Download or read book Method of classification of global machine conditions based on spectral features of infrared images and classifiers fusion written by Marek Fidali and published by Infinite Study. This book was released on with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances and Applications of DSmT for Information Fusion Collected Works Volume 5 written by Florentin Smarandache and published by Infinite Study. This book was released on 2023-12-27 with total page 932 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Handbook of Intelligent Computing and Optimization for Sustainable Development written by Mukhdeep Singh Manshahia and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 948 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Classification Methods for Remotely Sensed Data written by Taskin Kavzoglu and published by CRC Press. This book was released on 2024-09-04 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. New in this edition: Provides comprehensive background on the theory of deep learning and its application to remote sensing data. Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications. Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies. Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models. This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc.

Download or read book International Aerospace Abstracts written by and published by . This book was released on 1999 with total page 1048 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi classifiers and Decision Fusion for Robust Statistical Pattern Recognition with Applications to Hyperspectral Classification written by Saurabh Prasad and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers' generalization ability in that the classifier tends to over-fit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform "local" classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches.

Download or read book A Spectral textural Classifier for Digital Imagery written by Jong-hun Lee and published by . This book was released on 1990 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing written by Ni-Bin Chang and published by CRC Press. This book was released on 2018-02-21 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Pattern Recognition Methods for Automated Detection and Quantification written by Hua Yu and published by . This book was released on 2014 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition has over past decades become a fast growing area of chemometrics. Accurate, user-friendly, and fast pattern recognition methods are desired to accommodate the increased capacity of automated instruments to obtain large-scale data under complex circumstances. It has found significant applications in diverse fields such as environmental monitoring and biomedical diagnostics. In this dissertation, the capabilities of pattern recognition methods in case studies related to environmental remote sensing and biomedical sensing are investigated. For remote sensing applications, two types of airborne spectroscopic data, passive Fourier transform infrared (FTIR) and gamma-ray, are subject to analysis in order to develop automated classifiers for either ammonia vapor or the radioisotope cesium-137 in the open-air. Support vector machine (SVM) classification is the primary pattern recognition method used in this work. In order to overcome the limitation of available representative patterns associated with airborne data, and provide sufficient patterns presenting the analyte-active class for use in the training set, a spectral simulation protocol is employed to generate abundant patterns bearing both the signature of the target analyte and the background spectral profile. Signal processing procedures including segment selection and digital filtering are further used to extract the information most relevant to the target analyte out the acquired raw data. Also, to ease the computational demand from the SVM, an alternative pattern recognition method, piecewise linear discriminant analysis (PLDA) is applied to optimize signal processing conditions for final SVM classification. Process control techniques are applied to the SVM score profiles of prediction sets to improve pattern recognition performance by incorporating probabilities associated with every SVM score. Ammonia classifiers developed from this methodology result in classification performance with high sensitivity and selectivity, and the cesium-137 classifiers developed from the same concepts exhibit excellent sensitivity to test data with very low signal strengths. Under the case of ammonia classification, the relationship between the concentration profile of the active patterns in the training set and the limit of detection of the corresponding classifier is investigated. Classifiers built to detect low concentrations of ammonia are developed and tested through this work. For a glucose sensing application, studies are conducted to provide sound performance diagnostics for an established calibration model for glucose from near infrared spectroscopic data. Six-component aqueous matrixes of glucose in the presence of five other interfering species, all spanning physiological levels, serve as samples to be analyzed. A novel residual modeling protocol is proposed to retrieve the residual glucose concentrations, the concentration not being predicted by the calibration model, from the residual spectra, the portion of the raw spectra not being used by the calibration model. The recovered glucose concentration from the residual modeling can be used as a means, combined with process control techniques, to evaluate the performance of the established calibration model. Several modeling techniques are used for residual modeling, including PLS, support vector regression (SVR), a hybrid method, PLS-aided SVR, and an amplified version of the hybrid, amplified PLS-aided SVR. Through this work, a calibration updating strategy is developed which provides an effective way to monitor the established calibration model.

Download or read book Infrared Thermography written by Raghu Prakash and published by BoD – Books on Demand. This book was released on 2012-03-14 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Infrared Thermography (IRT) is commonly as a NDE tool to identify damages and provide remedial action. The fields of application are vast, such as, materials science, life sciences and applied engineering. This book offers a collection of ten chapters with three major sections - relating to application of infrared thermography to study problems in materials science, agriculture, veterinary and sports fields as well as in engineering applications. Both mathematical modeling and experimental aspects of IRT are evenly discussed in this book. It is our sincere hope that the book meets the requirements of researchers in the domain and inspires more researchers to study IRT.

Download or read book International Conference on Oriental Thinking and Fuzzy Logic written by Bing-Yuan Cao and published by Springer. This book was released on 2016-06-18 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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".

Download or read book The First IEEE Conference on Control Applications written by and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 1992 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hyperspectral Remote Sensing written by Prem Chandra Pandey and published by Elsevier. This book was released on 2020-08-05 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection Provides an overview of the state-of-the-art, including algorithms, techniques and case studies

Download or read book Characterization and Analysis of Microplastics written by and published by Elsevier. This book was released on 2017-03-19 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Characterization and Analysis of Microplastics, Volume 75 presents the latest information on new and published analytical methodologies for the identification and quantification of microplastics. This series focuses on a variety of interesting topics surrounding the field of microplastics, with this new release in the series covering sampling and sample handing, the characterization of microplastics by raman spectroscopy, and techniques for assessing the chemical compounds related to microplastics. Users will find a variety of useful information that includes morphological, physical and chemical characterizations, along with analytical techniques and future perspectives of analytical methodologies in this rapidly advancing field. Concise, comprehensive coverage of analytical techniques and applications Clear diagrams adequately support important topics Includes real examples that illustrate applications of the analytical techniques on the sampling, characterization, and analysis of microplastics

Download or read book Computational Vision and Bio Inspired Computing written by S. Smys and published by Springer Nature. This book was released on 2022-03-30 with total page 877 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers from the 5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2021), held in Coimbatore, India, during November 25–26, 2021. This book presents state-of-the-art research innovations in computational vision and bio-inspired techniques. The book reveals the theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary optimization and big data modeling and management that make use of effectual computing processes in the bio-inspired systems. It also contributes to the novel research that focuses on developing bio-inspired computing solutions for various domains, such as human–computer interaction, image processing, sensor-based single processing, recommender systems and facial recognition, which play an indispensable part in smart agriculture, smart city, biomedical and business intelligence applications.

Download or read book Histopathological Image Analysis written by Gurcan and published by Wiley-Blackwell. This book was released on with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: