Synthesis of time-to-amplitude converter by mean coevolution with adaptive parameters

Copyright © 2011 the authors and Scientific Research Publishing Inc. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)The challenging task to synthesize automatically a time-to-amplitude converter, which unites by its functionality several digital circuits, has been successfully solved with the help of a novel methodology. The proposed approach is based on a paradigm according to which the substructures are regarded as additional mutation types and when ranged with other mutations form a new adaptive individual-level mutation technique. This mutation approach led to the discovery of an original coevolution strategy that is characterized by very low selection rates. Parallel island-model evolution has been running in a hybrid competitive-cooperative interaction throughout two incremental stages. The adaptive population size is applied for synchronization of the parallel evolutions

Synthesis and characterisation of controllably functionalised polyaniline nanofibres

A novel method for functionalising solution based polyaniline (PAni) nanofibres is reported whereby the degree of side-chain attachment can be controllably altered. The covalent attachment of functional side-groups to the surface of PAni nanostructures is achieved by post-polymerisation reflux in the presence of a nucleophile and the functionalised nanomaterial can be purified by simple centrifugation. The technique is therefore easily scalable. We demonstrate that control over the extent of side-chain attachment can be achieved simply by altering the amount of nucleophile added during reflux. We provide evidence that covalently attached carboxlate side-chains influence the doping mechanism of polyaniline and can be used to introduce self-doping behaviour. Acid functionalised nanofibres remain redox active and retain their optical switching capabilities in response to changes in the local chemical environment, thus making them suitable for adaptive sensing applications

A study of transonic aerodynamic analysis methods for use with a hypersonic aircraft synthesis code

A means of performing routine transonic lift, drag, and moment analyses on hypersonic all-body and wing-body configurations were studied. The analysis method is to be used in conjunction with the Hypersonic Vehicle Optimization Code (HAVOC). A review of existing techniques is presented, after which three methods, chosen to represent a spectrum of capabilities, are tested and the results are compared with experimental data. The three methods consist of a wave drag code, a full potential code, and a Navier-Stokes code. The wave drag code, representing the empirical approach, has very fast CPU times, but very limited and sporadic results. The full potential code provides results which compare favorably to the wind tunnel data, but with a dramatic increase in computational time. Even more extreme is the Navier-Stokes code, which provides the most favorable and complete results, but with a very large turnaround time. The full potential code, TRANAIR, is used for additional analyses, because of the superior results it can provide over empirical and semi-empirical methods, and because of its automated grid generation. TRANAIR analyses include an all body hypersonic cruise configuration and an oblique flying wing supersonic transport

Progress on Gust Load Alleviation Wind Tunnel Experiment and Aeroservoelastic Model Validation for a Flexible Wing with Variable Camber Continuous Trailing Edge Flap System

This paper discusses a wind tunnel experiment of active gust load alleviation of a flexible wing which took place at University of Washington (UW) in 2019. The experiment performed under a NASA SBIR contract with Scientific Systems Company, Inc (SSCI). The objective of the experiment is to demonstrate active controls of the Variable Camber Continuous Trailing Edge Flap (VCCTEF) system for gust load alleviation and real-time drag optimization. The wind tunnel model is a 8.2% sub-scale Common Research Model (CRM) wing. The wing structure is designed to provide a substantial degree of flexibility to represent that of a modern high-aspect ratio wing. Eight active control surfaces are employed in the VCCTEF. A new gust generator system was designed and installed by UW under a sub-contract with SSCI. The first test entry started in July 2019 and ended in September 2019. During this test entry, many significant issues were found with the hardware and software. The significant issues with the servos prevented the test objective from being completed. A follow-up second test entry in 2020 is being planned. The wing system is being repaired by SSCI. This paper reports on the progress of this experimental effort and the aeroservoelastic (ASE) model validation which was conducted during the test entry

Use of supervised machine learning for GNSS signal spoofing detection with validation on real-world meaconing and spoofing data : part I

The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user's system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing