UQTools: The Uncertainty Quantification Toolbox - Introduction and Tutorial

UQTools is the short name for the Uncertainty Quantification Toolbox, a software package designed to efficiently quantify the impact of parametric uncertainty on engineering systems. UQTools is a MATLAB-based software package and was designed to be discipline independent, employing very generic representations of the system models and uncertainty. Specifically, UQTools accepts linear and nonlinear system models and permits arbitrary functional dependencies between the system s measures of interest and the probabilistic or non-probabilistic parametric uncertainty. One of the most significant features incorporated into UQTools is the theoretical development centered on homothetic deformations and their application to set bounding and approximating failure probabilities. Beyond the set bounding technique, UQTools provides a wide range of probabilistic and uncertainty-based tools to solve key problems in science and engineering

Drone Tracking with Drone using Deep Learning

With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection algorithm, which was trained with the created data set, reached a success rate of approximately 93% as a result of the training. As the second approach, the object-tracking algorithm we developed is used. Trainings were carried out in the simulator created in the Matlab environment. The results are presented in detail in the following sections. In this article, some artificial neural networks and some object tracking methods used in the literature are explained

Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library

Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three di erent case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models

Evolutionary computing and particle filtering: a hardware-based motion estimation system

Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm

Achieving broad access to satellite control research with zero robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This thesis was scanned as part of an electronic thesis pilot project.Cataloged from PDF version of thesis.Includes bibliographical references (p. 307-313).Since operations began in 2006, the SPHERES facility, including three satellites aboard the International Space Station (ISS), has demonstrated many future satellite technologies in a true microgravity environment and established a model for developing successful ISS payloads. In 2009, the Zero Robotics program began with the goal of leveraging the resources of SPHERES as a tool for Science, Technology, Engineering, and Math education through a unique student robotics competition. Since the first iteration with two teams, the program has grown over four years into an international tournament involving more than two thousand student competitors and has given hundreds of students the experience of running experiments on the ISS. Zero Robotics tournaments involve an annually updated challenge motivated by a space theme and designed to match the hardware constraints of the SPHERES facility. The tournament proceeds in several phases of increasing difficulty, including a multi-week collaboration period where geographically separated teams work together through the provided tools to write software for SPHERES. Students initially compete in a virtual, online simulation environment, then transition to hardware for the final live championship round aboard the ISS. Along the way, the online platform ensures compatibility with the satellite hardware and provides feedback in the form of 3D simulation animations. During each competition phase, a continuous scoring system allows competitors to incrementally explore new strategies while striving for a seat in the championship. This thesis will present the design of the Zero Robotics competition and supporting online environment and tools that enable users from around the world to successfully write computer programs for satellites. The central contribution is a framework for building virtual platforms that serve as surrogates for limited availability hardware facilities. The framework includes the elaboration of the core principles behind the design of Zero Robotics along with examples and lessons from the implementation of the competition. The virtual platform concept is further extended with a web-based architecture for writing, compiling, simulating, and analyzing programs for a dynamic robot. A standalone and key enabling component of the architecture is a pattern for building fast, high fidelity, web-based simulations. For control of the robots, an easy to use programming interface for controlling 6 degree-of-freedom (6DOF) satellites is presented, along with a lightweight supervisory control law to prevent collisions between satellites without user action. This work also contributes a new form of student robotics competition, including the unique features of model-based online simulation, programming, 6DOF dynamics, a multi-week team collaboration phase, and the chance to test satellites aboard the ISS. Scoring during the competition is made possible by possible by a game-agnostic scoring algorithm, which has been demonstrated during a tournament season and improved for responsiveness. Lastly, future directions are suggested for improving the tournament including a detailed initial exploration of creating open-ended Monte Carlo analysis tools.by Jacob G. Katz.Ph.D

A methodology for the efficient integration of transient constraints in the design of aircraft dynamic systems

Transient regimes experienced by dynamic systems may have severe impacts on the operation of the aircraft. They are often regulated by dynamic constraints, requiring the dynamic signals to remain within bounds whose values vary with time. The verification of these peculiar types of constraints, which generally requires high-fidelity time-domain simulation, intervenes late in the system development process, thus potentially causing costly design iterations. The research objective of this thesis is to develop a methodology that integrates the verification of dynamic constraints in the early specification of dynamic systems. In order to circumvent the inefficiencies of time-domain simulation, multivariate dynamic surrogate models of the original time-domain simulation models are generated using wavelet neural networks (or wavenets). Concurrently, an alternate approach is formulated, in which the envelope of the dynamic response, extracted via a wavelet-based multiresolution analysis scheme, is subject to transient constraints. Dynamic surrogate models using sigmoid-based neural networks are generated to emulate the transient behavior of the envelope of the time-domain response. The run-time efficiency of the resulting dynamic surrogate models enables the implementation of a data farming approach, in which the full design space is sampled through a Monte-Carlo Simulation. An interactive visualization environment, enabling what-if analyses, is developed; the user can thereby instantaneously comprehend the transient response of the system (or its envelope) and its sensitivities to design and operation variables, as well as filter the design space to have it exhibit only the design scenarios verifying the dynamic constraints. The proposed methodology, along with its foundational hypotheses, is tested on the design and optimization of a 350VDC network, where a generator and its control system are concurrently designed in order to minimize the electrical losses, while ensuring that the transient undervoltage induced by peak demands in the consumption of a motor does not violate transient power quality constraints.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Charrier, Jean-Jacques; Committee Member: Garcia, Elena; Committee Member: Grijalva, Santiago; Committee Member: Schrage, Danie