EChO Payload electronics architecture and SW design

EChO is a three-modules (VNIR, SWIR, MWIR), highly integrated spectrometer, covering the wavelength range from 0.55 $\mu$m, to 11.0 $\mu$m. The baseline design includes the goal wavelength extension to 0.4 $\mu$m while an optional LWIR module extends the range to the goal wavelength of 16.0 $\mu$m. An Instrument Control Unit (ICU) is foreseen as the main electronic subsystem interfacing the spacecraft and collecting data from all the payload spectrometers modules. ICU is in charge of two main tasks: the overall payload control (Instrument Control Function) and the housekeepings and scientific data digital processing (Data Processing Function), including the lossless compression prior to store the science data to the Solid State Mass Memory of the Spacecraft. These two main tasks are accomplished thanks to the Payload On Board Software (P-OBSW) running on the ICU CPUs.Comment: Experimental Astronomy - EChO Special Issue 201

Realisierung von umfassenden Analysetechniken in einer hybriden Datenverarbeitungsarchitektur

Es gibt heutzutage viele Bereiche, in denen große Mengen an Daten anfallen, wie zum Beispiel in der Industrie 4.0, bei eHealth und bei Überwachung und Regelung des öffentlichen Personennahverkehrs (ÖPNVs). Um möglichst viele vorteilhafte Informationen aus den Daten zu gewinnen, werden umfassende Analysen benötigt, die nicht nur historische, sondern auch Echtzeitdaten berücksichtigen und die Analyseergebnisse in Echtzeit anwenden können. Es gibt hybride Architekturen, welche die Analyse beider Arten von Daten durch die Nutzung von Stream- und Batchverarbeitung unterstützen. Eine solche Architektur ist Hybrid Processing Architecture for Big Data (BRAID), wobei BRAID zusätzlich die Zusammenarbeit zwischen Batch und Stream ermöglicht. Diese Arbeit untersucht, inwiefern BRAID für die Umsetzung solcher umfassender Analysen geeignet ist. Hierfür wird ein Anwendungsfall aus dem Bereich des ÖPNV entwickelt, welcher umfassende Analysen benötigt, und es werden Anforderungen abgeleitet, welche ein System erfüllen muss, um dem Anwendungsfall gerecht zu werden. Beispiele aus der Literatur werden untersucht. Dabei zeigt sich, dass die Anforderungen von bestehenden Systemen noch nicht voll erfüllt werden können. Unter Nutzung der Architektur BRAID wird ein System entwickelt, welches die Anforderungen erfüllt. Es werden verschiedene Machine Learning (ML)-Verfahren und Frameworks, welche für solch ein System genutzt werden können, diskutiert, untereinander verglichen und evaluiert. Das geeignetste wird jeweils für die Umsetzung ausgewählt und das System wird prototypisch implementiert. Das entwickelte System wird gegen die Anforderungen evaluiert, wobei sich zeigt, dass das System alle Anforderungen erfüllen kann. Insgesamt zeigt sich hierdurch, dass BRAID zur Umsetzung eines Systems für umfassende Analysen geeignet ist

Aircraft electromagnetic compatibility

Illustrated are aircraft architecture, electromagnetic interference environments, electromagnetic compatibility protection techniques, program specifications, tasks, and verification and validation procedures. The environment of 400 Hz power, electrical transients, and radio frequency fields are portrayed and related to thresholds of avionics electronics. Five layers of protection for avionics are defined. Recognition is given to some present day electromagnetic compatibility weaknesses and issues which serve to reemphasize the importance of EMC verification of equipment and parts, and their ultimate EMC validation on the aircraft. Proven standards of grounding, bonding, shielding, wiring, and packaging are laid out to help provide a foundation for a comprehensive approach to successful future aircraft design and an understanding of cost effective EMC in an aircraft setting

Metamodel-based uncertainty quantification for the mechanical behavior of braided composites

The main design requirement for any high-performance structure is minimal dead weight. Producing lighter structures for aerospace and automotive industry directly leads to fuel efficiency and, hence, cost reduction. For wind energy, lighter wings allow larger rotor blades and, consequently, better performance. Prosthetic implants for missing body parts and athletic equipment such as rackets and sticks should also be lightweight for augmented functionality. Additional demands depending on the application, can very often be improved fatigue strength and damage tolerance, crashworthiness, temperature and corrosion resistance etc. Fiber-reinforced composite materials lie within the intersection of all the above requirements since they offer competing stiffness and ultimate strength levels at much lower weight than metals, and also high optimization and design potential due to their versatility. Braided composites are a special category with continuous fiber bundles interlaced around a preform. The automated braiding manufacturing process allows simultaneous material-structure assembly, and therefore, high-rate production with minimal material waste. The multi-step material processes and the intrinsic heterogeneity are the basic origins of the observed variability during mechanical characterization and operation of composite end-products. Conservative safety factors are applied during the design process accounting for uncertainties, even though stochastic modeling approaches lead to more rational estimations of structural safety and reliability. Such approaches require statistical modeling of the uncertain parameters which is quite expensive to be performed experimentally. A robust virtual uncertainty quantification framework is presented, able to integrate material and geometric uncertainties of different nature and statistically assess the response variability of braided composites in terms of effective properties. Information-passing multiscale algorithms are employed for high-fidelity predictions of stiffness and strength. In order to bypass the numerical cost of the repeated multiscale model evaluations required for the probabilistic approach, smart and efficient solutions should be applied. Surrogate models are, thus, trained to map manifolds at different scales and eventually substitute the finite element models. The use of machine learning is viable for uncertainty quantification, optimization and reliability applications of textile materials, but not straightforward for failure responses with complex response surfaces. Novel techniques based on variable-fidelity data and hybrid surrogate models are also integrated. Uncertain parameters are classified according to their significance to the corresponding response via variance-based global sensitivity analysis procedures. Quantification of the random properties in terms of mean and variance can be achieved by inverse approaches based on Bayesian inference. All stochastic and machine learning methods included in the framework are non-intrusive and data-driven, to ensure direct extensions towards more load cases and different materials. Moreover, experimental validation of the adopted multiscale models is presented and an application of stochastic recreation of random textile yarn distortions based on computed tomography data is demonstrated

Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children's story to motivate Braid's design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.Comment: Accepted at AAAI-202

Third NASA Advanced Composites Technology Conference, volume 1, part 1

This document is a compilation of papers presented at the Third NASA Advanced Composites Technology (ACT) Conference. The ACT Program is a major multi-year research initiative to achieve a national goal of technology readiness before the end of the decade. Conference papers recorded results of research in the ACT Program in the specific areas of automated fiber placement, resin transfer molding, textile preforms, and stitching as these processes influence design, performance, and cost of composites in aircraft structures. Papers sponsored by the Department of Defense on the Design and Manufacturing of Low Cost Composites (DMLCC) are also included in Volume 2 of this document

Manufacturing Quality Control with Autoencoder-Based Defect Localization and Unsupervised Class Selection

Manufacturing industries require efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlling product quality with high precision. Automation based on computer vision poses a promising solution to prevent bottlenecks at the product quality checkpoint. We considered recent advancements in machine learning to improve visual defect localization, but challenges persist in obtaining a balanced feature set and database of the wide variety of defects occurring in the production line. This paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pre-trained VGG-16 network. The selected classes of defects are augmented with natural wild textures to simulate artificial defects. The study demonstrates the effectiveness of the defect localizing autoencoder with unsupervised class selection for improving defect detection in manufacturing industries. The proposed methodology shows promising results with precise and accurate localization of quality defects on melamine-faced boards for the furniture industry. Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios