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2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society
| 2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (114 pages) |
| Disciplina | 005.117 |
| Soggetto topico |
Distributed databases
Object-oriented methods (Computer science) - Distributed processing Electronic data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-5386-4141-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996280537403316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society
| 2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (114 pages) |
| Disciplina | 005.117 |
| Soggetto topico |
Distributed databases
Object-oriented methods (Computer science) - Distributed processing Electronic data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-5386-4141-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910294556403321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers
| 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Los Alamitos, California : , : IEEE, , 2019 |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 005.758 |
| Soggetto topico |
Distributed databases
Electronic data processing - Distributed processing Object-oriented methods (Computer science) |
| ISBN | 1-7281-4598-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop |
| Record Nr. | UNINA-9910389510203321 |
| Los Alamitos, California : , : IEEE, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers
| 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Los Alamitos, California : , : IEEE, , 2019 |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 005.758 |
| Soggetto topico |
Distributed databases
Electronic data processing - Distributed processing Object-oriented methods (Computer science) |
| ISBN | 1-7281-4598-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop |
| Record Nr. | UNISA-996574766603316 |
| Los Alamitos, California : , : IEEE, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
23rd International Conference on Distributed Computing and Networking / / Association for Computing Machinery
| 23rd International Conference on Distributed Computing and Networking / / Association for Computing Machinery |
| Pubbl/distr/stampa | New York, NY : , : Association for Computing Machinery, , 2022 |
| Descrizione fisica | 1 online resource (298 pages) |
| Disciplina | 005.7 |
| Soggetto topico | Distributed databases |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910548282103321 |
| New York, NY : , : Association for Computing Machinery, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / / Hien Luu
| Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / / Hien Luu |
| Autore | Hien Luu |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | New York, New York : , : Apress, , [2021] |
| Descrizione fisica | 1 online resource (445 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Distributed databases Open source software Machine learning |
| ISBN | 1-4842-7383-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Apache Spark -- Overview -- History -- Spark Core Concepts and Architecture -- Spark Cluster and Resource Management System -- Spark Applications -- Spark Drivers and Executors -- Spark Unified Stack -- Spark Core -- Spark SQL -- Spark Structured Streaming -- Spark MLlib -- Spark GraphX -- SparkR -- Apache Spark 3.0 -- Adaptive Query Execution Framework -- Dynamic Partition Pruning (DPP) -- Accelerator-aware Scheduler -- Apache Spark Applications -- Spark Example Applications -- Apache Spark Ecosystem -- Delta Lake -- Koalas -- MLflow -- Summary -- Chapter 2: Working with Apache Spark -- Downloading and Installation -- Downloading Spark -- Installing Spark -- Spark Scala Shell -- Spark Python Shell -- Having Fun with the Spark Scala Shell -- Useful Spark Scala Shell Command and Tips -- Basic Interactions with Scala and Spark -- Basic Interactions with Scala -- Spark UI and Basic Interactions with Spark -- Spark UI -- Basic Interactions with Spark -- Introduction to Collaborative Notebooks -- Create a Cluster -- Create a Folder -- Create a Notebook -- Setting up Spark Source Code -- Summary -- Chapter 3: Spark SQL: Foundation -- Understanding RDD -- Introduction to the DataFrame API -- Creating a DataFrame -- Creating a DataFrame from RDD -- Creating a DataFrame from a Range of Numbers -- Creating a DataFrame from Data Sources -- Creating a DataFrame by Reading Text Files -- Creating a DataFrame by Reading CSV Files -- Creating a DataFrame by Reading JSON Files -- Creating a DataFrame by Reading Parquet Files -- Creating a DataFrame by Reading ORC Files -- Creating a DataFrame from JDBC -- Working with Structured Operations -- Working with Columns -- Working with Structured Transformations.
