VIPLE Extensions in Robotic Simulation, Quadrotor Control Platform, and Machine Learning for Multirotor Activity Recognition

abstract: Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.Dissertation/ThesisMasters Thesis Computer Science 201

Manutenção aeronáutica preditiva: procedimentos, técnicas e business models

A necessidade de optimização do tempo de imobilização das aeronaves para acções de manutenção, fruto da concorrência para uma constante disponibilidade dos recursos, a par das oportunidades resultantes da big data e da IoT, exorta a reflexão acerca da abordagem mais eficiente a adoptar na resolução antecipada de avarias. O presente trabalho discute a premência da manutenção preditiva entre os agentes da aviação civil, propondo um conjunto de procedimentos, técnicas e business models a aplicar pelos decisores de planeamento e estratégias de manutenção dentro de uma companhia aérea. A metodologia utilizada parte da análise de artigos científicos e de revistas da especialidade. Devido ao carácter exploratório do tema, foram realizadas entrevistas estruturadas e nãoestruturadas a profissionais e investigadores especialistas nesta temática para compreender o problema em análise e validar as sugestões apresentadas. Como técnicas de manutenção preditiva são propostas: 1) estipulação de prognósticos quanto ao tempo estimado de operacionalidade de um componente com base no desempenho esperado e nas condições de funcionalidade; 2) classificação dos prognósticos por estratégias opportunity-based e on-condition de acordo com métodos data-driven e model-based; 3) definição do teor de dados a alocar e o papel da IoT na recolha e transmissão destes; 4) as-aservice como hipóteses genéricas e extensíveis de business models; e discutidas práticas relevantes em curso. As sugestões apresentadas permitem a criação de valor através da manutenção preditiva, ponderando os desafios associados à partilha de dados, procedimentos legais e impacto financeiro. É sugerida para pesquisa o desenvolvimento da manutenção prescritiva através da inteligência artificial.The need to optimize immobilization time of aircraft maintenance actions, due to increased competition for constant availability of resources, together with the opportunities created by big data analysis and the IoT, is leading to a reflection on the most efficient way to proceed with respect to premature termination of mechanics faults. This study discusses the relevance of predictive approach to players of civil aviation and proposes a set of procedures, techniques and business models to be applied by decision-makers regarding maintenance planning and strategies within airline companies. The choice for the methodology applied is based on scientific articles and specialty magazines. Due to the exploratory content, a set of structured and non-structured interviews are performed with industry experts and researchers specialized on this topic in order to understand the problem and validate the suggestions. The following predictive maintenance techniques are proposed: 1) definition of prognostics regarding the estimated time of operability of a given component according to its expected performance and its functional conditions; 2) classification of prognostics by opportunity-based and on-condition strategies according to data-driven and model-based methods; 3) definition of content of data to be allocated and the role of IoT in data collection and transmission; 4) as-aservice as generic and extendable hypotheses of business models; and are discussed ongoing practices. These proposals represent a step forward towards value-creation through predictive maintenance, considering the challenges associated with data sharing, legal procedures and financial impact. For future research is proposed the development of prescriptive maintenance through artificial intelligence