Method Of Classification Of Global Machine Conditions Based On Spectral Features Of Infrared Images And Classifiers Fusion
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Method of classification of global machine conditions based on spectral features of infrared images and classifiers fusion by Marek Fidali ,Wojciech Jamrozik Pdf
This paper describes an original method of global machine condition assessment for infrared condition monitoring and diagnostics systems. This method integrates two approaches: the first is processing and analysis of infrared images in the frequency domain by the use of 2D Fourier transform and a set of F-image features, the second uses fusion of classification results obtained independently for F-image features. To find the best condition assessment solution, the two different types of classifiers, k-nearest neighbours and support vector machine, as well as data fusion method based on Dezert–Smarandache theory have been investigated. This method has been verified using infrared images recorded during experiments performed on the laboratory model of rotating machinery. The results obtained during the research confirm that the method could be successfully used for the identification of operational conditions that are difficult to be recognised.
Advances and Applications of DSmT for Information Fusion (Collected Works. Volume 5) by Florentin Smarandache,Jean Dezert,Albena Tchamova Pdf
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 (available at fs.unm.edu/DSmT-book4.pdf or www.onera.fr/sites/default/files/297/2015-DSmT-Book4.pdf) in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. We want to thank all the contributors of this fifth volume for their research works and their interests in the development of DSmT, and the belief functions. We are grateful as well to other colleagues for encouraging us to edit this fifth volume, and for sharing with us several ideas and for their questions and comments on DSmT through the years. We thank the International Society of Information Fusion (www.isif.org) for diffusing main research works related to information fusion (including DSmT) in the international fusion conferences series over the years. Florentin Smarandache is grateful to The University of New Mexico, U.S.A., that many times partially sponsored him to attend international conferences, workshops and seminars on Information Fusion. Jean Dezert is grateful to the Department of Information Processing and Systems (DTIS) of the French Aerospace Lab (Office National d’E´tudes et de Recherches Ae´rospatiales), Palaiseau, France, for encouraging him to carry on this research and for its financial support. Albena Tchamova is first of all grateful to Dr. Jean Dezert for the opportunity to be involved during more than 20 years to follow and share his smart and beautiful visions and ideas in the development of the powerful Dezert-Smarandache Theory for data fusion. She is also grateful to the Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, for sponsoring her to attend international conferences on Information Fusion.
Handbook of Intelligent Computing and Optimization for Sustainable Development by Mukhdeep Singh Manshahia,Valeriy Kharchenko,Elias Munapo,J. Joshua Thomas,Pandian Vasant Pdf
HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.
Towards Autonomous Robotic Systems by Kaspar Althoefer,Jelizaveta Konstantinova,Ketao Zhang Pdf
The two volumes LNAI 11649 and 11650 constitute the refereed proceedings of the 20th Annual Conference "Towards Autonomous Robotics", TAROS 2019, held in London, UK, in July 2019. The 87 full papers and 12 short papers presented were carefully reviewed and selected from 101 submissions. The papers present and discuss significant findings and advances in autonomous robotics research and applications. They are organized in the following topical sections: robotic grippers and manipulation; soft robotics, sensing and mobile robots; robotic learning, mapping and planning; human-robot interaction; and robotic systems and applications.
International Conference on Oriental Thinking and Fuzzy Logic by Bing-Yuan Cao,Pei-Zhuang Wang,Zeng-Liang Liu,Yu-Bin Zhong Pdf
This proceedings book presents edited results of the eighth International Conference on Fuzzy Information and Engineering (ICFIE'2015) and on Oriental Thinking and Fuzzy Logic, in August 17-20, 2015, in Dalian, China. The book contains 65 high-quality papers and is divided into six main parts: "Fuzzy Information Processing", "Fuzzy Engineering", "Internet and Big Data Applications", "Factor Space and Factorial Neural Networks", "Information Granulation and Granular Computing" as well as "Extenics and Innovation Methods".
Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing by Ni-Bin Chang,Kaixu Bai Pdf
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
Climate Change Adaptation in Eastern Europe by Walter Leal Filho,Goran Trbic,Dejan Filipovic Pdf
This book focuses on managing risks and building resilience to climate change, showcasing experiences from research, field projects and best practices to foster climate change adaptation in Eastern Europe that can be implemented elsewhere. Climate change affects countries in Eastern Europe, i.e. the Western Balkans and Southeast Europe in a variety of ways. Apart from severe floods, there are reports of decreasing water reserves in the southern regions, and of gradual changes in biodiversity and agricultural production. In the South Caucasus area, for instance, climate change models project a decline in precipitation and suggest that it will continue to become drier this century. Many Eastern European countries, especially the non-EU ones, have weak national climate policies, and transboundary collaborations, as well as limited public engagement in matters related to climate change. As a result, climate change poses a serious threat to their economic stability and development and to the sustainable development of the region. The above state of affairs illustrates the need for a better understanding of how climate change influences Eastern Europe, and for the identification of processes, methods and tools that may help the countries and the communities in the region to adapt. There is also a perceived need to showcase successful examples of how to cope with the social, economic and political problems posed by floods/droughts in the region, especially ways of increasing the resilience of agriculture systems and of communities. Addressing this need, the book presents papers written by scholars, social practitioners and members of government agencies involved in research and/or climate change projects in Eastern Europe.
Hyperspectral Image Analysis by Saurabh Prasad,Jocelyn Chanussot Pdf
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Climate Change, Hazards and Adaptation Options by Walter Leal Filho,Gustavo J. Nagy,Marco Borga,Pastor David Chávez Muñoz,Artur Magnuszewski Pdf
This book addresses the issue of climate change risks and hazards holistically. Climate change adaptation aims at managing climate risks and hazards to an acceptable level, taking advantage of any positive opportunities that may arise. At the same time, developing suitable responses to hazards for communities and users of climate services is important in ensuring the success of adaptation measures. But despite this, knowledge about adaptation options, including possible actions that can be implemented to improve adaptation and reduce the impacts of climate change hazards, is still limited. Addressing this need, the book presents studies and research findings and offers a catalogue of potential adaptation options that can be explored. It also includes case studies providing illustrative and inspiring examples of how we can adapt to a changing climate.
This book sums up key research findings, and theoretical and technological advances having a direct bearing on neuroergonomics. Neuroergonomics is an emerging area whose Neuroergonomics is an emerging area that is collectively defined as the study of human brain function and behaviour in relation to behavioural performance in natural environments and everyday settings. It helps readers to understand neural mechanisms of human cognition in the context of human interaction with complex systems, as well as understanding the change of perception, decision-making and training in humans. The authors give new insights into augmenting human performance, reflecting upon the opportunities provided through neuroergonomics research and development. Computer systems acting on data from behavioural-output, physiological, and neurological sensing technologies are used to determine the user’s cognitive state and adapt the systems to change, support, and monitor human cognition. Various domains and case studies delve into the field of neuroergonomics in detail. These include, but are not limited to: an evaluation of technologies in health, workplace, and education settings, to show the different impacts of neuroergonomics in everyday lives; assessment of real-time cognitive measures; dynamic casual interactions between inhibition and updating functions, through analysis of behavioral, neurophysiological and effective connectivity metrics; and applications in human performance modelling and assessment of mental workload, showing the reader how to train and improve working memory capacity. Neuroergonomics: Principles and Practice provides academic practitioners and graduate students with a single go-to handbook that will be of significant assistance in research associated with human factors and ergonomics, human-computer interaction, human-systems engineering and cognitive neuroscience.
An Introduction to Infrared Image Acquisition and Classification Systems by Louis-François Pau,M. Y. El Nahas Pdf
Provides an integrated introduction to infrared sensors, propagation, imaging systems, source simulation, image analysis and feature extraction, design and measurement, and hardware considerations. Stresses the main design rules and concepts for infrared image acquisition and infrared source classification systems. .