select(columns) -- selectExpr(expressions) -- filler(condition), where(condition) -- distinct, dropDuplicates -- sort(columns), orderBy(columns) -- limit(n) -- union(otherDataFrame) -- withColumn(colName, column) -- withColumnRenamed(existingColName, newColName) -- drop(columnName1, columnName2) -- sample(fraction), sample(fraction, seed), sample(fraction, seed, withReplacement) -- randomSplit(weights) -- Working with Missing or Bad Data -- Working with Structured Actions -- describe(columnNames) -- Introduction to Datasets -- Creating Datasets -- Working with Datasets -- Using SQL in Spark SQL -- Running SQL in Spark -- Writing Data Out to Storage Systems -- The Trio: DataFrame, Dataset, and SQL -- DataFrame Persistence -- Summary -- Chapter 4: Spark SQL: Advanced -- Aggregations -- Aggregation Functions -- Common Aggregation Functions -- count(col) -- countDistinct(col) -- min(col), max(col) -- sum(col) -- sumDistinct(col) -- avg(col) -- skewness(col), kurtosis(col) -- variance(col), stddev(col) -- Aggregation with Grouping -- Multiple Aggregations per Group -- Collection Group Values -- Aggregation with Pivoting -- Joins -- Join Expression and Join Types -- Working with Joins -- Inner Joins -- Left Outer Joins -- Right Outer Joins -- Outer Joins (a.k.a. Full Outer Joins) -- Left Anti-Joins -- Left Semi-Joins -- Cross (a.k.a. Cartesian) -- Dealing with Duplicate Column Names -- Use Original DataFrame -- Renaming Column Before Joining -- Using Joined Column Name -- Overview of Join Implementation -- Shuffle Hash Join -- Broadcast Hash Join -- Functions -- Working with Built-in Functions -- Working with Date Time Functions -- Working with String Functions -- Working with Math Functions -- Working with Collection Functions -- Working with Miscellaneous Functions -- Working with User-Defined Functions (UDFs) -- Advanced Analytics Functions. Aggregation with Rollups and Cubes -- Rollups -- Cubes -- Aggregation with Time Windows -- Window Functions -- Exploring Catalyst Optimizer -- Logical Plan -- Physical Plan -- Catalyst in Action -- Project Tungsten -- Summary -- Chapter 5: Optimizing Spark Applications -- Common Performance Issues -- Spark Configurations -- Different Ways of Setting Properties -- Different Kinds of Properties -- Viewing Spark Properties -- Spark Memory Management -- Spark Driver -- Spark Executor -- Leverage In-Memory Computation -- When to Persist and Cache Data -- Persistence and Caching APIs -- Persistence and Caching Example -- Understanding Spark Joins -- Broadcast Hash Join -- Shuffle Sort Merge Join -- Adaptive Query Execution -- Dynamically Coalescing Shuffle Partitions -- Dynamically Switching Join Strategies -- Dynamically Optimizing Skew Joins -- Summary -- Chapter 6: Spark Streaming -- Stream Processing -- Concepts -- Data Delivery Semantics -- Notion of Time -- Windowing -- Stream Processing Engine Landscape -- Spark Streaming Overview -- Spark DStream -- Spark Structured Streaming -- Overview -- Core Concepts -- Data Sources -- Output Modes -- Trigger Types -- Data Sinks -- Watermarking -- Structured Streaming Applications -- Streaming DataFrame Operations -- Selection, Project, Aggregation Operations -- Join Operations -- Working with Data Sources -- Working with a Socket Data Source -- Working with a Rate Data Source -- Working with a File Data Source -- Working with a Kafka Data Source -- Working with a Custom Data Source -- Working with Data Sinks -- Working with a File Data Sink -- Working with a Kafka Data Sink -- Working with a foreach Data Sink -- Working with a Console Data Sink -- Working with a Memory Data Sink -- Output Modes -- Triggers -- Summary -- Chapter 7: Advanced Spark Streaming -- Event Time. Fixed Window Aggregation over an Event Time -- Sliding Window Aggregation over Event Time -- Aggregation State -- Watermarking: Limit State and Handle Late Data -- Arbitrary Stateful Processing -- Arbitrary Stateful Processing with Structured Streaming -- Handling State Timeouts -- Arbitrary State Processing in Action -- Extracting Patterns with mapGroupsWithState -- User Sessionization with flatMapGroupsWithState -- Handling Duplicate Data -- Fault Tolerance -- Streaming Application Code Change -- Spark Runtime Change -- Streaming Query Metrics and Monitoring -- Streaming Query Metrics -- Monitoring Streaming Queries