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The Future Internet [[electronic resource] ] : Future Internet Assembly 2011: Achievements and Technological Promises / / edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, Srdjan Krco, Henning Müller, Man-Sze Li, Hans Schaffers, Volkmar Lotz, Federico Alvarez, Burkhard Stiller, Stamatis Karnouskos, Susanna Avessta, Michael Nilsson
| The Future Internet [[electronic resource] ] : Future Internet Assembly 2011: Achievements and Technological Promises / / edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, Srdjan Krco, Henning Müller, Man-Sze Li, Hans Schaffers, Volkmar Lotz, Federico Alvarez, Burkhard Stiller, Stamatis Karnouskos, Susanna Avessta, Michael Nilsson |
| Autore | Domingue John |
| Edizione | [1st ed. 2011.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2011 |
| Descrizione fisica | 1 online resource (XVI, 465 p.) |
| Disciplina | 004.6 |
| Collana | Computer Communication Networks and Telecommunications |
| Soggetto topico |
Computer communication systems
Application software Information storage and retrieval Multimedia information systems Electrical engineering Computers and civilization Computer Communication Networks Information Systems Applications (incl. Internet) Information Storage and Retrieval Multimedia Information Systems Communications Engineering, Networks Computers and Society |
| Soggetto non controllato |
Computer Communication Networks
Information Systems Applications (incl. Internet) Information Storage and Retrieval Multimedia Information Systems Communications Engineering, Networks Computers and Society |
| ISBN | 3-642-20898-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Future internet foundations : architectural issues -- pt. 2. Future internet foundations : socio-economic issues -- pt. 3. Future internet foundations : security and trust -- Future internet foundations : experiments and experimental design -- pt. 5. Future internet areas : networks -- pt. 6. Future internet areas : services -- pt. 7. Future internet areas : content -- pt. 8. Future internet applications. |
| Record Nr. | UNISA-996206466603316 |
Domingue John
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| Cham, : Springer Nature, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Model-Driven Development and Operation of Multi-Cloud Applications [[electronic resource] ] : The MODAClouds Approach / / edited by Elisabetta Di Nitto, Peter Matthews, Dana Petcu, Arnor Solberg
| Model-Driven Development and Operation of Multi-Cloud Applications [[electronic resource] ] : The MODAClouds Approach / / edited by Elisabetta Di Nitto, Peter Matthews, Dana Petcu, Arnor Solberg |
| Autore | Di Nitto Elisabetta |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2017 |
| Descrizione fisica | 1 online resource (VIII, 149 p. 49 illus.) |
| Disciplina | 621.382 |
| Collana | PoliMI SpringerBriefs |
| Soggetto topico |
Electrical engineering
Computer communication systems Software engineering Computational intelligence Communications Engineering, Networks Computer Communication Networks Software Engineering Computational Intelligence |
| Soggetto non controllato |
Communications Engineering, Networks
Computer Communication Networks Software Engineering Computational Intelligence |
| ISBN | 3-319-46031-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Cloud Service Offer Selection -- The MODAClouds Model-Driven Development -- QoS Assessment and SLA Management -- Monitoring in a Multi-Cloud Environment -- Load Balancing for Multi-Cloud -- Fault-tolerant Off-line Data Migration: The Hegira4Clouds Approach. -Deployment of Cloud Supporting Services -- Models@Runtime for Continuous Design and Deployment -- Cloud Patterns -- Modelio Project Management Server Constellation -- BPM in the Cloud: The BOC Case -- Healthcare Application -- Operation Control Interfaces -- Conclusion and Future Research. |
| Record Nr. | UNINA-9910166651403321 |
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Di Nitto Elisabetta
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| Cham, : Springer Nature, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Probability in Electrical Engineering and Computer Science [[electronic resource] ] : An Application-Driven Course
| Probability in Electrical Engineering and Computer Science [[electronic resource] ] : An Application-Driven Course |
| Autore | Walrand Jean |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (390 p.) |
| Soggetto topico |
Maths for computer scientists
Communications engineering / telecommunications Maths for engineers Probability & statistics |
| Soggetto non controllato |
Probability and Statistics in Computer Science
Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics |
| ISBN | 3-030-49995-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464521903316 |
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Walrand Jean
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Probability in Electrical Engineering and Computer Science : An Application-Driven Course
| Probability in Electrical Engineering and Computer Science : An Application-Driven Course |
| Autore | Walrand Jean |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (390 p.) |
| Soggetto topico |
Maths for computer scientists
Communications engineering / telecommunications Maths for engineers Probability & statistics |
| Soggetto non controllato |
Probability and Statistics in Computer Science
Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics |
| ISBN | 3-030-49995-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Introduction -- About This Second Edition -- Contents -- 1 PageRank: A -- 1.1 Model -- 1.2 Markov Chain -- 1.2.1 General Definition -- 1.2.2 Distribution After n Steps and Invariant Distribution -- 1.3 Analysis -- 1.3.1 Irreducibility and Aperiodicity -- 1.3.2 Big Theorem -- 1.3.3 Long-Term Fraction of Time -- 1.4 Illustrations -- 1.5 Hitting Time -- 1.5.1 Mean Hitting Time -- 1.5.2 Probability of Hitting a State Before Another -- 1.5.3 FSE for Markov Chain -- 1.6 Summary -- 1.6.1 Key Equations and Formulas -- 1.7 References -- 1.8 Problems -- 2 PageRank: B -- 2.1 Sample Space -- 2.2 Laws of Large Numbers for Coin Flips -- 2.2.1 Convergence in Probability -- 2.2.2 Almost Sure Convergence -- 2.3 Laws of Large Numbers for i.i.d. RVs -- 2.3.1 Weak Law of Large Numbers -- 2.3.2 Strong Law of Large Numbers -- 2.4 Law of Large Numbers for Markov Chains -- 2.5 Proof of Big Theorem -- 2.5.1 Proof of Theorem 1.1 (a) -- 2.5.2 Proof of Theorem 1.1 (b) -- 2.5.3 Periodicity -- 2.6 Summary -- 2.6.1 Key Equations and Formulas -- 2.7 References -- 2.8 Problems -- 3 Multiplexing: A -- 3.1 Sharing Links -- 3.2 Gaussian Random Variable and CLT -- 3.2.1 Binomial and Gaussian -- 3.2.2 Multiplexing and Gaussian -- 3.2.3 Confidence Intervals -- 3.3 Buffers -- 3.3.1 Markov Chain Model of Buffer -- 3.3.2 Invariant Distribution -- 3.3.3 Average Delay -- 3.3.4 A Note About Arrivals -- 3.3.5 Little's Law -- 3.4 Multiple Access -- 3.5 Summary -- 3.5.1 Key Equations and Formulas -- 3.6 References -- 3.7 Problems -- 4 Multiplexing: B -- 4.1 Characteristic Functions -- 4.2 Proof of CLT (Sketch) -- 4.3 Moments of N(0, 1) -- 4.4 Sum of Squares of 2 i.i.d. N(0, 1) -- 4.5 Two Applications of Characteristic Functions -- 4.5.1 Poisson as a Limit of Binomial -- 4.5.2 Exponential as Limit of Geometric -- 4.6 Error Function.
