MSE and Regularization -- 5.3 Optimal Regularization for FIR Models -- 5.4 Bayesian Formulation and BIBO Stability -- 5.5 Smoothness and Contractivity: Time- and Frequency-Domain Interpretations -- 5.5.1 Maximum Entropy Priors for Smoothness and Stability: From Splines to Dynamical Systems -- 5.6 Regularization and Basis Expansion -- 5.7 Hankel Nuclear Norm Regularization -- 5.8 Historical Overview -- 5.8.1 The Distributed Lag Estimator: Prior Means and Smoothing -- 5.8.2 Frequency-Domain Smoothing and Stability. 5.8.3 Exponential Stability and Stochastic Embedding -- 5.9 Further Topics and Advanced Reading -- 5.10 Appendix -- 5.10.1 Optimal Kernel -- 5.10.2 Proof of Lemma 5.1 -- 5.10.3 Proof of Theorem 5.5 -- 5.10.4 Proof of Corollary 5.1 -- 5.10.5 Proof of Lemma 5.2 -- 5.10.6 Proof of Theorem 5.6 -- 5.10.7 Proof of Lemma 5.5 -- 5.10.8 Forward Representations of Stable-Splines Kernels -- References -- 6 Regularization in Reproducing Kernel Hilbert Spaces -- 6.1 Preliminaries -- 6.2 Reproducing Kernel Hilbert Spaces -- 6.2.1 Reproducing Kernel Hilbert Spaces Induced by Operations on Kernels -- 6.3 Spectral Representations of Reproducing Kernel Hilbert Spaces -- 6.3.1 More General Spectral Representation -- 6.4 Kernel-Based Regularized Estimation -- 6.4.1 Regularization in Reproducing Kernel Hilbert Spaces and the Representer Theorem -- 6.4.2 Representer Theorem Using Linear and Bounded Functionals -- 6.5 Regularization Networks and Support Vector Machines -- 6.5.1 Regularization Networks -- 6.5.2 Robust Regression via Huber Loss -- 6.5.3 Support Vector Regression -- 6.5.4 Support Vector Classification -- 6.6 Kernels Examples -- 6.6.1 Linear Kernels, Regularized Linear Regression and System Identification -- 6.6.2 Kernels Given by a Finite Number of Basis Functions -- 6.6.3 Feature Map and Feature Space -- 6.6.4 Polynomial Kernels -- 6.6.5 Translation Invariant and Radial Basis Kernels -- 6.6.6 Spline Kernels -- 6.6.7 The Bias Space and the Spline Estimator -- 6.7 Asymptotic Properties -- 6.7.1 The Regression Function/Optimal Predictor -- 6.7.2 Regularization Networks: Statistical Consistency -- 6.7.3 Connection with Statistical Learning Theory -- 6.8 Further Topics and Advanced Reading -- 6.9 Appendix -- 6.9.1 Fundamentals of Functional Analysis -- 6.9.2 Proof of Theorem 6.1 -- 6.9.3 Proof of Theorem 6.10 -- 6.9.4 Proof of Theorem 6.13. 6.9.5 Proofs of Theorems 6.15 and 6.16 -- 6.9.6 Proof of Theorem 6.21 -- References -- 7 Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification -- 7.1 Regularized Linear System Identification in Reproducing Kernel Hilbert Spaces -- 7.1.1 Discrete-Time Case -- 7.1.2 Continuous-Time Case -- 7.1.3 More General Use of the Representer Theorem for Linear System Identification -- 7.1.4 Connection with Bayesian Estimation of Gaussian Processes -- 7.1.5 A Numerical Example -- 7.2 Kernel Tuning -- 7.2.1 Marginal Likelihood Maximization -- 7.2.2 Stein's Unbiased Risk Estimator -- 7.2.3 Generalized Cross-Validation -- 7.3 Theory of Stable Reproducing Kernel Hilbert Spaces -- 7.3.1 Kernel Stability: Necessary and Sufficient Conditions -- 7.3.2 Inclusions of Reproducing Kernel Hilbert Spaces in More General Lebesque Spaces -- 7.4 Further Insights into Stable Reproducing Kernel Hilbert Spaces -- 7.4.1 Inclusions Between Notable Kernel Classes -- 7.4.2 Spectral Decomposition of Stable Kernels -- 7.4.3 Mercer Representations of Stable Reproducing Kernel Hilbert Spaces and of Regularized Estimators -- 7.4.4 Necessary and Sufficient Stability Condition Using Kernel Eigenvectors and Eigenvalues -- 7.5 Minimax Properties of the Stable Spline Estimator -- 7.5.1 Data Generator and Minimax Optimality -- 7.5.2 Stable Spline Estimator -- 7.5.3 Bounds on the Estimation Error and Minimax Properties -- 7.6 Further Topics and Advanced Reading -- 7.7 Appendix -- 7.7.1 Derivation of the First-Order Stable Spline Norm -- 7.7.2 Proof of Proposition 7.1 -- 7.7.3 Proof of Theorem 7.5 -- 7.7.4 Proof of Theorem 7.7 -- 7.7.5 Proof of Theorem 7.9 -- References -- 8 Regularization for Nonlinear System Identification -- 8.1 Nonlinear System Identification -- 8.2 Kernel-Based Nonlinear System Identification. 