via Callback -- Monitoring Streaming Queries via Visualization UI -- Streaming Query Summary Information -- Streaming Query Detailed Statistics Information -- Troubleshooting Streaming Query -- Summary -- Chapter 8: Machine Learning with Spark -- Machine Learning Overview -- Machine Learning Terminologies -- Machine Learning Types -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Machine Learning Development Process -- Spark Machine Learning Library -- Machine Learning Pipelines -- Transformers -- Estimators -- Pipeline -- Pipeline Persistence: Saving and Loading -- Model Tuning -- Speeding Up Model Tuning -- Model Evaluators -- Machine Learning Tasks in Action -- Classification -- Model Hyperparameters -- Example -- Regression -- Model Hyperparameters -- Example -- Recommendation -- Model Hyperparameters -- Example -- Deep Learning Pipeline -- Summary -- Chapter 9: Managing the Machine Learning Life Cycle -- The Rise of MLOps -- MLOps Overview -- MLflow Overview -- MLflow Components -- MLflow in Action -- MLflow Tracking -- MLflow Projects -- MLflow Models -- MLflow Model Registry -- Model Deployment and Prediction -- Summary -- Index. |
| Record Nr. | UNINA-9910506385403321 |
Hien Luu
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| New York, New York : , : Apress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Blockchain & distributed ledger technologies
| Blockchain & distributed ledger technologies |
| Pubbl/distr/stampa | [Washington, D.C.] : , : GAO - Science, Technology Assessment, and Analytics, , 2019 |
| Descrizione fisica | 1 online resource (2 pages) : color illustrations |
| Collana | Science & tech spotlight |
| Soggetto topico |
Blockchains (Databases)
Distributed databases Electronic funds transfers - Security measures |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Blockchain and distributed ledger technologies |
| Record Nr. | UNINA-9910713674203321 |
| [Washington, D.C.] : , : GAO - Science, Technology Assessment, and Analytics, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cooperative control of distributed multi-agent systems [[electronic resource] /] / edited by Jeff S. Shamma
| Cooperative control of distributed multi-agent systems [[electronic resource] /] / edited by Jeff S. Shamma |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 |
| Descrizione fisica | 1 online resource (453 p.) |
| Disciplina |
003.5
003/.5 |
| Altri autori (Persone) | ShammaJeff S |
| Soggetto topico |
Distributed artificial intelligence
Control theory Cooperation - Mathematics Distributed databases Electronic data processing - Distributed processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-31911-2
9786611319113 0-470-72420-X 0-470-72419-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cooperative Control of Distributed Multi-Agent Systems; Contents; List of Contributors; Preface; Part I Introduction; 1 Dimensions of cooperative control; 1.1 Why cooperative control?; 1.1.1 Motivation; 1.1.2 Illustrative example: command and control of networked vehicles; 1.2 Dimensions of cooperative control; 1.2.1 Distributed control and computation; 1.2.2 Adversarial interactions; 1.2.3 Uncertain evolution; 1.2.4 Complexity management; 1.3 Future directions; Acknowledgements; References; Part II Distributed Control and Computation
2 Design of behavior of swarms: From flocking to data fusion using microfilter networks2.1 Introduction; 2.2 Consensus problems; 2.3 Flocking behavior for distributed coverage; 2.3.1 Collective potential of flocks; 2.3.2 Distributed flocking algorithms; 2.3.3 Stability analysis for flocking motion; 2.3.4 Simulations of flocking; 2.4 Microfilter networks for cooperative data fusion; Acknowledgements; References; 3 Connectivity and convergence of formations; 3.1 Introduction; 3.2 Problem formulation; 3.3 Algebraic graph theory 3.4 Stability of vehicle formations in the case of time-invariant communication3.4.1 Formation hierarchy; 3.5 Stability of vehicle formations in the case of time-variant communication; 3.6 Stabilizing feedback for the time-variant communication case; 3.7 Graph connectivity and stability of vehicle formations; 3.8 Conclusion; Acknowledgements; References; 4 Distributed receding horizon control: stability via move suppression; 4.1 Introduction; 4.2 System description and objective; 