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Digital Human Modeling ; 1st International Conference, ICDHM 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings
Theis book includes human aspects of design and use of computing systems.It covers the entire field of Human-Computer Interaction, addressing major - vances in knowledge and effective use of computers in a variety of application areas. It also contains thematic area of Digital Human Modeling, Shape and Movement Modeling and Anthropometry, Building and Applying Virtual Humans, Medical and Rehabilitation Applications, Industrial and Ergonomic Applications
Digital darwinism : Surviving the new age of business disruption
This book guides you through the unrelenting pace of change and uncertainty facing business leaders today. Currently in a hybrid world where digital and real-world experiences collide and are expected to seamlessly blend into one another, never has the need to be on top of your digital transformation been felt more strongly.
Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control
This book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area. The main idea is that these two areas together can help solve various control problems and to find better results. In this book, the authors test the proposed method using five benchmark control problems. First, the water tank, temperature, mobile robot, and inverted pendulum controllers are considered. For these 4 problems, experimentation was carried out using a Type-1 fuzzy system and an Interval Type-2 system. The last control problem was the D.C. motor, for which the experiments were performed with Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results obtained in each of the controllers are better and with increasing uncertainty, the results are even better. For this reason, the authors consider in this book the proposed method using fuzzy systems and the differential evolution algorithm to improve the fuzzy controllers’ behavior in complex control problems.
Dependability Modelling under Uncertainty : An Imprecise Probabilistic Approach
Mechatronic design processes have become shorter and more parallelized, induced by growing time-to-market pressure. Methods that enable quantitative analysis in early design stages are required, should dependability analyses aim to influence the design. Due to the limited amount of data in this phase, the level of uncertainty is high and explicit modeling of these uncertainties becomes necessary. This work introduces new uncertainty-preserving dependability methods for early design stages.
Demystifying Climate Models : A Users Guide to Earth System Models
This book demystifies the models we use to simulate present and future climates, allowing readers to better understand how to use climate model results. In order to predict the future trajectory of the Earth’s climate, climate-system simulation models are necessary. When and how do we trust climate model predictions? The book offers a framework for answering this question. It provides readers with a basic primer on climate and climate change, and offers non-technical explanations for how climate models are constructed, why they are uncertain, and what level of confidence we should place in them. It presents current results and the key uncertainties concerning them. Uncertainty is not a weakness but understanding uncertainty is a strength and a key part of using any model, including climate models.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Decision Making under Deep Uncertainty : From Theory to Practice
Focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them.
Dealing with Uncertainties : A Guide to Error Analysis
Dealing with Uncertainties proposes and explains a new approach for the analysis of uncertainties. Firstly, it is shown that uncertainties are the consequence of modern science rather than of measurements. Secondly, it stresses the importance of the deductive approach to uncertainties.
Database Theory – ICDT 2007 ; 11th International Conference, Barcelona, Spain, January 10-12, 2007, Proceedings
The papers are organized in topical sections on information integration and peer to peer, axiomatizations for XML, expressive power of query languages, incompleteness, inconsistency, and uncertainty, XML schemas and typechecking, stream processing and sequential query processing, ranking, XML update and query, as well as query containment.
Critique de la valeur fondamentale = Critique of fundamental value
This work takes stock of the different conceptions of fundamental value in finance, the methods of its calculation and the ongoing debates in financial theory as in professional practices. The book reports on the alternatives offered by mathematical modeling.
Coping with Uncertainty : Modeling and Policy Issues
Ongoing global changes bring fundamentally new scientific problems requiring new concepts and tools. A key issue concerns a vast variety of practically irreducible uncertainties, which challenge our traditional models and require new concepts and analytical tools. The complexity of new problems does not allow to achieve enough certainty by increasing the resolution of models or by bringing in more links. Hence, new tools for modeling and management of uncertainty are needed, as given in this book.
Control Systems Theory and Applications for Linear Repetitive Processes
After motivating examples, this monograph gives substantial new results on the analysis and control of linear repetitive processes. These include further applications of the abstract model based stability theory which, in particular, shows the critical importance to the dynamics developed of the structure of the initial conditions at the start of each new pass, the development of stability tests and performance bounds in terms of so-called 1D and 2D Lyapunov equations. It presents the development of a major bank of results on the structure and design of control laws, including the case when there is uncertainty in the process model description, together with numerically reliable computational algorithms. Finally, the application of some of these results in the area of iterative learning control is treated --- including experimental results from a chain conveyor system and a gantry robot system.
