الصفحة 1
الصفحة 1
Nonlinear kalman filtering for force-controlled robot tasks
This monograph focuses on how to achieve more robot autonomy by means of reliable processing skills. "Nonlinear Kalman Filtering for Force-Controlled Robot Tasks " discusses the latest developments in the areas of contact modeling, nonlinear parameter estimation and task plan optimization for improved estimation accuracy.
Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems : From Analytical to Soft Computing Approaches
This monograph presents a variety of techniques that can be used for designing robust fault diagnosis schemes for non-linear systems. The introductory part of the book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. Subsequently, advanced robust observer structures are presented. Parameter estimation based techniques are discussed as well. A particular attention is drawn to experimental design for fault diagnosis. The book also presents a number of robust soft computing approaches utilizing evolutionary algorithms and neural networks. All approaches described in this book are illustrated by practical applications.
Methods of Celestial Mechanics: Vol. I: Physical, Mathematical, and Numerical Principles
G. Beutler's Methods of Celestial Mechanics is a coherent textbook for students in physics, mathematics and engineering as well as an excellent reference for practitioners. This Volume I gives a thorough treatment of celestial mechanics and presents all the necessary mathematical details that a professional would need. After a brief review of the history of celestial mechanics, the equations of motion (Newtonian and relativistic versions) are developed for planetary systems (N-body-problem), for artificial Earth satellites, and for extended bodies (which includes the problem of Earth and lunar rotation). Perturbation theory is outlined in an elementary way from generally known mathematical principles without making use of the advanced tools of analytical mechanics. The variational equations associated with orbital motion - of fundamental importance for parameter estimation (e.g., orbit determination), numerical error propagation, and stability considerations - are introduced and their properties discussed in considerable detail. Numerical methods, especially for orbit determination and orbit improvement, are discussed in considerable depth. The algorithms may be easily applied to objects of the planetary system and to Earth satellites and space debris.
Inference in Hidden Markov Models
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states.In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models.
Fault-Diagnosis Systems : An Introduction from Fault Detection to Fault Tolerance
This book gives an introduction into the field of fault detection, fault diagnosis and fault-tolerant systems with methods which have proven their performance in practical applications. It guides the reader in a structured tutorial style: supervision methods, reliability, safety, system integrity and related terminology; fault detection with signal-based methods for periodic and stochastic signals; fault detection with process model-based methods like parameter estimation, state estimation, parity equations and principal component analysis; fault diagnosis with classification and inference methods; fault-tolerant systems with hardware and analytical redundancy; many practical simulation examples and experimental results for processes like electrical motors, pumps, actuators, sensors and automotive components; end-of-chapter exercises for self testing or for practice.
Data assimilation fundamentals : A unified formulation of the state and parameter estimation problem
This textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method.
Data Assimilation : The Ensemble Kalman Filter
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples.It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page.The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Automotive Control Systems : For Engine, Driveline, and Vehicle
Reflecting the trend to optimization through integrative approaches for engine, driveline and vehicle control, this book enables control engineers to understand engine and vehicle models necessary for controller design and also introduces mechanical engineers to vehicle-specific signal processing and automatic control. The emphasis on measurement, comparisons between performance and modelling, and realistic examples derive from the authors’ industrial experience at Bosch and interactions within IFAC and SAE. The second edition offers new or expanded topics such as diesel-engine modelling, diagnosis and anti-jerking control, and vehicle modelling and parameter estimation. The book addresses professional engineers as well as students.
Advances in radar systems for target detection and tracking
Radar systems can provide the all-weather and all-time detection and tracking of targets of interest, and they have been extensively applied by the remote sensing community, in applications such as geological exploration, disaster forecasting, traffic monitoring, urban planning, environmental sciences, hydrology, littoral zones, oceans, etc. This reprint contains the several advance research studies on radar systems for target detection and tracking. It includes multipath ghost suppression, maneuvering target tracking, target detection, and other topics.
Advanced artificial intelligence models and its applications
The field of artificial intelligence (AI) has undergone enormous expansion since its inception in the mid-20th century, as demonstrated by its application across an array of engineering and scientific challenges. Particularly in the last decade, AI has witnessed a significant breakthrough with the advent of deep learning, which has facilitated the employment of various AI models across a multitude of domains. This reprint features ten papers accepted for publication in the Special Issue titled "Advanced Artificial Intelligence Models and Their Applications," published in the MDPI Mathematics journal. These papers explore numerous facets of advanced artificial intelligence models and their applications, covering areas such as cybersecurity, image classification, logistics optimization, automatic music generation, human capital investment, writer recognition, remote sensing image indexing, target tracking, and more.
Accelerator-Driven System at Kyoto University Critical Assembly
This book is a unique compilation of experimental benchmark analyses of the accelerator-driven system (ADS) at the Kyoto University Critical Assembly (KUCA) on the most recent advances in the development of computational methods.