8.2.1 Connection with Bayesian Estimation of Gaussian Random Fields -- 8.2.2 Kernel Tuning -- 8.3 Kernels for Nonlinear System Identification -- 8.3.1 A Numerical Example -- 8.3.2 Limitations of the Gaussian and Polynomial Kernel -- 8.3.3 Nonlinear Stable Spline Kernel -- 8.3.4 Numerical Example Revisited: Use of the Nonlinear Stable Spline Kernel -- 8.4 Explicit Regularization of Volterra Models -- 8.5 Other Examples of Regularization in Nonlinear System Identification -- 8.5.1 Neural Networks and Deep Learning Models -- 8.5.2 Static Nonlinearities and Gaussian Process (GP) -- 8.5.3 Block-Oriented Models -- 8.5.4 Hybrid Models -- 8.5.5 Sparsity and Variable Selection -- References -- 9 Numerical Experiments and Real World Cases -- 9.1 Identification of Discrete-Time Output Error Models -- 9.1.1 Monte Carlo Studies with a Fixed Output Error Model -- 9.1.2 Monte Carlo Studies with Different Output Error Models -- 9.1.3 Real Data: A Robot Arm -- 9.1.4 Real Data: A Hairdryer -- 9.2 Identification of ARMAX Models -- 9.2.1 Monte Carlo Experiment -- 9.2.2 Real Data: Temperature Prediction -- 9.3 Multi-task Learning and Population Approaches -- 9.3.1 Kernel-Based Multi-task Learning -- 9.3.2 Numerical Example: Real Pharmacokinetic Data -- References -- Appendix Index -- Index. |
| Record Nr. | UNINA-9910568256103321 |
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Pillonetto Gianluigi
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| Cham, : Springer International Publishing AG, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Special Topics in Information Technology
| Special Topics in Information Technology |
| Autore | Geraci Angelo |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (150 pages) |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Communications engineering / telecommunications
Automatic control engineering Algorithms & data structures |
| Soggetto non controllato |
Communications Engineering, Networks
Control and Systems Theory Data Structures and Information Theory Information Technology PhD Springer Award Politecnico DEIB Polimi PhD School artificial intelligence computer system architectures Telecommunications Open access Communications engineering / telecommunications Automatic control engineering Algorithms & data structures Information theory |
| ISBN | 3-030-62476-5 |
| Classificazione | COM031000TEC004000TEC041000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part ITelecommunications -- 1 Machine-Learning Defined Networking: Towards Intelligent Networks -- 1.1 Introduction -- 1.2 Network Traffic Prediction -- 1.3 Network Traffic Pattern Identification -- 1.4 Reinforcement Learning for Adaptive Network Resource Allocation -- 1.5 Implementation of Machine Learning in Real SDN/NFV Testbeds -- 1.6 Concluding Remarks -- References -- 2 Traffic Management in Networks with Programmable Data Planes -- 2.1 Software-Defined Networks (SDN) -- 2.2 Control Plane Programmability -- 2.2.1 Traffic Engineering Framework -- 2.2.2 ONOS Intent Monitor and Reroute Service -- 2.3 Data Plane Programmability -- 2.3.1 Network Failures -- 2.3.2 Network Congestion -- 2.4 Conclusions -- References -- Part IIElectronics -- 3 Frequency Synthesizers Based on Fast-Locking Bang-Bang PLL for Cellular Applications -- 3.1 Introduction -- 3.2 Digital PLL: Output Phase Noise and Locking Transient -- 3.3 Multi-loop Architecture for Fast Locking Transient -- 3.4 Measurement results -- 3.5 Conclusions -- References -- 4 Inductorless Frequency Synthesizers for Low-Cost Wireless -- 4.1 Introduction -- 4.2 Fractional-N MDLLs -- 4.3 Jitter-Power Tradeoff Analysis -- 4.4 DTC Range-Reduction Technique -- 4.5 Implemented Architecture -- 4.6 Measurement Results -- 4.7 Conclusion -- References -- 5 Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications -- 5.1 Introduction -- 5.2 Spin-Transfer Torque Magnetic Memory (STT-MRAM) -- 5.3 Understanding Dielectric Breakdown-Limited Cycling Endurance -- 5.4 Modeling Stochastic Switching in STT-MRAM -- 5.5 Stochastic STT Switching for Security and Computing -- 5.6 Conclusions -- References -- 6 One Step in-Memory Solution of Inverse Algebraic Problems -- 6.1 Introduction -- 6.2 In Memory Computing.
