01064nam--2200361---450-99000199866020331620050913135237.0000199866USA01000199866(ALEPH)000199866USA0100019986620040910d1964----km-y0itay0103----bafreFR||||||||001yyService des affaires classéesRoy Vickerspreface de Ellery QueenParisClub du Livre Policier1964432 p.21 cm<<Le>> livre de poche2602001<<Le>> livre de poche2602001001-------2001VICKERS,Roy566119QUEEN,ElleryITsalbcISBD990001998660203316VI.4. Coll.13/ 567(II f G 4 260)21050 L.M.II f GBKUMASIAVER9020040910USA011413COPAT59020050913USA011352Service des affaires classées1046054UNISA03120nam 22004693 450 991016390210332120230810002109.09781623171131162317113X(CKB)3710000001055982(MiAaPQ)EBC6107954(Au-PeEL)EBL6107954(OCoLC)961214030(Exl-AI)6107954(EXLCZ)99371000000105598220210901d2017 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierThe Genius of Being Contemplating the Profound Intelligence of ExistenceBerkeley :North Atlantic Books,2017.©2017.1 online resource (163 pages)9781623171124 1623171121 Titlepage -- Copyright -- Chapter One: Grasping the Genius of Being -- Bound by Brilliance -- Fact and Truth, Belief and Assumption -- Chapter Two: Getting from Here to There -- Grasping the Communication—Developing Existential Listening -- The Truth Principle -- Not Knowing: An Essential Tool for Contemplation -- A Path to Experiential Learning -- Chapter Three: The Consummate Dominion of Mind and Perception -- Self-Orientation and “The Bull” Contemplation -- Social Management and Self-Image -- Cultural and Personal Beliefs -- The Creative Aspect of Mind -- What Is a Distinction? -- Chapter Four: The Creative World of Language -- What Is Language? -- Self and Other -- Language vs. Concept -- Creating an Internal Person -- Forming a Social Self -- Internal Dialogue: Whom Are You Talking To? -- Chapter Five: A Complex Matrix of Mind -- A Framework for Reality -- Human and Cultural Assumptions -- An Assumed Reality -- The Consequences of Being an Object -- The Consequences of Being “Inside” -- The Assumption of Life and SentienceGenerated by AI.Peter Ralston's 'The Genius of Being' explores the profound intelligence of existence through the lens of consciousness and ontology. The book delves into the nature of being, perception, and the mind, offering insights into self-awareness and the creation of one's reality. Ralston encourages readers to question assumptions and beliefs, aiming to foster a deeper understanding of themselves and the world. The work is designed for individuals interested in consciousness studies, personal growth, and philosophical inquiry. Through contemplation and experiential learning, Ralston guides readers toward a transformative understanding of existence, promoting a shift in perspective that reveals the creative power inherent in being.Generated by AI.ConsciousnessGenerated by AIOntologyGenerated by AIConsciousnessOntology128Ralston Peter1079686MiAaPQMiAaPQMiAaPQBOOK9910163902103321The Genius of Being2592361UNINA10775nam 22004813 450 991103446910332120251201110046.01-394-35507-61-394-35505-X(CKB)41593476400041(MiAaPQ)EBC32336717(Au-PeEL)EBL32336717(OCoLC)1544934995(CaSebORM)9781394355044(EXLCZ)994159347640004120251012d2025 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAutonomous Marine Vehicles Planning and Control1st ed.Newark :John Wiley & Sons, Incorporated,2025.