4.7 Adaptive Multiple Access -- 4.8 Summary -- 4.8.1 Key Equations and Formulas -- 4.9 References -- 4.10 Problems -- 5 Networks: A -- 5.1 Spreading Rumors -- 5.2 Cascades -- 5.3 Seeding the Market -- 5.4 Manufacturing of Consent -- 5.5 Polarization -- 5.6 M/M/1 Queue -- 5.7 Network of Queues -- 5.8 Optimizing Capacity -- 5.9 Internet and Network of Queues -- 5.10 Product-Form Networks -- 5.10.1 Example -- 5.11 References -- 5.12 Problems -- 6 Networks-B -- 6.1 Social Networks -- 6.2 Continuous-Time Markov Chains -- 6.2.1 Two-State Markov Chain -- 6.2.2 Three-State Markov Chain -- 6.2.3 General Case -- 6.2.4 Uniformization -- 6.2.5 Time Reversal -- 6.3 Product-Form Networks -- 6.4 Proof of Theorem 5.7 -- 6.5 References -- 7 Digital Link-A -- 7.1 Digital Link -- 7.2 Detection and Bayes' Rule -- 7.2.1 Bayes' Rule -- 7.2.2 Circumstances vs. Causes -- 7.2.3 MAP and MLE -- Example: Ice Cream and Sunburn -- 7.2.4 Binary Symmetric Channel -- 7.3 Huffman Codes -- 7.4 Gaussian Channel -- Simulation -- 7.4.1 BPSK -- 7.5 Multidimensional Gaussian Channel -- 7.5.1 MLE in Multidimensional Case -- 7.6 Hypothesis Testing -- 7.6.1 Formulation -- 7.6.2 Solution -- 7.6.3 Examples -- Gaussian Channel -- Mean of Exponential RVs -- Bias of a Coin -- Discrete Observations -- 7.7 Summary -- 7.7.1 Key Equations and Formulas -- 7.8 References -- 7.9 Problems -- 8 Digital Link-B -- 8.1 Proof of Optimality of the Huffman Code -- 8.2 Proof of Neyman-Pearson Theorem 7.4 -- 8.3 Jointly Gaussian Random Variables -- 8.3.1 Density of Jointly Gaussian Random Variables -- 8.4 Elementary Statistics -- 8.4.1 Zero-Mean? -- 8.4.2 Unknown Variance -- 8.4.3 Difference of Means -- 8.4.4 Mean in Hyperplane? -- 8.4.5 ANOVA -- 8.5 LDPC Codes -- 8.6 Summary -- 8.6.1 Key Equations and Formulas -- 8.7 References -- 8.8 Problems -- 9 Tracking-A -- 9.1 Examples -- 9.2 Estimation Problem. 9.3 Linear Least Squares Estimates -- 9.3.1 Projection -- 9.4 Linear Regression -- 9.5 A Note on Overfitting -- 9.6 MMSE -- 9.6.1 MMSE for Jointly Gaussian -- 9.7 Vector Case -- 9.8 Kalman Filter -- 9.8.1 The Filter -- 9.8.2 Examples -- Random Walk -- Random Walk with Unknown Drift -- Random Walk with Changing Drift -- Falling Object -- 9.9 Summary -- 9.9.1 Key Equations and Formulas -- 9.10 References -- 9.11 Problems -- 10 Tracking: B -- 10.1 Updating LLSE -- 10.2 Derivation of Kalman Filter -- 10.3 Properties of Kalman Filter -- 10.3.1 Observability -- 10.3.2 Reachability -- 10.4 Extended Kalman Filter -- 10.4.1 Examples -- 10.5 Summary -- 10.5.1 Key Equations and Formulas -- 10.6 References -- 11 Speech Recognition: A -- 11.1 Learning: Concepts and Examples -- 11.2 Hidden Markov Chain -- 11.3 Expectation Maximization and Clustering -- 11.3.1 A Simple Clustering Problem -- 11.3.2 A Second Look -- 11.4 Learning: Hidden Markov Chain -- 11.4.1 HEM -- 11.4.2 Training the Viterbi Algorithm -- 11.5 Summary -- 11.5.1 Key Equations and Formulas -- 11.6 References -- 11.7 Problems -- 12 Speech Recognition: B -- 12.1 Online Linear Regression -- 12.2 Theory of Stochastic Gradient Projection -- 12.2.1 Gradient Projection -- 12.2.2 Stochastic Gradient Projection -- 12.2.3 Martingale Convergence -- 12.3 Big Data -- 12.3.1 Relevant Data -- 12.3.2 Compressed Sensing -- 12.3.3 Recommendation Systems -- 12.4 Deep Neural Networks -- 12.4.1 Calculating Derivatives -- 12.5 Summary -- 12.5.1 Key Equations and Formulas -- 12.6 References -- 12.7 Problems -- 13 Route Planning: A -- 13.1 Model -- 13.2 Formulation 1: Pre-planning -- 13.3 Formulation 2: Adapting -- 13.4 Markov Decision Problem -- 13.4.1 Examples -- 13.5 Infinite Horizon -- 13.6 Summary -- 13.6.1 Key Equations and Formulas -- 13.7 References -- 13.8 Problems -- 14 Route Planning: B -- 14.1 LQG Control. 14.1.1 Letting N →∞ -- 14.2 LQG with Noisy Observations -- 14.2.1 Letting N →∞ -- 14.3 Partially Observed MDP -- 14.3.1 Example: Searching for Your Keys -- 14.4 Summary -- 14.4.1 Key Equations and Formulas -- 14.5 References -- 14.6 Problems -- 15 Perspective and Complements -- 15.1 Inference -- 15.2 Sufficient Statistic -- 15.2.1 Interpretation -- 15.3 Infinite Markov Chains -- 15.3.1 Lyapunov-Foster Criterion -- 15.4 Poisson Process -- 15.4.1 Definition -- 15.4.2 Independent Increments -- 15.4.3 Number of Jumps -- 15.5 Boosting -- 15.6 Multi-Armed Bandits -- 15.7 Capacity of BSC -- 15.8 Bounds on Probabilities -- 15.8.1 Applying the Bounds to Multiplexing -- 15.9 Martingales -- 15.9.1 Definitions -- 15.9.2 Examples -- 15.9.3 Law of Large Numbers -- 15.9.4 Wald's Equality -- 15.10 Summary -- 15.10.1 Key Equations and Formulas -- 15.11 References -- 15.12 Problems -- Correction to: Probability in Electrical Engineering and Computer Science -- Correction to: Probability in Electrical Engineering and Computer Science (Funding Information) -- A Elementary Probability -- A.1 Symmetry -- A.2 Conditioning -- A.3 Common Confusion -- A.4 Independence -- A.5 Expectation -- A.6 Variance -- A.7 Inequalities -- A.8 Law of Large Numbers -- A.9 Covariance and Regression -- A.10 Why Do We Need a More Sophisticated Formalism? -- A.11 References -- A.12 Solved Problems -- B Basic Probability -- B.1 General Framework -- B.1.1 Probability Space -- B.1.2 Borel-Cantelli Theorem -- B.1.3 Independence -- B.1.4 Converse of Borel-Cantelli Theorem -- B.1.5 Conditional Probability -- B.1.6 Random Variable -- B.2 Discrete Random Variable -- B.2.1 Definition -- B.2.2 Expectation -- B.2.3 Function of a RV -- B.2.4 Nonnegative RV -- B.2.5 Linearity of Expectation -- B.2.6 Monotonicity of Expectation -- B.2.7 Variance, Standard Deviation. B.2.8 Important Discrete Random Variables -- B.3 Multiple Discrete Random Variables -- B.3.1 Joint Distribution -- B.3.2 Independence -- B.3.3 Expectation of Function of Multiple RVs -- B.3.4 Covariance -- B.3.5 Conditional Expectation -- B.3.6 Conditional Expectation of a Function -- B.4 General Random Variables -- B.4.1 Definitions -- B.4.2 Examples -- B.4.3 Expectation -- B.4.4 Continuity of Expectation -- B.5 Multiple Random Variables -- B.5.1 Random Vector -- B.5.2 Minimum and Maximum of Independent RVs -- B.5.3 Sum of Independent Random Variables -- B.6 Random Vectors -- B.6.1 Orthogonality and Projection -- B.7 Density of a Function of Random Variables -- B.7.1 Linear Transformations -- B.7.2 Nonlinear Transformations -- B.8 References -- B.9 Problems -- References -- Index. |
| Record Nr. | UNINA-9910488709003321 |
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Walrand Jean
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| Cham, : Springer International Publishing AG, 2021 | ||
| 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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