A Neural Network Approach to Fault Detection in Spacecraft Attitude Determination and Control Systems

This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are dened such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time

Maternal input and infants’ response to infant‐directed speech

Caregivers typically use an exaggerated speech register known as infant‐directed speech (IDS) in communication with infants. Infants prefer IDS over adult‐directed speech (ADS) and IDS is functionally relevant in infant‐directed communication. We examined interactions among maternal IDS quality, infants’ preference for IDS over ADS, and the functional relevance of IDS at 6 and 13 months. While 6‐month‐olds showed a preference for IDS over ADS, 13‐month‐olds did not. Differences in gaze following behavior triggered by speech register (IDS vs. ADS) were found in both age groups. The degree of infants’ preference for IDS (relative to ADS) was linked to the quality of maternal IDS infants were exposed to. No such relationship was found between gaze following behavior and maternal IDS quality and infants’ IDS preference. The results speak to a dynamic interaction between infants’ preference for different kinds of social signals and the social cues available to them

Catalogue of new Herbig Ae/Be and classical Be stars - A machine learning approach to Gaia DR2

Context. The intermediate-mass pre-main sequence Herbig Ae/Be stars are key to understanding the differences in formation mechanisms between low- and high-mass stars. The study of the general properties of these objects is hampered by the lack of a well-defined, homogeneous sample, and because few and mostly serendipitously discovered sources are known. Aims. Our goal is to identify new Herbig Ae/Be candidates to create a homogeneous and well defined catalogue of these objects. Methods. We have applied machine learning techniques to 4 150 983 sources with data from Gaia DR2, 2MASS, WISE, and IPHAS or VPHAS+. Several observables were chosen to identify new Herbig Ae/Be candidates based on our current knowledge of this class, which is characterised by infrared excesses, photometric variabilities, and Hα emission lines. Classical techniques are not efficient for identifying new Herbig Ae/Be stars mainly because of their similarity with classical Be stars, with which they share many characteristics. By focusing on disentangling these two types of objects, our algorithm has also identified new classical Be stars. Results. We have obtained a large catalogue of 8470 new pre-main sequence candidates and another catalogue of 693 new classical Be candidates with a completeness of 78.8 ± 1.4% and 85.5 ± 1.2%, respectively. Of the catalogue of pre-main sequence candidates, at least 1361 sources are potentially new Herbig Ae/Be candidates according to their position in the Hertzsprung-Russell diagram. In this study we present the methodology used, evaluate the quality of the catalogues, and perform an analysis of their flaws and biases. For this assessment, we make use of observables that have not been accounted for by the algorithm and hence are selection-independent, such as coordinates and parallax based distances. The catalogue of new Herbig Ae/Be stars that we present here increases the number of known objects of the class by an order of magnitude

Composition analysis of liquid particles in the Arctic stratosphere

International audienceSynoptic scale polar stratospheric clouds (PSCs) that formed without the presence of mountain leewaves were observed in early December 2002 from Kiruna/Sweden using balloon-borne instruments. The physical, chemical, and optical properties of the particles were measured. Within the PSC solid particles existed whenever the temperature was below the equilibrium temperature for nitric acid trihydrate and liquid particles appeared when the temperature fell below an even lower threshold about 3 K above the frost point. The correlation of liquid supercooled ternary solution aerosols with local temperatures is a pronounced feature observed during this flight although the molar ratios H2O/HNO3 were about a factor of 2 higher than model predictions. In addition HCl has been measured for the first time in liquid aerosols. The chlorine isotope signature served as a unique tool to identify unambiguously HCl dissolved in STS particles. Within a narrow temperature range of about three degrees above the frost point, measured HCl molar ratios are below 1 weight%. There is only fair agreement with model predictions