©2026.1 online resource (505 pages)1-394-35504-1 Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Overview -- 1.2 System Structure -- 1.3 Mathematical Model of a USV -- 1.4 Maritime Applications -- 1.5 Motivation of this Book -- References -- Chapter 2 Automatic Control Module -- 2.1 Origin and Development -- 2.2 Common Control System Development -- 2.2.1 Dynamic Positioning and Position Mooring Systems -- 2.2.1.1 Dynamic Positioning Control System -- 2.2.1.2 Position Mooring Control System -- 2.2.2 Waypoint Tracking and Path-Following Control Systems -- 2.2.2.1 Waypoint Tracking Control System -- 2.2.2.2 Path-Following Control System -- 2.3 Advanced Control System Development -- 2.3.1 Linear Quadratic Optimal Control -- 2.3.2 State Feedback Linearization -- 2.3.2.1 Decoupling in the BODY Frame (Velocity Control) -- 2.3.2.2 Decoupling in the NED Frame (Position and Attitude Control) -- 2.3.3 Integrator Backstepping Control -- 2.3.4 Sliding-Mode Control -- 2.3.4.1 SISO Sliding-Mode Control -- 2.3.4.2 Sliding-Mode Control Using the Eigenvalue Decomposition -- References -- Chapter 3 Perception and Sensing Module -- 3.1 Low-Pass and Notch Filtering -- 3.1.1 Low-Pass Filtering -- 3.1.2 Cascaded Low-Pass and Notch Filtering -- 3.2 Fixed Gain Observer Design -- 3.2.1 Observability -- 3.2.2 Luenberger Observer -- 3.2.3 Case Study: Luenberger Observer for Heading Autopilots Using Only Compass Measurements -- 3.3 Kalman Filter Design -- 3.3.1 Discrete-Time Kalman Filter -- 3.3.2 Continuous-Time Kalman Filter -- 3.3.3 Extended Kalman Filter -- 3.3.4 Corrector-Predictor Representation for Nonlinear Observers -- 3.3.5 Case Study: Kalman Filter for Heading Autopilots Using Only Compass Measurements -- 3.3.5.1 Heading Sensors Overview -- 3.3.5.2 System Model for Heading Autopilot Observer Design.3.3.6 Case Study: Kalman Filter for Dynamic Positioning Systems Using GNSS and Compass Measurements -- 3.4 Nonlinear Passive Observer Designs -- 3.4.1 Case Study: Passive Observer for Dynamic Positioning Using GNSS and Compass Measurements -- 3.4.2 Case Study: Passive Observer for Heading Autopilots Using only Compass Measurements -- 3.4.3 Case Study: Passive Observer for Heading Autopilots Using Both Compass and Rate Measurements -- 3.5 Integration Filters for IMU and Global Navigation Satellite Systems -- 3.5.1 Integration Filter for Position and Linear Velocity -- 3.5.2 Accelerometer and Compass Aided Attitude Observer -- 3.5.3 Attitude Observer Using Gravitational and Magnetic Field Directions -- References -- Chapter 4 Model Predictive Control for Autonomous Marine Vehicles: A Review -- 4.1 Introduction -- 4.1.1 Object Introduction -- 4.1.2 Previous Reviews -- 4.2 Fundamental Models and a General Picture -- 4.2.1 Model of AMVs -- 4.2.1.1 6-DOF Model -- 4.2.1.2 3-DOF Model -- 4.2.2 Model Predictive Control -- 4.2.3 Literature Search -- 4.3 Methodology -- 4.3.1 MPC Applications of AMVs -- 4.3.1.1 Real-Coded Chromosome -- 4.3.1.2 Path Following -- 4.3.1.3 Trajectory Tracking -- 4.3.1.4 Cooperative Control/Formation Control -- 4.3.1.5 Collision Avoidance -- 4.3.1.6 Energy Management -- 4.3.1.7 Other Topics -- 4.4 Discussion -- 4.4.1 Limitations