Information, Uncertainty and Fusion

Information, Uncertainty and Fusion PDF

Author: Bernadette Bouchon-Meunier

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 456

ISBN-13: 1461552095

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As we stand at the precipice of the twenty first century the ability to capture and transmit copious amounts of information is clearly a defining feature of the human race. In order to increase the value of this vast supply of information we must develop means for effectively processing it. Newly emerging disciplines such as Information Engineering and Soft Computing are being developed in order to provide the tools required. Conferences such as the International Conference on Information Processing and ManagementofUncertainty in Knowledge-based Systems (IPMU) are being held to provide forums in which researchers can discuss the latest developments. The recent IPMU conference held at La Sorbonne in Paris brought together some of the world's leading experts in uncertainty and information fusion. In this volume we have included a selection ofpapers from this conference. What should be clear from looking at this volume is the number of different ways that are available for representing uncertain information. This variety in representational frameworks is a manifestation of the different types of uncertainty that appear in the information available to the users. Perhaps, the representation with the longest history is probability theory. This representation is best at addressing the uncertainty associated with the occurrence of different values for similar variables. This uncertainty is often described as randomness. Rough sets can be seen as a type of uncertainty that can deal effectively with lack of specificity, it is a powerful tool for manipulating granular information.

Uncertainty Theories and Multisensor Data Fusion

Uncertainty Theories and Multisensor Data Fusion PDF

Author: Alain Appriou

Publisher: John Wiley & Sons

Published: 2014-07-09

Total Pages: 288

ISBN-13: 1118578678

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Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.

Multi-Sensor Information Fusion

Multi-Sensor Information Fusion PDF

Author: Xue-Bo Jin

Publisher: MDPI

Published: 2020-03-23

Total Pages: 602

ISBN-13: 3039283022

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This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.

Uncertainty-sensitive Heterogeneous Information Fusion

Uncertainty-sensitive Heterogeneous Information Fusion PDF

Author: Paul K. Davis

Publisher: Rand Corporation

Published: 2016

Total Pages: 0

ISBN-13:

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Presents research on methods for heterogeneous information fusion--combining data that are qualitative, subjective, fuzzy, ambiguous, contradictory, and even deceptive, in order to form a realistic assessment of threat in a counterterrorism context.

Information Processing and Management of Uncertainty in Knowledge-Based Systems

Information Processing and Management of Uncertainty in Knowledge-Based Systems PDF

Author: Eyke Hüllermeier

Publisher: Springer Science & Business Media

Published: 2010-06-25

Total Pages: 786

ISBN-13: 3642140548

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The International Conference on Information Processing and Management of - certainty in Knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the management of uncertainty and aggregation of information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas between th theoreticians and practitioners working in these areas and related ?elds. The 13 IPMU conference took place in Dortmund, Germany, June 28–July 2, 2010. This volume contains 79 papers selected through a rigorous reviewing process. The contributions re?ect the richness of research on topics within the scope of the conference and represent several important developments, speci?cally focused on theoretical foundations and methods for information processing and management of uncertainty in knowledge-based systems. We were delighted that Melanie Mitchell (Portland State University, USA), Nihkil R. Pal (Indian Statistical Institute), Bernhard Sch ̈ olkopf (Max Planck I- titute for Biological Cybernetics, Tubing ̈ en, Germany) and Wolfgang Wahlster (German Research Center for Arti?cial Intelligence, Saarbruc ̈ ken) accepted our invitations to present keynote lectures. Jim Bezdek received the Kamp ́ede F ́ eriet Award, granted every two years on the occasion of the IPMU conference, in view of his eminent research contributions to the handling of uncertainty in clustering, data analysis and pattern recognition.

Combating Uncertainty With Fusion

Combating Uncertainty With Fusion PDF

Author:

Publisher:

Published: 2003

Total Pages: 35

ISBN-13:

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This report is a summary of a NASA/ONR-sponsored workshop, Combating Uncertainty with Fusion, that was organized in Woods Hole in April 2002. The main purpose of the workshop was to address a class of difficult computational problems that are characterized by combining large amounts of data or datasets from diverse sources that are related in complex, stochastic, and poorly understood ways. The intent was to determine whether understanding of biological fusion processes could provide guidance to the development of robust algorithms that would alleviate the difficulties encountered in a variety of application areas including the Earth Observation System.

Context-Enhanced Information Fusion

Context-Enhanced Information Fusion PDF

Author: Lauro Snidaro

Publisher: Springer

Published: 2016-05-25

Total Pages: 696

ISBN-13: 3319289713

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This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.

Artificial Intelligence in Construction Engineering and Management

Artificial Intelligence in Construction Engineering and Management PDF

Author: Limao Zhang

Publisher: Springer Nature

Published: 2021-06-18

Total Pages: 271

ISBN-13: 9811628424

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This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally.