This book describes the resilient navigation techniques under the background of collaboration in swarm. The significance of this work is that it focuses on the navigation enhancement by collaboration in swarm rather than ground infrastructure, which exploit potentialities of swarm in GNSS restricted environment. Although unmanned swarm is receiving greater attention, both through theoretical research and through increasing mention in the industrial developments, the navigation promotion by effective and efficient collaboration remains largely unexplored. While my scholarly work has explored some of the modeling, error characteristic, fusion algorithm, fault detection, and isolation aspects of the “adaptive navigation system” (such as the navigation system of robots and ground vehicles, aircrafts, aerospace vehicles, and unmanned aerial vehicles), the present book proposes the specialized investigation on the navigation with the resilient character, which could maintain the performance by essential collaboration with members in swarm in GNSS degradation environment. This book focused on the resilient navigation techniques under the background of collaboration in swarm. The key techniques of collaborative resilient navigation are proposed, including the collaboration framework, collaborative observation modeling, geometry optimization, integrity augmentation, and fault detection. The experiments are also carried out to validate the effectiveness of the corresponding techniques.
This book focuses on different facets of flight data analysis, including the basic goals, methods, and implementation techniques. As mass flight data possesses the typical characteristics of time series, the time series analysis methods and their application for flight data have been illustrated from several aspects, such as data filtering, data extension, feature optimization, similarity search, trend monitoring, fault diagnosis, and parameter prediction, etc. An intelligent information-processing platform for flight data has been established to assist in aircraft condition monitoring, training evaluation and scientific maintenance. The book will serve as a reference resource for people working in aviation management and maintenance, as well as researchers and engineers in the fields of data analysis and data mining.
This book involves collection of papers primarily focused on the origin and development of Chinese civilization in the concept of archaeological context from the 6000 BCE to 1300 BCE through archaeological cultural perspectives. It systematically illustrates the prehistoric cultural history of China at the period from Neolithic to the early Bronze Age during 20000-1300 BCE, composing not only the proper region around the Central Plain but also the margin areas mainly in the west, and examines the cultural relationship and exchanges nationally and internationally through thousand years of advancing social complexity in geographical and temporal genealogies. It introduces three prehistoric stages for the course of Chinese Civilization Development; the three major Civilization Development Models during the Chalcolithic period; how environmental changes and warfare functioned as the part of mechanism to make civilization evolve; the Bronze Age Revolution from the West; and the critical evaluation of the characteristics belonging to Chinese Civilization and the review of ancient legendary histories and legends through the archaeological perspectives. This book is essential reading for all those wanting more information about the foundations of Chinese history and civilization through archaeological studies. Jianye Han is Professor in the Department of Archaeology and Museology, School of History, Renmin University of China.
This compendium covers several important topics related to multiagent systems, from learning and game theoretic analysis, to automated negotiation and human-agent interaction. Each chapter is written by experienced researchers working on a specific topic in mutliagent system interactions, and covers the state-of-the-art research results related to that topic.The book will be a good reference material for researchers and graduate students working in the area of artificial intelligence/machine learning, and an inspirational read for those in social science, behavioural economics and psychology.
This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.
Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization. Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples. Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.
This book describes the resilient navigation techniques under the background of collaboration in swarm. The significance of this work is that it focuses on the navigation enhancement by collaboration in swarm rather than ground infrastructure, which exploit potentialities of swarm in GNSS restricted environment. Although unmanned swarm is receiving greater attention, both through theoretical research and through increasing mention in the industrial developments, the navigation promotion by effective and efficient collaboration remains largely unexplored. While my scholarly work has explored some of the modeling, error characteristic, fusion algorithm, fault detection, and isolation aspects of the “adaptive navigation system” (such as the navigation system of robots and ground vehicles, aircrafts, aerospace vehicles, and unmanned aerial vehicles), the present book proposes the specialized investigation on the navigation with the resilient character, which could maintain the performance by essential collaboration with members in swarm in GNSS degradation environment. This book focused on the resilient navigation techniques under the background of collaboration in swarm. The key techniques of collaborative resilient navigation are proposed, including the collaboration framework, collaborative observation modeling, geometry optimization, integrity augmentation, and fault detection. The experiments are also carried out to validate the effectiveness of the corresponding techniques.
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