scholarly journals Avionics Graphics Hardware Performance Prediction with Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Simon R. Girard ◽  
Vincent Legault ◽  
Guy Bois ◽  
Jean-François Boland
Keyword(s):  
Machine Learning ◽  
System Design ◽  
Application Programming Interface ◽  
Design Tool ◽  
Graphics Hardware ◽  
Training Phase ◽  
Desktop Computer ◽  
Software Application ◽  
Nonparametric Models ◽  
3D Scene

Within the strongly regulated avionic engineering field, conventional graphical desktop hardware and software application programming interface (API) cannot be used because they do not conform to the avionic certification standards. We observe the need for better avionic graphical hardware, but system engineers lack system design tools related to graphical hardware. The endorsement of an optimal hardware architecture by estimating the performance of a graphical software, when a stable rendering engine does not yet exist, represents a major challenge. As proven by previous hardware emulation tools, there is also a potential for development cost reduction, by enabling developers to have a first estimation of the performance of its graphical engine early in the development cycle. In this paper, we propose to replace expensive development platforms by predictive software running on a desktop computer. More precisely, we present a system design tool that helps predict the rendering performance of graphical hardware based on the OpenGL Safety Critical API. First, we create nonparametric models of the underlying hardware, with machine learning, by analyzing the instantaneous frames per second (FPS) of the rendering of a synthetic 3D scene and by drawing multiple times with various characteristics that are typically found in synthetic vision applications. The number of characteristic combinations used during this supervised training phase is a subset of all possible combinations, but performance predictions can be arbitrarily extrapolated. To validate our models, we render an industrial scene with characteristic combinations not used during the training phase and we compare the predictions to those real values. We find a median prediction error of less than 4 FPS.

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Enabling Technology for Remote Prosthetic Alignment Tuning

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 659-664
Author(s):  
David A Boone ◽  
Sarah R Chang
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
Software Application ◽  
Prosthetic Socket ◽  
Prosthetic Alignment ◽  
Prosthesis Alignment ◽  
Alignment Tool ◽  
Measuring Machine

ABSTRACT Introduction This research has resulted in a system of sensors and software for effectively adjusting prosthetic alignment with digital numeric control. We called this suite of technologies the Prosthesis Smart Alignment Tool (ProSAT) system. Materials and Methods The ProSAT system has three components: a prosthesis-embedded sensor, an alignment tool, and an Internet-connected alignment expert system application that utilizes machine learning to analyze prosthetic alignment. All components communicate via Bluetooth. Together, they provide for numerically controlled prosthesis alignment adjustment. The ProSAT components help diagnose and guide the correction of very subtle, difficult-to-see imbalances in dynamic gait. The sensor has been cross-validated against kinetic measurement in a gait laboratory, and bench testing was performed to validate the performance of the tool while adjusting a prosthetic socket based on machine learning analyses from the software application. Results The three-dimensional alignment of the prosthetic socket was measured pre- and postadjustment from two fiducial points marked on the anterior surface of the prosthetic socket. A coordinate measuring machine was used to derive an alignment angular offset from vertical for both conditions: pre- and postalignment conditions. Of interest is the difference in the angles between conditions. The ProSAT tool is only controlling the relative change made to the alignment, not an absolute position or orientation. Target alignments were calculated by the machine learning algorithm in the ProSAT software, based on input of kinetic data samples representing the precondition and where a real prosthetic misalignment condition was known a priori. Detected misalignments were converted by the software to a corrective adjustment in the prosthesis alignment being tested. We demonstrated that a user could successfully and quickly achieve target postalignment change within an average of 0.1°. Conclusions The accuracy of a prototype ProSAT system has been validated for controlled alignment changes by a prosthetist. Refinement of the ergonomic form and technical function of the hardware and clinical usability of the mobile software application are currently being completed with benchtop experiments in advance of further human subject testing of alignment efficiency, accuracy, and user experience.

AN INTEGRATED SPACE SYSTEM DESIGN TOOL KIT FOR THE ITA SPACE CENTER’S PROJECTS

2021 ◽  
Author(s):  
Ana Luiza de Souza Maran ◽  
Gustavo Corrêa Fazio ◽  
Victor Moreno Coelho ◽  
Débora Miyuki Shinzato ◽  
Erica Generoso Lima ◽  
...  
Keyword(s):  
System Design ◽  
Design Tool ◽  
Space System

Design Methodology for Fluid Power Systems Using Hypermedia

1999 ◽  
Author(s):  
Edmund J. Hughes ◽  
Thomas G. Richards ◽  
Derek G. Tilley
Keyword(s):  
Power Systems ◽  
Power System ◽  
System Design ◽  
Good Practice ◽  
Practical Experience ◽  
Design Tool ◽  
Fluid Power ◽  
Specific Information ◽  
Fluid Power System ◽  
Fluid Power Systems

Abstract Fluid power system design is a skilful and complex task requiring significant practical experience and heuristic knowledge gained over many years. Industries specifying and/or designing fluid power systems in their products are at risk should they lose key personnel with this knowledge. This problem is compounded by a lack of formal training at the graduate level, creating a shortfall in qualified fluid power designers. Design support tools that assist and guide designers in their work, as part of a Concurrent Engineering approach, should be capable of providing both good practice guidance and a framework into which product specific information can be stored for later reuse. This paper discusses the early development of a fluid power system design tool that seeks to offer this support using a methodology developed in hypermedia.

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