of Existing Techniques and Challenges in Developing MPC -- 4.4.1.1 Uncertainties of AMV Motion Models -- 4.4.1.2 Stability and Security of the New MPC Method -- 4.4.1.3 The Balance Between Effectiveness and Efficiency of the Methods -- 4.4.1.4 The Practical Application Scenario of the MPC and the Discussion of the Working Conditions -- 4.4.1.5 Challenges Posed by the Marine Environment Affect MPC Development for AMVs -- 4.4.2 Trends in the Technology Development for MPC in AMV.4.4.2.1 More Cooperative Control with MPC -- 4.4.2.2 Rigorous Theoretical Derivation and Experimental Verification -- 4.4.2.3 Real-Time MPC for AMVs Applications -- 4.4.2.4 The Combination of Machine Learning/Neural Networks and MPC for AMVs Applications -- 4.4.2.5 Address the Challenges Posed by the Marine Environment -- 4.4.2.6 Potential Interdisciplinary Approaches that Combine MPC with Other Innovative Fields -- 4.5 Conclusion -- Acknowledgement -- References -- Chapter 5 Controller-Consistent Path Planning for Unmanned Surface Vehicles -- 5.1 Introduction -- 5.2 Problem Formulation -- 5.3 Methodology -- 5.3.1 Improved Artificial Fish Swarm Algorithm -- 5.3.1.1 Prey Behavior -- 5.3.1.2 Follow Behavior -- 5.3.1.3 Swarm Behavior -- 5.3.1.4 Random Behavior -- 5.3.1.5 Adaptive Visual and Step -- 5.3.2 Expanding Technique -- 5.3.3 Node Cutting and Path Smoother -- 5.3.4 Establishment of USV Model -- 5.4 Simulation -- 5.4.1 Monte Carlo Simulation -- 5.4.2 Path Quality Test -- 5.4.3 Simulation Using USV Control Model in Practical Environment -- 5.5 Conclusion -- References -- Chapter 6 Nonlinear Model Predictive Control and Routing for USV-Assisted Water Monitoring -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.2.1 Heterogeneous Global Path Planning Problem -- 6.2.1.1 USV Model -- 6.2.1.2 Task Model -- 6.2.1.3 Problem Statement -- 6.2.2 Problem Analysis -- 6.2.3 Path Following Problem -- 6.2.3.1 Basic Assumptions -- 6.2.3.2 Vessel Model -- 6.2.3.3 Problem Description -- 6.3 Methodology -- 6.3.1 Greedy Partheno Genetic Algorithm -- 6.3.1.1 Dual-Coded Chromosome -- 6.3.1.2 Fitness Function -- 6.3.1.3 Greedy Randomized Initialization -- 6.3.1.4 Local Exploration -- 6.3.1.5 Mutation Operators -- 6.3.1.6 Algorithm Flow -- 6.3.2 Nonlinear Model Predictive Control -- 6.3.2.1 State Space Model -- 6.3.2.2 NMPC Design -- 6.3.2.3 Solver -- 6.3.2.4 Stability.6.4 Results and Discussion -- 6.4.1 Simulation: Global Task Planning -- 6.4.1.1 Convergence Test -- 6.4.1.2 Heterogeneous Task Planning -- 6.4.2 Simulation: NMPC Control Performance -- 6.4.2.1 Test 1: Simulation Under Different Model Uncertainties -- 6.4.2.2 Test 2: Comparative Study with Other Methods -- 6.4.3 Simulation Verification of the Framework -- 6.5 Conclusion -- References -- Chapter 7 Global-Local Hierarchical Framework for USV Trajectory Planning -- 7.1 Introduction -- 7.2 Problem Formulation -- 7.2.1 Marine Environment -- 7.2.2 Dynamic Obstacles -- 7.2.3 Effects of Currents -- 7.2.4 USV Model and Constraints -- 7.2.5 Protocol Constraints -- 7.2.6 Objective Functions -- 7.2.6.1 The Minimum Cruising Time -- 7.2.6.2 The Minimum Variation of Heading Angle -- 7.2.6.3 The Safest Path -- 7.2.7 Problem Statement -- 7.3 Methodology -- 7.3.1 Adaptive-Elite GA with