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Based on the first Workshop for Women in Computational Topology that took place in 2016, this volume assembles new research and applications in computational topology. Featured articles range over the breadth of the discipline, including topics such as surface reconstruction, topological data analysis, persistent homology, algorithms, and surface-embedded graphs. Applications in graphics, medical imaging, and GIS are discussed throughout the book. Four of the papers in this volume are the product of working groups that were established and developed during the workshop. Additional papers were also solicited from the broader Women in Computational Topology network. The volume is accessible to a broad range of researchers, both within the field of computational topology and in related disciplines such as statistics, computational biology, and machine learning. .
Topology --- machine learning --- topologie
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This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.
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This book starts from the design requirements of variable geometry turbines for marine gas turbines. It systematically and comprehensively introduces the flow mechanism and characteristics of variable geometry turbines, aerodynamic design methods, variable vane turning design methods, structural design technology of the variable vane system, aerodynamic characteristics and reliability test technology for variable geometry turbines, and so on.
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This book covers designing of various machine elements and serves as a reference for mechanical designing of machine elements in academia and industry. It provides information on designing approaches and several examples and problems, enabling readers to make all of their required calculations for their specific mechanical design or fabrication tasks by using the book's plots (graphs), instead of complicated formulas.
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Machine elements --- Manufacturing technologies --- machineonderdelen
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Machine elements --- Manufacturing technologies --- machineonderdelen
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"A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.Probabilistic Machine Learning grew out of the author's 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach." [Publisher]
Apprentissage automatique --- Algorithmes --- Probabilités. --- Machine learning. --- Algorithms. --- Probabilities. --- Artificial intelligence. Robotics. Simulation. Graphics --- machine learning --- Machine learning --- Probabilities --- Probabilités.
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Machine elements --- Engineering sciences. Technology --- machines --- ingenieurswetenschappen
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Machine elements --- Transport engineering --- transport --- machines
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Machine elements --- Computer. Automation --- beeldverwerking --- machines --- signaalverwerking
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