Simulation of a Machine Learning Based Controller for a Fixed-Wing UAV with Distributed Sensors

Recent research suggests that the information obtained from arrays of sensors distributed on the wing of a fixed-wing small unmanned aerial vehicle (UAV) can provide information not available to conventional sensor suites. These arrays of sensors are capable of sensing the flow around the aircraft and it has been indicated that they could be a potential tool to improve flight control and overall flight performance. However, more work needs to be carried out to fully exploit the potential of these sensors for flight control. This work presents a 3 degrees-of-freedom longitudinal flight dynamics and control simulation model of a small fixed-wing UAV. Experimental readings of an array of pressure and strain sensors distributed across the wing were integrated in the model. This study investigated the feasibility of using machine learning to control airspeed of the UAV using the readings from the sensing array, and looked into the sensor layout and its effect on the performance of the controller. It was found that an artificial neural network was able to learn to mimic a conventional airspeed controller using only distributed sensor signals, but showed better performance for controlling changes in airspeed for a constant altitude than holding airspeed during changes in altitude. The neural network could control airspeed using either pressure or strain sensor information, but having both improved robustness to increased levels of turbulence. Results showed that some strain sensors and many pressure sensors signals were not necessary to achieve good controller performance, but that the pressure sensors near the leading edge of the wing were required. Future work will focus on replacing other elements of the flight control system with machine learning elements and investigate the use of reinforcement learning in place of supervised learning.</p

Employability and higher education: contextualising female students' workplace experiences to enhance understanding of employability development

Current political and economic discourses position employability as a responsibility of higher education, which deploys mechanisms such as supervised work experience (SWE) to embed employability skills development into the undergraduate curriculum. However, workplaces are socially constructed complex arenas of embodied knowledge that are gendered. Understanding the usefulness of SWE therefore requires consideration of the contextualised experiences of it, within these complex environments. This study considers higher education's use of SWE as a mechanism of employability skills development through exploration of female students' experiences of accounting SWE, and its subsequent shaping of their views of employment. Findings suggest that women experience numerous, indirect gender-based inequalities within their accounting SWE about which higher education is silent, perpetuating the framing of employability as a set of individual skills and abilities. This may limit the potential of SWE to provide equality of employability development. The study concludes by briefly considering how insights provided by this research could better inform higher education's engagement with SWE within the employability discourse, and contribute to equality of employability development opportunity

Time And Frequency Synthesis Parameters Of Severely

This paper describes a pilot study into the mechanics of synthesizing severely pathological voices. Successful synthesis of suchvoices may ultimately provide a quantitative method for evaluating and documenting voice qualities. An analysis-by-synthesis approachusing the formantsynthesizer KLSYN was used to model the voices of 24 patients suffering from voice disorders. Results suggest a number of modifications to KLSYN that would facilitate synthesis of these voices