Fuzzy Inference (AEGAfi) -- 7.3.1.1 Real-Coded Chromosome -- 7.3.1.2 Initialization Based on Adaptive Random Testing (ART) -- 7.3.1.3 Adaptive Elite Selection -- 7.3.1.4 Double-Functioned Crossover -- 7.3.1.5 Mutation Operators -- 7.3.1.6 Fuzzy-Based Probability Choice -- 7.3.1.7 Fitness Function Design -- 7.3.2 Replanning Strategy Based on Sensory Vector -- 7.3.2.1 Sensory Vector Structure -- 7.3.2.2 Formulation of Vs -- 7.3.2.3 Formulation of Gap Vector Vg Based on COLREGs -- 7.3.2.4 Formulation of Transition Path -- 7.4 Simulation Study -- 7.4.1 Convergence Benchmark Analysis -- 7.4.2 Simulation Under Static Environment -- 7.4.3 Simulation Under Time-Varying Environment -- 7.4.4 Simulation on Real-World Geography -- 7.5 Conclusion -- Appendix -- List of Abbreviations -- Acknowledgements -- References -- Chapter 8 Reinforcement Learning for USV-Assisted Wireless Data Harvesting -- 8.1 Introduction -- 8.2 Fundamental Models -- 8.2.1 Environment Model.8.2.2 Sensor Node and Communication Model -- 8.2.3 USV Model -- 8.2.3.1 Kinematic Model -- 8.2.3.2 Sensing Module -- 8.3 Methodology -- 8.3.1 Brief States on Q-Learning -- 8.3.2 Interactive Learning -- 8.3.2.1 Heuristic Reward Design -- 8.3.2.2 Design of Value-Iterated Global Cost Matrix -- 8.3.2.3 Local Cost Matrix and Path Generation -- 8.3.2.4 USV Actions with Discrete Precise Clothoid Path -- 8.3.3 Summary of the Path Planning Algorithm -- 8.3.4 Time Complexity -- 8.4 Results and Discussion -- 8.4.1 Performance Indicators -- 8.4.2 Hyper-Parameter Analysis -- 8.4.3 Comparative Study with State of the Art -- 8.5 Conclusion -- Appendix -- References -- Chapter 9 Achieving Optimal Dynamic Path Planning for Unmanned Surface Vehicles: A Rational Multi-Objective Approach and a Sensory-Vector Re-Planner -- 9.1 Introduction -- 9.2 Problem Formulation -- 9.2.1 Environment Modeling -- 9.2.1.1 Motion Area -- 9.2.1.2 Effects of Currents -- 9.2.2 Dynamic Obstacles -- 9.2.3 Motion Constraints -- 9.2.4 Objective Functions -- 9.2.4.1 Path Length -- 9.2.4.2 Path Smoothness -- 9.2.4.3 Energy Consumption -- 9.2.4.4 The Safest Path -- 9.2.5 Optimization Problem Statement -- 9.3 Methodology -- 9.3.1 Framework of NSGA-II -- 9.3.2 AENSGA-II -- 9.3.2.1 Real-Coded Representation -- 9.3.2.2 Initialization Using Candidate Set Adaptive Random Testing (CSART) -- 9.3.2.3 Adaptive Crowding Distance (ACD) Strategy -- 9.3.2.4 Improved Binary Tournament Selection -- 9.3.3 Fuzzy Satisfactory Degree -- 9.3.4 Replanning Strategy Based on Sensory Vector -- 9.3.4.1 Sensory Vector Structure -- 9.3.4.2 Formulation of Gap Vector Vg Based on COLREGs -- 9.3.4.3 Formulation of Transition Path -- 9.4 Results and Discussion -- 9.4.1 Convergence and Diversity Analysis -- 9.4.2 Implementation in Static Environment -- 9.4.2.1 Fixed Currents -- 9.4.2.2 Time-Varying Currents.9.4.3 Simulation Under Dynamic Environment.Master the future of marine exploration and technology with Autonomous Marine Vehicles Planning and Control , which provides a comprehensive, interdisciplinary guide to the principles, control, and real-world applications of autonomous marine vehicles.Bai Yong627309Zhao Liang979440MiAaPQMiAaPQMiAaPQBOOK9911034469103321Autonomous Marine Vehicles Planning and Control4445977UNINA