2023 International Conference on Marine Equipment & Technology and Sustainable Development
2023 International Conference on Marine Equipment & Technology and Sustainable Development

Author: Desen Yang

Publisher: Springer Nature

Published: 2023-08-01

Total Pages: 1258

ISBN-13: 9819942918

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This book contains original, peer-reviewed, and selected research papers that were presented at the 2023 International Conference on Marine Equipment & Technology and Sustainable Development, which took place in Beijing, China on April 1st 2023. The papers cover a range of topics, including but not limited to: the vision and goals of building a maritime community with a shared future, marine machinery and transportation, marine ecology, environmental protection and conservation, marine safety, future ships and marine equipment, marine engineering, marine information and technology, maritime policy, and global governance. The papers included in this volume provide the latest findings on methodologies, algorithms, and applications in marine equipment and technology, as well as sustainable development. As a result, this book is an invaluable resource for researchers, engineers, and university students who are interested in these fields.

Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry

Author: Eduardo F. Camacho

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 250

ISBN-13: 1447130081

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Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Encyclopedia of Artificial Intelligence
Encyclopedia of Artificial Intelligence

Author: Juan Ramon Rabunal

Publisher: IGI Global

Published: 2009-01-01

Total Pages: 1640

ISBN-13: 1599048507

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"This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.

Intelligent Electronics and Circuits
Intelligent Electronics and Circuits

Author: Mingbo Niu

Publisher: BoD – Books on Demand

Published: 2022-09-28

Total Pages: 174

ISBN-13: 1803550007

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Intelligent electronics could shape future smart cities and promote initiatives on exploring brand-new integrated circuits, high-effective intelligent reconfigurable surfaces, nondestructive evaluation, terahertz (THz), ITS, 6G, medical and safety imaging, and signal filtering. This book presents mainstream principles, circuitry architectures, and a development roadmap for intelligent electronic systems. Its content ranges from theoretical basis to materials characteristics, and from featured advances to practical applications.

State Estimation for Robotics
State Estimation for Robotics

Author: Timothy D. Barfoot

Publisher: Cambridge University Press

Published: 2017-07-31

Total Pages: 381

ISBN-13: 1107159393

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A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.

Modelling and Control of Dynamic Systems Using Gaussian Process Models
Modelling and Control of Dynamic Systems Using Gaussian Process Models

Author: Juš Kocijan

Publisher: Springer

Published: 2015-11-21

Total Pages: 281

ISBN-13: 3319210211

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This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Applied Predictive Modeling
Applied Predictive Modeling

Author: Max Kuhn

Publisher: Springer Science & Business Media

Published: 2013-05-17

Total Pages: 595

ISBN-13: 1461468493

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Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.