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Uncertainty and Artificial Intelligence: Additive Manufacturing, Vibratory Control, Agro-composite, Mechatronics
by Abdelkhalak El HamiToday's information technology, along with Artificial Intelligence (AI), is moving towards total communication between all computerized systems. AI is a representation of human intelligence based on the creation and application of algorithms in specific computer environments. Its aim is to enable computers to act like human beings. For it to work, this type of technology requires computer systems, data with management systems and advanced algorithms, used by AI. In mechanical engineering, AI can offer many possibilities: in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration control and agro composites, among many others. This book is dedicated to Artificial Intelligence uncertainties in mechanical problems. Each chapter clearly sets out used and developed illustrative examples. Aimed at students, Uncertainty and Artificial Intelligence is also a valuable resource for practicing engineers and research lecturers.
Uncertainty and Artificial Intelligence: Additive Manufacturing, Vibratory Control, Agro-composite, Mechatronics
by Abdelkhalak El HamiToday's information technology, along with Artificial Intelligence (AI), is moving towards total communication between all computerized systems. AI is a representation of human intelligence based on the creation and application of algorithms in specific computer environments. Its aim is to enable computers to act like human beings. For it to work, this type of technology requires computer systems, data with management systems and advanced algorithms, used by AI. In mechanical engineering, AI can offer many possibilities: in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration control and agro composites, among many others. This book is dedicated to Artificial Intelligence uncertainties in mechanical problems. Each chapter clearly sets out used and developed illustrative examples. Aimed at students, Uncertainty and Artificial Intelligence is also a valuable resource for practicing engineers and research lecturers.
Uncertainty and Imprecision in Decision Making and Decision Support: Selected papers from BOS-2020, held on December 14-15, 2020, and IWIFSGN-2020, held on December 10-11, 2020 in Warsaw, Poland (Lecture Notes in Networks and Systems #338)
by Krassimir T. Atanassov Vassia Atanassova Janusz Kacprzyk Andrzej Kałuszko Maciej Krawczak Jan W. Owsiński Sotir S. Sotirov Evdokia Sotirova Eulalia Szmidt Sławomir ZadrożnyThis book is composed of selected papers from the Sixteenth National Conference on Operational and Systems Research, BOS-2020, held on December 14-15, 2020, one of premiere conferences in the field of operational and systems research. The second is the Nineteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2020, held on December 10-11, 2020, in Warsaw, Poland, in turn—one of premiere conferences on fuzzy logic, notably on extensions of the traditional fuzzy sets, also comprising a considerable part on the generalized nets (GNs), an important extension of the traditional Petri nets. A joint publication of selected papers from the two conferences follows a long tradition of such a joint organization and—from a substantial point of view—combines systems modeling, systems analysis, broadly perceived operational research, notably optimization, decision making, and decision support, with various aspects of uncertain and imprecise information and their related tools and techniques.
Uncertainty and Imprecision in Decision Making and Decision Support: Selected Papers from BOS-2016 and IWIFSGN-2016 held on October 12-14, 2016 in Warsaw, Poland (Advances in Intelligent Systems and Computing #559)
by Krassimir T. Atanassov Janusz Kacprzyk Andrzej Kałuszko Maciej Krawczak Jan Owsiński Sotir Sotirov Evdokia Sotirova Eulalia Szmidt Sławomir ZadrożnyThis book presents selected papers from two important conferences held on October 12–14, 2016 in Warsaw, Poland: the Fourteenth National Conference of Operational and Systems Research, BOS-2016, one of the premiere conferences in the field of operational and systems research not only in Poland but also at the European level; and the Fifteenth International Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN-2016, one of the foremost conferences on fuzzy logic, notably addressing extensions of the traditional fuzzy sets, as well as the Generalized Nets (GNs), a powerful extension of the traditional Petri net paradigm. The scope of the BOS conferences includes all types of problems related to systems modeling, systems analysis, broadly perceived operational research, optimization, decision making, and decision support, to name but a few. In all these areas, virtually all models used have to take into account not only uncertainty in its traditional sense, but also imprecision of information. That is, in addition to traditional probabilistic and statistical tools and techniques, the use of methods based on fuzzy sets can also be sensible. Even more so, employing certain extensions of the classic concept of a fuzzy set can be very useful. Applying intuitionistic fuzzy sets, which are at the core of the IWIFSGN conferences, is a good example. Both conferences, BOS-2016 and IWIFSGN-2016, offered ideal venues for the exchange of ideas, cross-fertilization, and mutual inspiration.
