Название: Machine Learning for Complex and Unmanned Systems
Автор: Jоsе Маrtinеz-Саrrаnzа, Еvеrаrdо Inzunzа-Gоnzаlеz, Еnriquе Еfrеn Gаrсіа-Guеrrеrо
Издательство: CRC Press
Год: 2024
Страниц: 386
Язык: английский
Формат: pdf (true)
Размер: 25.4 MB

This book highlights applications that include Machine Learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects Machine Learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of Machine Learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of Artificial Intelligence (AI), cryptography, embedded hardware, electronics, the Internet of Things (IoT), and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.

Название: Multilevel Modeling Using R, 3rd Edition
Автор: W. Ноlmеs Finсh, Jосеlуn Е. Воlin
Издательство: Springer
Год: 2024
Страниц: 339
Язык: английский
Формат: pdf (true)
Размер: 10.9 MB

Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data. The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The third edition also includes new sections in Chapter 11 describing two useful alternatives to standard multilevel models, fixed effects models and generalized estimating equations.

Название: Methods and Applications of Autonomous Experimentation
Автор: Маrсus М. Nоасk, Dаniеlа Ushizimа
Издательство: CRC Press
Серия: Chapman & Hall/CRC Computational Science
Год: 2024
Страниц: 445
Язык: английский
Формат: pdf (true)
Размер: 41.3 MB

Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Just like so many other topics that have been adopted into the realm of Machine Learning and AI—Deep Learning, digital twins, active learning, and so on—Autonomous Experimentation has become a fuzzy, ill-defined ideal everyone wants, but seemingly no one can deliver. The main reason for that is the missing, inherent meaning of the term “Autonomous Experimentation”. In this book, we want to separate the practical methods and applications from the buzz and hype surrounding the term. Autonomous Experimentation (AE) is an emerging paradigm for accelerating scientific discovery, leveraging Artificial Intelligence and Machine Learning methods to automate the entire experimental loop, including the decision-making step. AE combines advancements in hardware automation, data analytics, modeling, and active learning to augment a scientific instrument, enabling it to autonomously explore the search space corresponding to a problem of interest.

Название: Models for Multi-State Survival data: Rates, Risks, and Pseudo-Values
Автор: Реr Кrаgh Аndеrsеn, Неnrik Rаvn
Издательство: CRC Press
Серия: Texts in Statistical Science Series
Год: 2023
Страниц: 293
Язык: английский
Формат: pdf (true)
Размер: 11.0 MB

Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and public health. The book includes mathematical detail which can be skipped by readers more interested in the practical examples. It is aimed at biostatisticians and at readers with an interest in the topic having a more applied background, such as epidemiology. This book builds on several courses the authors have taught on the subject. Key · Intensity-based and marginal models. · Survival data, competing risks, illness-death models, recurrent events. · Includes a full chapter on pseudo-values. · Intuitive introductions and mathematical details. · Practical examples of event history data. · Exercises. Software code in R and SAS and the data used in the book can be found on the book’s webpage.

Название: Model-Based Machine Learning
Автор: Jоhn Winn
Издательство: CRC Press
Год: 2024
Страниц: 428
Язык: английский
Формат: pdf (true)
Размер: 30.8 MB

Today, Machine Learning (ML) is being applied to a growing variety of problems in a bewildering variety of domains. When doing machine learning, a fundamental challenge is connecting the abstract mathematics of a particular Machine Learning technique to a concrete, real-world problem. This book tackles this challenge through model-based Machine Learning. Model-based Machine Learning is an approach which focuses on understanding the assumptions encoded in a ML system, and their corresponding impact on the behaviour of the system. The practice of model-based ML involves separating out these assumptions being made about a real-world situation from the detailed mathematics of the algorithms needed to do the ML. This approach makes it easier to both understand the behaviour of a ML system and to communicate this to others.

Название: Machine Learning in Python for Dynamic Process Systems: A practitioner’s guide for building process modeling, predictive, and monitoring solutions using dynamic data
Автор: Ankur Kumar, Jesus Flores-Cerrillo
Издательство: Leanpub
Год: June 2023
Страниц: 208
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book provides a comprehensive coverage of Machine Learning (ML) methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show (using Python) how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods. This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. No prior experience with Machine Learning or Python is needed. Undergraduate-level knowledge of basic linear algebra and calculus is assumed.

Название: Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning
Автор: Robert Stevens
Издательство: CRC Press
Год: 2024
Страниц: 99
Язык: английский
Формат: pdf (true), djvu
Размер: 10.2 MB

Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can “meet you where you are” by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations. Data scientists speak of their workflow in terms of a pipeline. In fact, many modern software tools for data science build upon this concept to manage the tasks associated with building models. There is a myriad of ways pipelines are put forth, and no one scheme rules them all. Python is a broad programming language with strengths in manipulating data. A large variety of “libraries” are available that are purpose-driven, such as implementing a specific Machine Learning algorithm or a collection of algorithms.

Название: Modeling Mindsets: The Many Cultures Of Learning From Data
Автор: Christoph Molnar
Издательство: Mucbook
Год: 2022
Страниц: 113
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB

In less than 100 pages, Modeling Mindsets elucidates the worldviews behind various statistical modeling and Machine Learning mindsets. Most books on modeling dive straight into the math and methodologies, leaving readers struggling to grasp the underlying assumptions and limitations. Written in a clear and concise style, Modeling Mindsets introduces approaches such as Bayesian inference, supervised learning, causal inference, and more. With this book, you'll gain a deeper understanding of different modeling techniques, empowering you to choose the right one for your problem. Machine Learning (ML) is the branch of Artificial Intelligence (AI) that deals with improving at a given task through “experience,” which means learning from data.

Название: Introduction to Modeling and Simulation: A Systems Approach
Автор: Mark W. Spong
Издательство: Wiley
Год: 2023
Страниц: 417
Язык: английский
Формат: epub (true)
Размер: 31.3 MB

An essential introduction to engineering system modeling and simulation from a well-trusted source in engineering and education. This new introductory-level textbook provides thirteen self-contained chapters, each covering an important topic in engineering systems modeling and simulation. The importance of such a topic cannot be overstated; modeling and simulation will only increase in importance in the future as computational resources improve and become more powerful and accessible, and as systems become more complex. This resource is a wonderful mix of practical examples, theoretical concepts, and experimental sessions that ensure a well-rounded education on the topic. It may also be of interest to those in mathematical modeling courses, as it provides in-depth material on MATLAB simulation and contains appendices with brief reviews of linear algebra, real analysis, and probability theory.

Название: Creo Parametric Modeling with Augmented Reality
Автор: Ulan Dakeev
Издательство: Wiley
Год: 2023
Страниц: 299
Язык: английский
Формат: pdf (true)
Размер: 49.2 MB

Creo Parametric Modeling with Augmented Reality Tutorial-based introduction to 3D Modeling with Creo Parametric, including images to be scanned and viewed using an AR mobile app. Using a tutorial approach, Creo Parametric Modeling with Augmented Reality provides an introduction to the modeling techniques and functionality of Creo Parametric, beginning with an overview of parametric design and Creo’s sketching capabilities and 3D tools; proceeding through design methods and skills related to patterns, dimensions, sections, assemblies, and tolerances and GD&T; and concluding by connecting Creo’s capabilities to the more specialized skills of Finite Element Analysis, mechanism animation, and sheet metal design.