Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos PDF Download
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Author: Janya-anurak, Chettapong
Publisher: KIT Scientific Publishing
ISBN: 3731506424
Category :
Languages : en
Pages : 248
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Book Description
Author: Janya-anurak, Chettapong
Publisher: KIT Scientific Publishing
ISBN: 3731506424
Category :
Languages : en
Pages : 248
Get Book
Book Description
Author: Chettapong Janya-anurak
Publisher:
ISBN: 9781013281723
Category : Mathematics
Languages : en
Pages : 238
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Book Description
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Author: Bullinger, Sebastian
Publisher: KIT Scientific Publishing
ISBN: 373151012X
Category : Computers
Languages : en
Pages : 194
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Book Description
"This work proposes a Multibody Structure from Motion (MSfM) algorithm for moving object reconstruction that incorporates instance-aware semantic segmentation and multiple view geometry methods. The MSfM pipeline tracks two-dimensional object shapes on pixel level to determine object specific feature correspondences, in order to reconstruct 3D object shapes as well as 3D object motion trajectories" -- Publicaciones de Arquitectura y Arte.
Author: Grinberg, Michael
Publisher: KIT Scientific Publishing
ISBN: 3731507811
Category :
Languages : en
Pages : 296
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Book Description
Author: Sommer, Lars Wilko
Publisher: KIT Scientific Publishing
ISBN: 3731511134
Category : Computers
Languages : en
Pages : 276
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Book Description
This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced.
Author: Becker, Stefan
Publisher: KIT Scientific Publishing
ISBN: 3731510383
Category : Computers
Languages : en
Pages : 228
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Book Description
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together.
Author: Qu, Chengchao
Publisher: KIT Scientific Publishing
ISBN: 3731508281
Category :
Languages : en
Pages : 234
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Book Description
Author: Beyerer, Jürgen
Publisher: KIT Scientific Publishing
ISBN: 3731513048
Category :
Languages : en
Pages : 140
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Book Description
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.
Author: Guan, Tianyi
Publisher: KIT Scientific Publishing
ISBN: 3731509784
Category : Computers
Languages : en
Pages : 320
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Book Description
Author: Hug, Ronny
Publisher: KIT Scientific Publishing
ISBN: 3731511983
Category : Mathematics
Languages : en
Pages : 224
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Book Description
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.