4.3 Distributed receding horizon control; 4.4 Feasibility and stability analysis; 4.5 Conclusion; Acknowledgement References5 Distributed predictive control: synthesis, stability and feasibility; 5.1 Introduction; 5.2 Problem formulation; 5.3 Distributed MPC scheme; 5.4 DMPC stability analysis; 5.4.1 Individual value functions as Lyapunov functions; 5.4.2 Generalization to arbitrary number of nodes and graph; 5.4.3 Exchange of information; 5.4.4 Stability analysis for heterogeneous unconstrained LTI subsystems; 5.5 Distributed design for identical unconstrained LTI subsystems; 5.5.1 LQR properties for dynamically decoupled systems; 5.5.2 Distributed LQR design; 5.6 Ensuring feasibility 5.6.1 Robust constraint fulfillment5.6.2 Review of methodologies; 5.7 Conclusion; References; 6 Task assignment for mobile agents; 6.1 Introduction; 6.2 Background; 6.2.1 Primal and dual problems; 6.2.2 Auction algorithm; 6.3 Problem statement; 6.3.1 Feasible and optimal vehicle trajectories; 6.3.2 Benefit functions; 6.4 Assignment algorithm and results; 6.4.1 Assumptions; 6.4.2 Motion control for a distributed auction; 6.4.3 Assignment algorithm termination; 6.4.4 Optimality bounds; 6.4.5 Early task completion; 6.5 Simulations; 6.5.1 Effects of delays; 6.5.2 Effects of bidding increment 6.5.3 Early task completions |
| Record Nr. | UNINA-9910144579503321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cooperative control of distributed multi-agent systems [[electronic resource] /] / edited by Jeff S. Shamma
| Cooperative control of distributed multi-agent systems [[electronic resource] /] / edited by Jeff S. Shamma |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 |
| Descrizione fisica | 1 online resource (453 p.) |
| Disciplina |
003.5
003/.5 |
| Altri autori (Persone) | ShammaJeff S |
| Soggetto topico |
Distributed artificial intelligence
Control theory Cooperation - Mathematics Distributed databases Electronic data processing - Distributed processing |
| ISBN |
1-281-31911-2
9786611319113 0-470-72420-X 0-470-72419-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cooperative Control of Distributed Multi-Agent Systems; Contents; List of Contributors; Preface; Part I Introduction; 1 Dimensions of cooperative control; 1.1 Why cooperative control?; 1.1.1 Motivation; 1.1.2 Illustrative example: command and control of networked vehicles; 1.2 Dimensions of cooperative control; 1.2.1 Distributed control and computation; 1.2.2 Adversarial interactions; 1.2.3 Uncertain evolution; 1.2.4 Complexity management; 1.3 Future directions; Acknowledgements; References; Part II Distributed Control and Computation
2 Design of behavior of swarms: From flocking to data fusion using microfilter networks2.1 Introduction; 2.2 Consensus problems; 2.3 Flocking behavior for distributed coverage; 2.3.1 Collective potential of flocks; 2.3.2 Distributed flocking algorithms; 2.3.3 Stability analysis for flocking motion; 2.3.4 Simulations of flocking; 2.4 Microfilter networks for cooperative data fusion; Acknowledgements; References; 3 Connectivity and convergence of formations; 3.1 Introduction; 3.2 Problem formulation; 3.3 Algebraic graph theory 3.4 Stability of vehicle formations in the case of time-invariant communication3.4.1 Formation hierarchy; 3.5 Stability of vehicle formations in the case of time-variant communication; 3.6 Stabilizing feedback for the time-variant communication case; 3.7 Graph connectivity and stability of vehicle formations; 3.8 Conclusion; Acknowledgements; References; 4 Distributed receding horizon control: stability via move suppression; 4.1 Introduction; 4.2 System description and objective; 4.3 Distributed receding horizon control; 4.4 Feasibility and stability analysis; 4.5 Conclusion; Acknowledgement References5 Distributed predictive control: synthesis, stability and feasibility; 5.1 Introduction; 5.2 Problem formulation; 5.3 Distributed MPC scheme; 5.4 DMPC stability analysis; 5.4.1 Individual value functions as Lyapunov functions; 5.4.2 Generalization to arbitrary number of nodes and graph; 5.4.3 Exchange of information; 5.4.4 Stability analysis for heterogeneous unconstrained LTI subsystems; 5.5 Distributed design for identical unconstrained LTI subsystems; 5.5.1 LQR properties for dynamically decoupled systems; 5.5.2 Distributed LQR design; 5.6 Ensuring feasibility 5.6.1 Robust constraint fulfillment5.6.2 Review of methodologies; 5.7 Conclusion; References; 6 Task assignment for mobile agents; 6.1 Introduction; 6.2 Background; 6.2.1 Primal and dual problems; 6.2.2 Auction algorithm; 6.3 Problem statement; 6.3.1 Feasible and optimal vehicle trajectories; 6.3.2 Benefit functions; 6.4 Assignment algorithm and results; 6.4.1 Assumptions; 6.4.2 Motion control for a distributed auction; 6.4.3 Assignment algorithm termination; 6.4.4 Optimality bounds; 6.4.5 Early task completion; 6.5 Simulations; 6.5.1 Effects of delays; 6.5.2 Effects of bidding increment 6.5.3 Early task completions |
