Probabilistic Parametric Curves for Sequence Modeling

Probabilistic Parametric Curves for Sequence Modeling

Author: Hug, Ronny

Publisher: KIT Scientific Publishing

Published: 2022-07-12

Total Pages: 224

ISBN-13: 3731511983

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This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation.


Robotics, Computer Vision and Intelligent Systems

Robotics, Computer Vision and Intelligent Systems

Author: Péter Galambos

Publisher: Springer Nature

Published: 2022-11-09

Total Pages: 241

ISBN-13: 3031196503

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This volume constitutes the papers of two workshops which were held in conjunctionwith the First International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2020, Virtual Event, in November 4-6, 2020 and Second International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2021, Virtual Event, in October 25-27, 2021. The 11 revised full papers presented in this book were carefully reviewed and selectedfrom 53 submissions.


Multimodal Panoptic Segmentation of 3D Point Clouds

Multimodal Panoptic Segmentation of 3D Point Clouds

Author: Dürr, Fabian

Publisher: KIT Scientific Publishing

Published: 2023-10-09

Total Pages: 248

ISBN-13: 3731513145

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The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.


Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

Author: Beyerer, Jürgen

Publisher: KIT Scientific Publishing

Published: 2023-07-05

Total Pages: 140

ISBN-13: 3731513048

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In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop's results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB.


Self-learning Anomaly Detection in Industrial Production

Self-learning Anomaly Detection in Industrial Production

Author: Meshram, Ankush

Publisher: KIT Scientific Publishing

Published: 2023-06-19

Total Pages: 224

ISBN-13: 3731512572

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Configuring an anomaly-based Network Intrusion Detection System for cybersecurity of an industrial system in the absence of information on networking infrastructure and programmed deterministic industrial process is challenging. Within the research work, different self-learning frameworks to analyze passively captured network traces from PROFINET-based industrial system for protocol-based and process behavior-based anomaly detection are developed, and evaluated on a real-world industrial system.


Distributed Planning for Self-Organizing Production Systems

Distributed Planning for Self-Organizing Production Systems

Author: Pfrommer, Julius

Publisher: KIT Scientific Publishing

Published: 2024-06-04

Total Pages: 210

ISBN-13: 373151253X

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In dieser Arbeit wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. - Most production processes are rigid not only by way of the physical layout of machines and their integration, but also by the custom programming of the control logic for the integration of components to a production systems. Changes are time- and resource-expensive. This makes the production of small lot sizes of customized products economically challenging. This work develops solutions for the automated adaptation of production systems based on self-organisation and distributed planning.


Probabilistic Graphical Models

Probabilistic Graphical Models

Author: Luis Enrique Sucar

Publisher: Springer Nature

Published: 2020-12-23

Total Pages: 370

ISBN-13: 3030619435

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This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.


Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modeling in Bioinformatics and Medical Informatics

Author: Dirk Husmeier

Publisher: Springer Science & Business Media

Published: 2006-05-06

Total Pages: 511

ISBN-13: 1846281199

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Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.


Probabilistic Numerics

Probabilistic Numerics

Author: Philipp Hennig

Publisher: Cambridge University Press

Published: 2022-06-30

Total Pages: 411

ISBN-13: 1107163447

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A thorough introduction to probabilistic numerics showing how to build more flexible, efficient, or customised algorithms for computation.


Probabilistic Structural Mechanics Handbook

Probabilistic Structural Mechanics Handbook

Author: C.R. Sundararajan

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 756

ISBN-13: 1461517710

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The need for a comprehensive book on probabilistic structural mechanics that brings together the many analytical and computational methods developed over the years and their applications in a wide spectrum of industries-from residential buildings to nuclear power plants, from bridges to pressure vessels, from steel structures to ceramic structures-became evident from the many discussions the editor had with practising engineers, researchers and professors. Because no single individual has the expertise to write a book with such a di.verse scope, a group of 39 authors from universities, research laboratories, and industries from six countries in three continents was invited to write 30 chapters covering the various aspects of probabilistic structural mechanics. The editor and the authors believe that this handbook will serve as a reference text to practicing engineers, teachers, students and researchers. It may also be used as a textbook for graduate-level courses in probabilistic structural mechanics. The editor wishes to thank the chapter authors for their contributions. This handbook would not have been a reality without their collaboration.