Bayesian Methods in the Search for MH370


Book Description

This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.




The Final Path of Flight Mh370


Book Description

Multiobjective Genetic Algorithms for detecting the Malaysia Airlines Flight 370 delivers the critical tool needed to understand its vanishing scenario in the southern Indian Ocean. Filling the gap between the conspiracy theories of MH370 vanishing and remote sensing detected debris, this reference is packed with technical details associated with the critical questions of has not MH370 vanished in the southern Indian Ocean, and where is last destination of MH370? Rounding out with practical simulation trajectory movements of MH370 debris using the ocean dynamic features, Multiobjective Genetic Algorithms bring an effective evident of the last destination of MH370. Key Features • Bridge between the conspiracy theories of missing MH370 and remote sensing technology. • Understanding a new approach of debris automatic detection. • Advance knowledge on image processing based on multiobjective genetic algorithms. • Disprove some current theories of MH370 missing and suggest new answers




Vehicle Accident Analysis and Reconstruction Methods


Book Description

In this third edition of Vehicle Accident Analysis & Reconstruction Methods, Raymond M. Brach and R. Matthew Brach have expanded and updated their essential work for professionals in the field of accident reconstruction. Most accidents can be reconstructed effectively using of calculations and investigative and experimental data: the authors present the latest scientific, engineering, and mathematical reconstruction methods, providing a firm scientific foundation for practitioners. Accidents that cannot be reconstructed using the methods in this book are rare. In recent decades, the field of crash reconstruction has been transformed through the use of technology. The advent of event data records (EDRs) on vehicles signaled the era of modern crash reconstruction, which utilizes the same physical evidence that was previously available as well as electronic data that are measured/captured before, during, and after the collision. There is increased demand for more professional and accurate reconstruction as more crash data is available from vehicle sensors. The third edition of this essential work includes a new chapter on the use of EDRs as well as examples using EDR data in accident reconstruction. Early chapters feature foundational material that is necessary for the understanding of vehicle collisions and vehicle motion; later chapters present applications of the methods and include example reconstructions. As a result, Vehicle Accident Analysis & Reconstruction Methods remains the definitive resource in accident reconstruction.




Genetic Algorithms and Remote Sensing Technology for Tracking Flight Debris


Book Description

The tragic disappearance of the Malaysia Airlines Flight MH370 has created a need for research in the areas of aircraft transportation, and specifically flight debris tracking. As researchers and scientists continue to search for novel technologies that will assist with aeronautical detection, two modes have emerged as possible solutions. The use of remote sensing technology and genetic algorithms are techniques that scientists are beginning to use to improve aircraft trajectory models and to locate downed aircraft. Genetic Algorithms and Remote Sensing Technology for Tracking Flight Debris is an essential reference source that discusses developing tracking methods using advanced algorithms as well as satellite technologies. Featuring research on topics such as wave pattern modeling, microwave satellite data, and trajectory movement, this book is ideally designed for practitioners, researchers, oceanographers, aerospace engineers, scientists, educators, developers, analysts, and students seeking coverage on advancements in sensor and tracking technology in regard to flight dynamics.




China Satellite Navigation Conference (CSNC) 2018 Proceedings


Book Description

These proceedings present selected research papers from CSNC 2018, held during 23rd-25th May in Harbin, China. The theme of CSNC 2018 is Location, Time of Augmentation. These papers discuss the technologies and applications of the Global Navigation Satellite System (GNSS), and the latest progress made in the China BeiDou System (BDS) especially. They are divided into 12 topics to match the corresponding sessions in CSNC 2018, which broadly covered key topics in GNSS. Readers can learn about the BDS and keep abreast of the latest advances in GNSS techniques and applications.




Computer Simulation Validation


Book Description

This unique volume introduces and discusses the methods of validating computer simulations in scientific research. The core concepts, strategies, and techniques of validation are explained by an international team of pre-eminent authorities, drawing on expertise from various fields ranging from engineering and the physical sciences to the social sciences and history. The work also offers new and original philosophical perspectives on the validation of simulations. Topics and features: introduces the fundamental concepts and principles related to the validation of computer simulations, and examines philosophical frameworks for thinking about validation; provides an overview of the various strategies and techniques available for validating simulations, as well as the preparatory steps that have to be taken prior to validation; describes commonly used reference points and mathematical frameworks applicable to simulation validation; reviews the legal prescriptions, and the administrative and procedural activities related to simulation validation; presents examples of best practice that demonstrate how methods of validation are applied in various disciplines and with different types of simulation models; covers important practical challenges faced by simulation scientists when applying validation methods and techniques; offers a selection of general philosophical reflections that explore the significance of validation from a broader perspective. This truly interdisciplinary handbook will appeal to a broad audience, from professional scientists spanning all natural and social sciences, to young scholars new to research with computer simulations. Philosophers of science, and methodologists seeking to increase their understanding of simulation validation, will also find much to benefit from in the text.




Autonomy and Artificial Intelligence: A Threat or Savior?


Book Description

This book explores how Artificial Intelligence (AI), by leading to an increase in the autonomy of machines and robots, is offering opportunities for an expanded but uncertain impact on society by humans, machines, and robots. To help readers better understand the relationships between AI, autonomy, humans and machines that will help society reduce human errors in the use of advanced technologies (e.g., airplanes, trains, cars), this edited volume presents a wide selection of the underlying theories, computational models, experimental methods, and field applications. While other literature deals with these topics individually, this book unifies the fields of autonomy and AI, framing them in the broader context of effective integration for human-autonomous machine and robotic systems. The contributions, written by world-class researchers and scientists, elaborate on key research topics at the heart of effective human-machine-robot-systems integration. These topics include, for example, computational support for intelligence analyses; the challenge of verifying today’s and future autonomous systems; comparisons between today’s machines and autism; implications of human information interaction on artificial intelligence and errors; systems that reason; the autonomy of machines, robots, buildings; and hybrid teams, where hybrid reflects arbitrary combinations of humans, machines and robots. The contributors span the field of autonomous systems research, ranging from industry and academia to government. Given the broad diversity of the research in this book, the editors strove to thoroughly examine the challenges and trends of systems that implement and exhibit AI; the social implications of present and future systems made autonomous with AI; systems with AI seeking to develop trusted relationships among humans, machines, and robots; and the effective human systems integration that must result for trust in these new systems and their applications to increase and to be sustained.




Track-Before-Detect Using Expectation Maximisation


Book Description

This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems.




Malaysia Airlines Flight 370


Book Description

On March 8, 2014, Malaysia Airlines Flight 370 loaded 239 people on board and took off for what should have been a six-hour flight. It never made it—and it’s still missing. It’s been a year since Malaysia Airlines Flight 370 vanished, and there’s still no sign of the aircraft, its passengers, or its crew—nor confirmation of what happened or where the aircraft resides. In this gripping investigation of the events that led to the plane’s disappearance—and why they could happen again—CNN aviation analyst David Soucie exposes the flaws in the aviation industry, shares what needs to be done so a plane doesn’t go missing again, and uses a Bayesian analysis model to reveal what most likely happened on board the plane that led to its downfall. Comprehensive in scope, personal and empathetic in voice, Soucie draws on his thirty years of experience as an accident investigator working with the Federal Aviation Administration. He allows you to put the wild speculation about the plane’s disappearance aside and assess the facts through the eyes of an experienced accident investigator.




Bayesian Statistical Methods


Book Description

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.