LiDAR-based Weather Detection: Automotive LiDAR Sensors in Adverse Weather Conditions

Technologische Verbesserungen erhöhen den Automatisierungsgrad von Fahrzeugen. Der natürliche Schritt ist dabei, den Fahrer dort zu unterstützen, wo er es am meisten wünscht: bei schlechtem Wetter. Das Wetter beeinflusst alle Sensoren, die zur Wahrnehmung der Umgebung verwendet werden, daher ist es entscheidend, diese Effekte zu berücksichtigen und abzuschwächen. Die vorliegende Dissertation konzentriert sich auf die gerade entstehende Technologie der automobilen Light Detection and Ranging (LiDAR)-Sensoren und trägt zur Entwicklung von autonomen Fahrzeugen bei, die in der Lage sind, unter verschiedenen Wetterbedingungen zu fahren. Die Grundlage ist der erste LiDAR-Punktwolken-Datensatz mit dem Schwerpunkt auf schlechte Wetterbedingungen, welcher punktweise annonatatierte Wetterinformationen enthält, während er unter kontrollierten Wetterbedingungen aufgezeichnet wurde. Dieser Datensatz wird durch eine neuartige Wetter-Augmentation erweitert, um realistische Wettereffekte erzeugen zu können. Ein neuartiger Ansatz zur Klassifizierung des Wetterzustands und der erste CNN-basierte Entrauschungsalgorithmus werden entwickelt. Das Ergebnis ist eine genaue Vorhersage des Wetterstatus und eine Verbesserung der Punktwolkenqualität. Kontrollierte Umgebungen unter verschiedenen Wetterbedingungen ermöglichen die Evaluierung der oben genannten Ansätze und liefern wertvolle Informationen für das automatisierte und autonome Fahren

Renormalizing Diffusion Models

We explain how to use diffusion models to learn inverse renormalization group flows of statistical and quantum field theories. Diffusion models are a class of machine learning models which have been used to generate samples from complex distributions, such as the distribution of natural images. These models achieve sample generation by learning the inverse process to a diffusion process which adds noise to the data until the distribution of the data is pure noise. Nonperturbative renormalization group schemes in physics can naturally be written as diffusion processes in the space of fields. We combine these observations in a concrete framework for building ML-based models for studying field theories, in which the models learn the inverse process to an explicitly-specified renormalization group scheme. We detail how these models define a class of adaptive bridge (or parallel tempering) samplers for lattice field theory. Because renormalization group schemes have a physical meaning, we provide explicit prescriptions for how to compare results derived from models associated to several different renormalization group schemes of interest. We also explain how to use diffusion models in a variational method to find ground states of quantum systems. We apply some of our methods to numerically find RG flows of interacting statistical field theories. From the perspective of machine learning, our work provides an interpretation of multiscale diffusion models, and gives physically-inspired suggestions for diffusion models which should have novel properties.Comment: 69+15 pages, 8 figures; v2: figure and references added, typos correcte

On-line Structural Integrity Monitoring and Defect Diagnosis of Steam Generators Using Analysis of Guided Acoustic Waves

Integrity monitoring and flaw diagnostics of flat beams and tubular structures was investigated in this research using guided acoustic signals. The primary objective was to study the feasibility of using imbedded sensors for monitoring steam generator and heat exchanger tubing. A piezo-sensor suite was deployed to activate and collect Lamb wave signals that propagate along metallic specimens. The dispersion curves of Lamb waves along plate and tubular structures were generated through numerical analysis. Several advanced techniques were explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang transform (HHT) is a recently developed technique for the analysis of non-linear and transient signals. A moving window method was introduced to generate the local peak characters from acoustic time series, and a zooming window technique was developed to localize the structural flaws. The dissertation presents the background of the analysis of acoustic signals acquired from piezo-electric transducers for structural defect monitoring. A comparison of the use of time-frequency techniques, including the Hilbert-Huang transform, is presented. It also presents the theoretical study of Lamb wave propagation in flat beams and tubular structures, and the need for mode separation in order to effectively perform defect diagnosis. The results of an extensive experimental study of detection, location, and isolation of structural defects in flat aluminum beams and brass tubes are presented. The time-frequency analysis and pattern recognition techniques were combined for classifying structural defects in brass tubes. Several types of flaws in brass tubes were tested, both in the air and in water. The techniques also proved to be effective under background/process noise. A detailed theoretical analysis of Lamb wave propagation was performed and simulations were carried out using the finite element software system ABAQUS. This analytical study confirmed the behavior of the acoustic signals acquired from the experimental studies. The results of this research showed the feasibility of on-line detection of small structural flaws by the use of transient and nonlinear acoustic signal analysis, and its implementation by the proper design of a piezo-electric transducer suite. The techniques developed in this research would be applicable to civil structures and aerospace structures