Control of Uncertain Systems : Modelling, Approximation, and Design; A Workshop on the Occasion of Keith Glover's 60th Birthday
This Festschrift contains a collection of articles by friends, co-authors, colleagues, and former Ph.D. students of Keith Glover, Professor of Engineering at the University of Cambridge, on the occasion of his sixtieth birthday. Professor Glover's scientific work spans a wide variety of topics, the main themes being system identification, model reduction and approximation, robust controller synthesis, and control of aircraft and engines. The articles in this volume are a tribute to Professor Glover's seminal work in these areas.
Control of nonlinear and hybrid process systems : Designs for uncertainty, constraints and time-delays
The book includes many detailed examples which can be easily modified by a control engineer to be tailored to a specific application. This book is useful for researchers in control systems theory, graduate students pursuing their degree in control systems and control engineers.
Conditionals, Information, and Inference
Conditionals are fascinating and versatile objects of knowledge representation. On the one hand, they may express rules in a very general sense, representing, for example, plausible relationships, physical laws, and social norms. On the other hand, as default rules or general implications, they constitute a basic tool for reasoning, even in the presence of uncertainty. In this sense, conditionals are intimately connected both to information and inference. Due to their non-Boolean nature, however, conditionals are not easily dealt with. They are not simply true or false — rather, a conditional “if A then B” provides a context, A, for B to be plausible (or true) and must not be confused with “A entails B” or with the material implication “not A or B.” This ill- trates how conditionals represent information, understood in its strict sense as reduction of uncertainty. To learn that, in the context A, the proposition B is plausible, may reduce uncertainty about B and hence is information. The ab- ity to predict such conditioned propositions is knowledge and as such (earlier) acquired information. The ?rst work on conditional objects dates back to Boole in the 19th c- tury, and the interest in conditionals was revived in the second half of the 20th century, when the emerging Arti?cial Intelligence made claims for appropriate formaltoolstohandle“generalizedrules.”Sincethen,conditionalshavebeenthe topic of countless publications, each emphasizing their relevance for knowledge representation, plausible reasoning, nonmonotonic inference, and belief revision.
Computational intelligence in information assurance and security
The global economic infrastructure is becoming increasingly dependent upon information technology, with computer and communication technology being essential and vital components of Government facilities, power plant systems, medical infrastructures, financial centers and military installations to name a few. Finding effective ways to protect information systems, networks and sensitive data within the critical information infrastructure is challenging even with the most advanced technology and trained professionals. This volume provides the academic and industrial community with a medium for presenting original research and applications related to information assurance and security using computational intelligence techniques. The included chapters communicate current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems using computational intelligence.
Computational intelligence for engineering and manufacturing
Unlike traditional computing, Computational Intelligence is tolerant of imprecise information, partial truth and uncertainty. This book presents a collection of contributions on a focused treatment of important elements of CI, centred on its key element: learning.
Cognitive Vision ; 4th International Workshop, ICVW 2008, Santorini, Greece, May 12, 2008, Revised Selected Papers
This volume constitutes the post-conference proceedings of the 4th International Cognitive Vision Workshop, ICVW 2008, held in Santorini, Greece, on May 12, 2008.
Mathematical Methods in Robust Control of Linear Stochastic Systems
Linear stochastic systems are successfully used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. This monograph presents a useful methodology for the control of such stochastic systems with a focus on robust stabilization in the mean square, linear quadratic control, the disturbance attenuation problem, and robust stabilization with respect to dynamic and parametric uncertainty.
Managing Weather and Climate Risks in Agriculture
In many parts of the world, weather and climate are one of the biggest production risks and uncertainty factors impacting on agricultural systems performance and management. Both structural and non-structural measures can be used to reduce the impacts of the variability (including extremes) of climate resources on crop production. While the structural measures include strategies such as irrigation, water harvesting, windbreaks etc., the non-structural measures include use of seasonal to interannual climate forecasts, improved application of medium-range weather forecasts and crop insurance. This book based on an International Workshop held in New Delhi, India should be of interest to all organizations and agencies interested in improved risk management in agriculture.