| Record Nr. | UNINA-9910830464603321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cooperative control of distributed multi-agent systems / / edited by Jeff S. Shamma
| Cooperative control of distributed multi-agent systems / / edited by Jeff S. Shamma |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 |
| Descrizione fisica | 1 online resource (453 p.) |
| Disciplina | 003/.5 |
| Altri autori (Persone) | ShammaJeff S |
| Soggetto topico |
Distributed artificial intelligence
Control theory Cooperation - Mathematics Distributed databases Electronic data processing - Distributed processing |
| ISBN |
1-281-31911-2
9786611319113 0-470-72420-X 0-470-72419-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cooperative Control of Distributed Multi-Agent Systems; Contents; List of Contributors; Preface; Part I Introduction; 1 Dimensions of cooperative control; 1.1 Why cooperative control?; 1.1.1 Motivation; 1.1.2 Illustrative example: command and control of networked vehicles; 1.2 Dimensions of cooperative control; 1.2.1 Distributed control and computation; 1.2.2 Adversarial interactions; 1.2.3 Uncertain evolution; 1.2.4 Complexity management; 1.3 Future directions; Acknowledgements; References; Part II Distributed Control and Computation
2 Design of behavior of swarms: From flocking to data fusion using microfilter networks2.1 Introduction; 2.2 Consensus problems; 2.3 Flocking behavior for distributed coverage; 2.3.1 Collective potential of flocks; 2.3.2 Distributed flocking algorithms; 2.3.3 Stability analysis for flocking motion; 2.3.4 Simulations of flocking; 2.4 Microfilter networks for cooperative data fusion; Acknowledgements; References; 3 Connectivity and convergence of formations; 3.1 Introduction; 3.2 Problem formulation; 3.3 Algebraic graph theory 3.4 Stability of vehicle formations in the case of time-invariant communication3.4.1 Formation hierarchy; 3.5 Stability of vehicle formations in the case of time-variant communication; 3.6 Stabilizing feedback for the time-variant communication case; 3.7 Graph connectivity and stability of vehicle formations; 3.8 Conclusion; Acknowledgements; References; 4 Distributed receding horizon control: stability via move suppression; 4.1 Introduction; 4.2 System description and objective; 4.3 Distributed receding horizon control; 4.4 Feasibility and stability analysis; 4.5 Conclusion; Acknowledgement References5 Distributed predictive control: synthesis, stability and feasibility; 5.1 Introduction; 5.2 Problem formulation; 5.3 Distributed MPC scheme; 5.4 DMPC stability analysis; 5.4.1 Individual value functions as Lyapunov functions; 5.4.2 Generalization to arbitrary number of nodes and graph; 5.4.3 Exchange of information; 5.4.4 Stability analysis for heterogeneous unconstrained LTI subsystems; 5.5 Distributed design for identical unconstrained LTI subsystems; 5.5.1 LQR properties for dynamically decoupled systems; 5.5.2 Distributed LQR design; 5.6 Ensuring feasibility 5.6.1 Robust constraint fulfillment5.6.2 Review of methodologies; 5.7 Conclusion; References; 6 Task assignment for mobile agents; 6.1 Introduction; 6.2 Background; 6.2.1 Primal and dual problems; 6.2.2 Auction algorithm; 6.3 Problem statement; 6.3.1 Feasible and optimal vehicle trajectories; 6.3.2 Benefit functions; 6.4 Assignment algorithm and results; 6.4.1 Assumptions; 6.4.2 Motion control for a distributed auction; 6.4.3 Assignment algorithm termination; 6.4.4 Optimality bounds; 6.4.5 Early task completion; 6.5 Simulations; 6.5.1 Effects of delays; 6.5.2 Effects of bidding increment 6.5.3 Early task completions |
| Record Nr. | UNINA-9910877081503321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
