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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for quantitatively measuring forgetting are selected and used to show how strategies like image augmentation, pretraining, and architectural adaptations mitigate catastrophic forgetting.
Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
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.
Probabilistic Parametric Curves for Sequence Modeling
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.
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems.
Multimodal Panoptic Segmentation of 3D Point Clouds
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.
Machine Vision
The book offers a thorough introduction to machine vision. It is organized in two parts. The first part covers the image acquisition, which is the crucial component of most automated visual inspection systems. All important methods are described in great detail and are presented with a reasoned structure. The second part deals with the modeling and processing of image signals and pays particular regard to methods, which are relevant for automated visual inspection.
Self-learning Anomaly Detection in Industrial Production
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.
Fundamental Interactions
This proceedings volume contains the latest developments in particle physics in collider experiments. The contributions cover new results such as the production of quark-gluon plasma in the heavy-ion collider, the new techniques for precision measurement at low energies, and the status of neutrino physics at various laboratories including the new facilities that are at the planning stage. Sample Chapter(s). Chapter 1: Making Precision Measurements at Hadron Colliders: Two Lectures (1,408 KB). Contents: Making Precision Measurements at Hadron Colliders: Two Lectures (H J Frisch); Evidence for a QuarkOCoGluon Plasma at RHC (J Harris); Neutrino Physics: A Selective Overview (S Oser); Low-Energy Tests of the Standard Model (M Pospelov); Leptonic B Decays (M-C Chang); Cosmic Neutrinos Beyond the Standard Model (U Harbach); Proton Structure from HERA (K Nagano); New Phenomena Searches at CDF (A Soha); Electroweak Results at LEP2 (P Spagnolo); and other papers. Readership: Graduate students, academics and researchers in particle and nuclear physics."
World Modeling for Intelligent Autonomous Systems
The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis.