Uncertainty and Imprecision in Decision Making and Decision Support - New Advances, Challenges, and Perspectives: Selected Papers from BOS/SOR-2022, Held on October 13-15, 2022, and IWIFSGN-2022, Held on October 13-14, 2022, in Warsaw, Poland (Lecture Notes in Networks and Systems #793)
by Krassimir T. Atanassov Vassia Atanassova Janusz Kacprzyk Andrzej Kałuszko Maciej Krawczak Jan W. Owsiński Sotir S. Sotirov Evdokia Sotirova Eulalia Szmidt Sławomir ZadrożnyThis volume is composed of selected papers from two conferences held in Warsaw, Poland on October 13-15, 2022: the BOS/SOR’2022 - National Conference on Operational and Systems Research, one of premiere conferences in the field of operational and systems research, and the Twentith International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN-2022, one of premiere conferences on fuzzy logic, notably on extensions of the traditional fuzzy sets, also comprising a considerable part on the Generalized Nets (GNs). A joint publication of selected papers from the two conferences follows a long tradition of such a joint organization, and – from a substantial point of view – combines systems modeling, systems analysis, broadly perceived operational research, notably optimization, decision making and decision support, with various aspects of uncertain and imprecise information and their related tools and techniques.
Uncertainty and Imprecision in Decision Making and Decision Support: Selected Papers from BOS-2018, held on September 24-26, 2018, and IWIFSGN-2018, held on September 27-28, 2018 in Warsaw, Poland (Advances in Intelligent Systems and Computing #1081)
by Krassimir T. Atanassov Vassia Atanassova Janusz Kacprzyk Andrzej Kaluszko Maciej Krawczak Jan W. Owsinski Sotir Sotirov Evdokia Sotirova Eulalia Szmidt Slawomir ZadroznyThis book gathers selected papers from two important conferences held on October 24–28, 2018, in Warsaw, Poland: theFifteenth National Conference of Operational and Systems Research, BOS-2018, one of the leading conferences in the field of operational and systems research not only in Poland but also at the European level; andthe Seventeenth International Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN-2018, one of thepremiere conferences on fuzzy logic.The papers presented here constitute a fair and comprehensive representation of the topics covered by both BOS-2018 and IWIFSGN-2018, includingextensions of the traditional fuzzy sets, in particular on the intuitionistic fuzzy sets, as well as other topics in uncertainty and imprecision modeling, the Generalized Nets (GNs), a powerful extension of the traditional Petri net paradigm, and InterCriteria Analysis, a new method for feature selection and analyses in multicriteria and multi-attribute decision-making problems. The Workshop was dedicated to the memory of Professor Beloslav Riečan (1936–2018), a regular participant at the IWIFSGN workshops.
Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling (Interdisciplinary Contributions to Archaeology #0)
by Marieka Brouwer Burg Hans Peeters William A. LovisThis volume deals with the pressing issue of uncertainty in archaeological modeling. Detecting where and when uncertainty is introduced to the modeling process is critical, as are strategies for minimizing, reconciling, or accommodating such uncertainty. Included chapters provide unique perspectives on uncertainty in archaeological modeling, ranging in both theoretical and methodological orientation. The strengths and weaknesses of various identification and mitigation techniques are discussed, in particular sensitivity analysis. The chapters demonstrate that for archaeological modeling purposes, there is no quick fix for uncertainty; indeed, each archaeological model requires intensive consideration of uncertainty and specific applications for calibration and validation. As very few such techniques have been problematized in a systematic manner or published in the archaeological literature, this volume aims to provide guidance and direction to other modelers in the field by distilling some basic principles for model testing derived from insight gathered in the case studies presented. Additionally, model applications and their attendant uncertainties are presented from distinct spatio-temporal contexts and will appeal to a broad range of archaeological modelers. This volume will also be of interest to non-modeling archaeologists, as consideration of uncertainty when interpreting the archaeological record is also a vital concern for the development of non-formal (or implicit) models of human behavior in the past.
Uncertainty and Vagueness in Knowledge Based Systems: Numerical Methods (Artificial Intelligence)
by Rudolf Kruse Erhard Schwecke Jochen HeinsohnThe primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig.
Uncertainty Approaches for Spatial Data Modeling and Processing: A decision support perspective (Studies in Computational Intelligence #271)
by Frederick E. Petry Adnan YaziciWe are facing an immense growth of digital data and information resources, both in terms of size, complexity, modalities and intrusiveness. Almost every aspect of our existence is being digitally captured. This is exemplified by the omnipresent existence of all kinds of data storage, far beyond those stored in traditional relational databases. The spectrum of data being digitally stored runs from multimedia data repositories to your purchases in most stores. Every tweet that you broadcast is captured for posterity. Needless to say this situation posses new research opportunities, challenges and problems in the ways we store, manipulate, search, and - in general - make use of such data and information. Attempts to cope with these problems have been emerging all over the world with thousands of people devoted to developing tools and techniques to deal with this new area of research. One of the prominent scholars and researchers in this field was the late Professor Ashley Morris who died suddenly and tragically at a young age. Ashley's career begun in industry, where he specialized in databases.
Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty (Springer Theses)
by Vassilis M. CharitopoulosThis book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty.
Uncertainty-Based Information: Elements of Generalized Information Theory (Studies in Fuzziness and Soft Computing #15)
by George J. Klir Mark J. WiermanInformation is precious. It reduces our uncertainty in making decisions. Knowledge about the outcome of an uncertain event gives the possessor an advantage. It changes the course of lives, nations, and history itself. Information is the food of Maxwell's demon. His power comes from know ing which particles are hot and which particles are cold. His existence was paradoxical to classical physics and only the realization that information too was a source of power led to his taming. Information has recently become a commodity, traded and sold like or ange juice or hog bellies. Colleges give degrees in information science and information management. Technology of the computer age has provided access to information in overwhelming quantity. Information has become something worth studying in its own right. The purpose of this volume is to introduce key developments and results in the area of generalized information theory, a theory that deals with uncertainty-based information within mathematical frameworks that are broader than classical set theory and probability theory. The volume is organized as follows.
Uncertainty Data in Interval-Valued Fuzzy Set Theory: Properties, Algorithms and Applications (Studies in Fuzziness and Soft Computing #367)
by Barbara PękalaThis book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov’s intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information. The theory and application of interval-values fuzzy sets to various decision making problems represent the central core of this book, which describes in detail aggregation operators and their use with imprecise data represented as intervals. Interval-valued fuzzy relations, compatibility measures of interval and the transitivity property are thoroughly covered. With its good balance between theoretical considerations and applications of originally developed algorithms to real-world problem, the book offers a timely, inspiring guide to mathematicians and engineers developing new decision making models or implementing/applying existing ones to a wide range of applications involving imprecise or incomplete data.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 5th International Workshop, UNSURE 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings (Lecture Notes in Computer Science #14291)
by Carole H. Sudre Christian F. Baumgartner Adrian Dalca Raghav Mehta Chen Qin William M. WellsThis book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings (Lecture Notes in Computer Science #13563)
by Carole H. Sudre Christian F. Baumgartner Adrian Dalca Chen Qin Ryutaro Tanno Koen Van Leemput William M. Wells IIIThis book constitutes the refereed proceedings of the Fourth Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with MICCAI 2022. The conference was hybrid event held from Singapore. For this workshop, 13 papers from 22 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures: First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (Lecture Notes in Computer Science #11840)
by Hayit Greenspan Ryutaro Tanno Marius Erdt Tal Arbel Christian Baumgartner Adrian Dalca Carole H. Sudre William M. Wells Klaus Drechsler Marius George Linguraru Cristina Oyarzun Laura Raj Shekhar Stefan Wesarg Miguel Ángel González BallesterThis book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings (Lecture Notes in Computer Science #12443)
by Tal Arbel Enzo Ferrante Sarah Parisot Aristeidis Sotiras Adrian Dalca Bartlomiej Papiez William M. Wells Ryutaro Tanno Carole H. Sudre Hamid Fehri Christian F. Baumgartner Koen Van LeemputThis book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic.For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis: 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (Lecture Notes in Computer Science #12959)
by Carole H. Sudre Roxane Licandro Christian Baumgartner Andrew Melbourne Adrian Dalca Jana Hutter Ryutaro Tanno Esra Abaci Turk Koen Van Leemput Jordina Torrents Barrena William M. Wells Christopher MacgowanThis book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.
Uncertainty Handling and Quality Assessment in Data Mining (Advanced Information and Knowledge Processing)
by Michalis Vazirgiannis Maria Halkidi Dimitrious GunopulosThe recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, UCLA, at Los Angeles, July 13-15, 1991
by Bruce D'Ambrosio Philippe Smets Piero Patrone BonissoneUncertainty Proceedings 1991
Uncertainty in Artificial Intelligence (ISSN #Volume 4)
by L. N. Kanal J. F. LemmerHow to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle, July 29-31, 1994
by MKPUncertainty Proceedings 1994
Uncertainty in Artificial Intelligence 2 (ISSN #Volume 5)
by L. N. Kanal J. F. LemmerThis second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.
Uncertainty in Artificial Intelligence 4 (ISSN #Volume 9)
by T. S. Levitt L. N. Kanal J. F. Lemmer R. D. ShachterClearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.
Uncertainty in Artificial Intelligence 5 (ISSN #Volume 10)
by M. Henrion J. F. Lemmer R. D. Shachter L. N. KanalThis volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language understanding.
Uncertainty in Biology: A Computational Modeling Approach (Studies in Mechanobiology, Tissue Engineering and Biomaterials #17)
by Liesbet Geris David Gomez-CabreroComputational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.