Physics-Based Imaging Methods for Terahertz Nondestructive Evaluation Applications

Lying between the microwave and far infrared (IR) regions, the terahertz gap is a relatively unexplored frequency band in the electromagnetic spectrum that exhibits a unique combination of properties from its neighbors. Like in IR, many materials have characteristic absorption spectra in the terahertz (THz) band, facilitating the spectroscopic fingerprinting of compounds such as drugs and explosives. In addition, non-polar dielectric materials such as clothing, paper, and plastic are transparent to THz, just as they are to microwaves and millimeter waves. These factors, combined with sub-millimeter wavelengths and non-ionizing energy levels, makes sensing in the THz band uniquely suited for many NDE applications. In a typical nondestructive test, the objective is to detect a feature of interest within the object and provide an accurate estimate of some geometrical property of the feature. Notable examples include the thickness of a pharmaceutical tablet coating layer or the 3D location, size, and shape of a flaw or defect in an integrated circuit. While the material properties of the object under test are often tightly controlled and are generally known a priori, many objects of interest exhibit irregular surface topographies such as varying degrees of curvature over the extent of their surfaces. Common THz pulsed imaging (TPI) methods originally developed for objects with planar surfaces have been adapted for objects with curved surfaces through use of mechanical scanning procedures in which measurements are taken at normal incidence over the extent of the surface. While effective, these methods often require expensive robotic arm assemblies, the cost and complexity of which would likely be prohibitive should a large volume of tests be needed to be carried out on a production line. This work presents a robust and efficient physics-based image processing approach based on the mature field of parabolic equation methods, common to undersea acoustics, seismology, and other areas of science and engineering. The method allows the generation of accurate 3D THz tomographic images of objects with irregular, non-planar surfaces using a simple planar scan geometry, thereby facilitating the integration of 3D THz imaging into mainstream NDE use

Experimental and Numerical Modeling of Fluid Flow

This Special Issue provides an overview of the applied experimental and numerical flow, models, which are used to investigate fluid flow in complex situations. The investigated problems are related to fundamental processes or new applications. As demonstrated, the field of the application of experimental and numerical flow models is constantly expanding

Maximum entropy methods applied to NMR and mass spectrometry

Maximum Entropy data processing techniques have been widely available for use by NMR spectroscopisis and mass spectrometrisls since they were first reported as a tool for enhancing damaged images. However, the techniques have been met with a certain amount of scepticism amongst the spectroscopic community; not least their apparent ability to get something for nothing. The aim of the work presented in this thesis is to demonstrate that if these techniques are used carefully and in appropriate situations a great deal of information can be extracted from both NMR and mass spectra. This has been achieved by using the Memsys5 and Massive Inference algorithms to process a range of NMR and mass spectra which suffer from some of the problems which are commonly encountered in spectroscopy, i.e. poor resolution, poor sensitivity, how to process spectra with a wide range of peak widths. The theory underlying the two algorithms is described simply and the techniques for selecting appropriate point spread functions are outlined. Experimental rather than simulated spectra are processed throughout. Throughout this work the Maximum Entropy results are freated with scepticism. A pragmatic approach is employed to demonstrate that the results are valid. It is concluded that the Maximum Entropy methods do have their place amongst the many other data processing strategies used by spectroscopists. If used correctly and in appropriate situations the results can be worth the investment in time needed to obtain a satisfactory result