Parameters Affecting Genetic Algorithm In Leak Detection By Inverse Transients Analysis

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A two-species continuum model for aeolian sand transport

Starting from the physics on the grain scale, we develop a simple continuum description of aeolian sand transport. Beyond popular mean-field models, but without sacrificing their computational efficiency, it accounts for both dominant grain populations, hopping (or "saltating") and creeping (or "reptating") grains. The predicted stationary sand transport rate is in excellent agreement with wind tunnel experiments simulating wind conditions ranging from the onset of saltation to storms. Our closed set of equations thus provides an analytically tractable, numerically precise, and computationally efficient starting point for applications addressing a wealth of phenomena from dune formation to dust emission.Comment: 23 pages, 9 figure

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.https://susy.mdpi

Study, definition and analysis of pilot/system performance measurements for planetary entry experiments

Definition analysis for experimental prediction of pilot performance during planetary entr

Evolutionary Optimization Techniques for 3D Simultaneous Localization and Mapping

Mención Internacional en el título de doctorMobile robots are growing up in applications to move through indoors and outdoors environments, passing from teleoperated applications to autonomous applications like exploring or navigating. For a robot to move through a particular location, it needs to gather information about the scenario using sensors. These sensors allow the robot to observe, depending on the sensor data type. Cameras mostly give information in two dimensions, with colors and pixels representing an image. Range sensors give distances from the robot to obstacles. Depth Cameras mix both technologies to expand their information to three-dimensional information. Light Detection and Ranging (LiDAR) provides information about the distance to the sensor but expands its range to planes and three dimensions alongside precision. So, mobile robots use those sensors to scan the scenario while moving. If the robot already has a map, the sensors measure, and the robot finds features that correspond to features on the map to localize itself. Men have used Maps as a specialized form of representing the environment for more than 5000 years, becoming a piece of important information in today’s daily basics. Maps are used to navigate from one place to another, localize something inside some boundaries, or as a form of documentation of essential features. So naturally, an intuitive way of making an autonomous mobile robot is to implement geometrical information maps to represent the environment. On the other hand, if the robot does not have a previous map, it should build it while moving around. The robot computes the sensor information with the odometer sensor information to achieve this task. However, sensors have their own flaws due to precision, calibration, or accuracy. Furthermore, moving a robot has its physical constraints and faults that may occur randomly, like wheel drifting or mechanical miscalibration that may make the odometers fail in the measurement, causing misalignment during the map building. A novel technique was presented in the mid-90s to solve this problem and overpass the uncertainty of sensors while the robot is building the map, the Simultaneous Localization and Mapping algorithm (SLAM). Its goal is to build a map while the robot’s position is corrected based on the information of two or more consecutive scans matched together or find the rigid registration vector between them. This algorithm has been broadly studied and developed for almost 25 years. Nonetheless, it is highly relevant in innovations, modifications, and adaptations due to the advances in new sensors and the complexity of the scenarios in emerging mobile robotics applications. The scan matching algorithm aims to find a pose vector representing the transformation or movement between two robot observations by finding the best possible value after solving an equation representing a good transformation. It means searching for a solution in an optimum way. Typically this optimization process has been solved using classical optimization algorithms, like Newton’s algorithm or solving gradient and second derivatives formulations, yet this requires an initial guess or initial state that helps the algorithm point in the right direction, most of the time by getting this information from the odometers or inertial sensors. Although, it is not always possible to have or trust this information, as some scenarios are complex and reckon sensors fail. In order to solve this problem, this research presents the uses of evolutionary optimization algorithms, those with a meta-heuristics definition based on iterative evolution that mimics optimization processes that do not need previous information to search a limited range for solutions to solve a fitness function. The main goal of this dissertation is to study, develop and prove the benefits of evolutionary optimization algorithms in simultaneous localization and mapping for mobile robots in six degrees of freedom scenarios using LiDAR sensor information. This work introduces several evolutionary algorithms for scan matching, acknowledge a mixed fitness function for registration, solve simultaneous localization and matching in different scenarios, implements loop closure and error relaxation, and proves its performance at indoors, outdoors and underground mapping applications.Los robots móviles están creciendo en aplicaciones para moverse por entornos interiores y exteriores, pasando de aplicaciones teleoperadas a aplicaciones autónomas como explorar o navegar. Para que un robot se mueva a través de una ubicación en particular, necesita recopilar información sobre el escenario utilizando sensores. Estos sensores permiten que el robot observe, según el tipo de datos del sensor. Las cámaras en su mayoría brindan información en dos dimensiones, con colores y píxeles que representan una imagen. Los sensores de rango dan distancias desde el robot hasta los obstáculos. Las Cámaras de Profundidad mezclan ambas tecnologías para expandir su información a información tridimensional. Light Detection and Ranging (LiDAR) proporciona información sobre la distancia al sensor, pero amplía su rango a planos y tres dimensiones así como mejora la precisión. Por lo tanto, los robots móviles usan esos sensores para escanear el escenario mientras se mueven. Si el robot ya tiene un mapa, los sensores miden y el robot encuentra características que corresponden a características en dicho mapa para localizarse. La humanidad ha utilizado los mapas como una forma especializada de representar el medio ambiente durante más de 5000 años, convirtiéndose en una pieza de información importante en los usos básicos diarios de hoy en día. Los mapas se utilizan para navegar de un lugar a otro, localizar algo dentro de algunos límites o como una forma de documentación de características esenciales. Entonces, naturalmente, una forma intuitiva de hacer un robot móvil autónomo es implementar mapas de información geométrica para representar el entorno. Por otro lado, si el robot no tiene un mapa previo, deberá construirlo mientras se desplaza. El robot junta la información del sensor de distancias con la información del sensor del odómetro para lograr esta tarea de crear un mapa. Sin embargo, los sensores tienen sus propios defectos debido a la precisión, la calibración o la exactitud. Además, mover un robot tiene sus limitaciones físicas y fallas que pueden ocurrir aleatoriamente, como el desvío de las ruedas o una mala calibración mecánica que puede hacer que los contadores de desplazamiento fallen en la medición, lo que provoca una desalineación durante la construcción del mapa. A mediados de los años 90 se presentó una técnica novedosa para resolver este problema y superar la incertidumbre de los sensores mientras el robot construye el mapa, el algoritmo de localización y mapeo simultáneos (SLAM). Su objetivo es construir un mapa mientras se corrige la posición del robot en base a la información de dos o más escaneos consecutivos emparejados o encontrar el vector de correspondencia entre ellos. Este algoritmo ha sido ampliamente estudiado y desarrollado durante casi 25 años. No obstante, es muy relevante en innovaciones, modificaciones y adaptaciones debido a los avances en sensores y la complejidad de los escenarios en las aplicaciones emergentes de robótica móvil. El algoritmo de correspondencia de escaneo tiene como objetivo encontrar un vector de pose que represente la transformación o el movimiento entre dos observaciones del robot al encontrar el mejor valor posible después de resolver una ecuación que represente una buena transformación. Significa buscar una solución de forma óptima. Por lo general, este proceso de optimización se ha resuelto utilizando algoritmos de optimización clásicos, como el algoritmo de Newton o la resolución de formulaciones de gradientes y segundas derivadas, pero esto requiere una conjetura inicial o un estado inicial que ayude al algoritmo a apuntar en la dirección correcta, la mayoría de las veces obteniendo esta información de los sensores odometricos o sensores de inercia, aunque no siempre es posible tener o confiar en esta información, ya que algunos escenarios son complejos y los sensores fallan. Para resolver este problema, esta investigación presenta los usos de los algoritmos de optimización evolutiva, aquellos con una definición meta-heurística basada en la evolución iterativa que imita los procesos de optimización que no necesitan información previa para buscar dentro de un rango limitado el grupo de soluciones que resuelve una función de calidad. El objetivo principal de esta tesis es estudiar, desarrollar y probar los usos de algoritmos de optimización evolutiva en localización y mapeado simultáneos para robots móviles en escenarios de seis grados de libertad utilizando información de sensores LiDAR. Este trabajo introduce varios algoritmos evolutivos que resuelven la correspondencia entre medidas, soluciona el problema de SLAM, implementa una fusion de funciones objetivos y demuestra sus ventajas con pruebas en escenarios reales tanto en interiores, exteriores como mapeado de escenarios subterraneos.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Gerardo Fernández López.- Secretario: María Dolores Blanco Rojas.- Vocal: David Álvarez Sánche

Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data

High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to relevant roadways and movements are presented. This allows for performance analyses that can range from the movement- to the system-level. A comprehensive suite of methodologies to evaluate signal performance using vehicle trajectories is then provided. Finally, uses of CV hard-braking and hard-acceleration event data to assess safety and driver behavior are discussed. To evaluate scalability and test the proposed techniques, performance measures for over 4,700 traffic signals were estimated using more than 910 million vehicle trajectories and 14 billion GPS points in all 50 states and Washington, D.C. The contents of this report will help the industry transition towards a hybrid blend of detector- and CV-based signal performance measures with rigorously defined performance measures that have been peer-reviewed by both academics and industry leaders

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

A new pheromone trail-based genetic algorithm for comparative genome assembly

Gap closing is considered one of the most challenging and time-consuming tasks in bacterial genome sequencing projects, especially with the emergence of new sequencing technologies, such as pyrosequencing, which may result in large amounts of data without the benefit of large insert libraries for contig scaffolding. We propose a novel algorithm to align contigs with more than one reference genome at a time. This approach can successfully overcome the limitations of low degrees of conserved gene order for the reference and target genomes. A pheromone trail-based genetic algorithm (PGA) was used to search globally for the optimal placement for each contig. Extensive testing on simulated and real data sets shows that PGA significantly outperforms previous methods, especially when assembling genomes that are only moderately related. An extended version of PGA can predict additional candidate connections for each contig and can thus increase the likelihood of identifying the correct arrangement of each contig. The software and test data sets can be accessed at http://sourceforge.net/projects/pga4genomics/