6.3 In-Memory Matrix-Vector-Multiplication Accelerator -- 6.4 One Step in-Memory Solution of Inverse Algebraic Problems -- 6.4.1 In-Memory Solution of Linear Systems in One-Step -- 6.4.2 In-Memory Eigenvectors Calculation in One-Step -- 6.4.3 In-Memory Regression and Classification in One-Step -- 6.5 Conclusions -- References -- 7 Development of a 3'' LaBr3 SiPM-Based Detection Module for High Resolution Gamma Ray Spectroscopy and Imaging -- 7.1 Introduction -- 7.2 Development -- References -- Part IIIComputer Science and Engineering -- 8 Velocity on the Web -- 8.1 Introduction -- 8.2 Background -- 8.3 Problem Statement -- 8.4 Major Results -- 8.5 Conclusion -- References -- 9 Preplay Communication in Multi-Player Sequential Games: An Overview of Recent Results -- 9.1 Introduction -- 9.1.1 Motivating Example -- 9.1.2 Sequential Games with Imperfect Information -- 9.1.3 Preplay Communication -- 9.2 Adversarial Team Games -- 9.3 Correlated Equilibria in Sequential Games -- 9.4 Bayesian Persuasion with Sequential Games -- 9.5 Discussion and Future Research -- References -- Part IVSystems and Control -- 10 Leadership Games: Multiple Followers, Multiple Leaders, and Perfection -- 10.1 Introduction -- 10.2 The Stackelberg Paradigm -- 10.3 Stackelberg Games with Multiple Followers -- 10.3.1 Norma-Form Stackelberg Games -- 10.3.2 Stackelberg Polymatrix Games -- 10.3.3 Stackelberg Congestion Games -- 10.4 Stackelberg Games with Multiple Leaders -- 10.5 Trembling-Hand Perfection in Stackelberg Games -- References -- 11 Advancing Joint Design and Operation of Water Resources Systems Under Uncertainty -- 11.1 Introduction -- 11.1.1 Research Challenges -- 11.2 Reinforcement Learning for Designing Water Reservoirs -- 11.2.1 pFQI Algorithm -- 11.2.2 Comparison with Traditional Least Cost Dam Design -- 11.3 A Novel Robust Assessment Framework. 11.3.1 Methodological Approach -- 11.3.2 Assessing Robustness of Alternatives for Changing Demands and Hydrology -- 11.4 Conclusions -- References -- 12 Optimization-Based Control of Microgrids for Ancillary Services Provision and Islanded Operation -- 12.1 Introduction -- 12.2 Microgrids Aggregators Providing Ancillary Services -- 12.2.1 Offline Economic Dispatch and Power Reserve Procurement -- 12.2.2 Online External Provision of Ancillary Services -- 12.2.3 Real-Time Self-balancing of Internal Power Uncertainties -- 12.3 Hierarchical Model Predictive Control Architectures for Islanded Microgrids -- 12.4 Conclusions -- References -- 13 Allowing a Real Collaboration Between Humans and Robots -- 13.1 Introduction -- 13.2 Recognizing the Human Actions -- 13.3 Predicting the Human Actions -- 13.4 Assistive Scheduling -- 13.5 Results -- 13.6 Conclusions -- References. |
| Record Nr. | UNINA-9910473458303321 |
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Geraci Angelo
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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