Missions and Vehicle Concepts for Modern, Propelled, Lighter-Than-Air Vehicles

The results of studies conducted over the last 15 years to assess missions and vehicle concepts for modern, propelled, lighter-than-air vehicles (airships) were surveyed. Rigid and non-rigid airship concepts are considered. The use of airships for ocean patrol and surveillance is discussed along with vertical heavy lift airships. Military and civilian needs for high altitude platforms are addressed. Around 1970 a resurgence of interest about lighter-than-air vehicles (airships) occurred in both the public at large and in certain isolated elements of the aerospace industry. Such renewals of airship enthusiasm are not new and have, in fact, occurred regularly since the days of the Hindenburg and other large rigid airships. However, the interest that developed in the early 1970's has been particularly strong and self-sustaining for a number of good reasons. The first is the rapid increase in fuel prices over the last decade and the common belief (usually true) that airships are the most fuel efficient means of air transportation. Second, a number of new mission needs have arisen, particularly in surveillance and patrol and in vertical heavy-lift, which would seem to be well-suited to airship capabilities. The third reason is the recent proposal of many new and innovative airship concepts. Finally, there is the prospect of adapting to airships the tremendous amount of new aeronautical technology which has been developed in the past few decades thereby obtaining dramatic new airship capabilities. The primary purpose of this volume is to survey the results of studies, conducted over the last 15 years, to assess missions and vehicle concepts for modern propelled lighter-than-air vehicles

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Multi-Trajectory Automatic Ground Collision Avoidance System with Flight Tests (Project Have ESCAPE)

Multi-trajectory automatic collision avoidance techniques for heavy-type aircraft are explored to increase aviation safety procedures and decrease losses due to controlled flight into terrain. Additionally, this research includes flight test results from the United States Test Pilot School’s Test Management Project (TMP) titled Have Emergency Safe Calculated Autonomous Preplanned Exit (ESCAPE). Currently, the heavy aircraft community lacks an automatic collision avoidance system that has proven to save lives in fighter-type aircraft. The tested algorithm includes both a 3-path and a 5-path avoidance technique that is compared to an optimal solution which minimizes aircraft control to avoid terrain. The research utilizes Level 1 Digital Terrain Elevation Data (DTED) to analyze the terrain and a 3-Degrees of Freedom (DOF) Equations of Motion (EOM) model to predict potential terrain avoidance paths for the aircraft based on current location. The algorithm then waits until all paths collide and automatically activates the path with the longest time until collision with an appropriate time safety margin. The research also characterizes terrain based on changing slope and presents a new classification of aircraft based on performance capabilities. The result was used for algorithm parameter specification of path execution times and pre-planned maneuver creation so that the system can be modified for a wide variety of aircraft. Finally, the algorithm was flight tested against DTED in a simulated environment using the Calspan Learjet to determine actual 3 and 5- path performance, parameter specification, and comparison to the optimal solution. The important recommendations include a need for flexible entry parameters based on current aircraft state, continued evaluation of the terrain during avoidance maneuver execution, and more precise control of the aircraft flight path angle. Finally, due to comparison with the optimal solution, it is concluded that an acceptable terrain avoidance algorithm is possible using only a 3-path solution given that all three paths include a climbing maneuver

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

The post-911 environment has punctuated the force-multiplying capabilities that Remotely Piloted Aircraft (RPA) provides combatant commanders at all echelons on the battlefield. Not only have unmanned aircraft systems made near-revolutionary impacts on the battlefield, their utility and proliferation in law enforcement, homeland security, humanitarian operations, and commercial applications have likewise increased at a rapid rate. As such, under the Federal Aviation Administration (FAA) Modernization and Reform Act of 2012, the United States Congress tasked the FAA to provide for the safe integration of civil unmanned aircraft systems into the national airspace system (NAS) as soon as practicable, but not later than September 30, 2015. However, a necessary entrance criterion to operate RPAs in the NAS is the ability to Sense and Avoid (SAA) both cooperative and noncooperative air traffic to attain a target level of safety as a traditional manned aircraft platform. The goal of this research effort is twofold: First, develop techniques for calculating optimal avoidance trajectories, and second, develop techniques for estimating an intruder aircraft\u27s trajectory in a stochastic environment. This dissertation describes the optimal control problem associated with SAA and uses a direct orthogonal collocation method to solve this problem and then analyzes these results for different collision avoidance scenarios

Simulation and Performance Evaluation of Algorithms for Unmanned Aircraft Conflict Detection and Resolution

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency

Simulation and Performance Evaluation of Algorithms for Unmanned Aircraft Conflict Detection and Resolution

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency

Determination of the viability of Toxoplasma gondii oocysts by PCR real-time after treatment with propidium monoazide

This study aimed to investigate a methodology for discriminating viable and non-viable T. gondii oocysts in water. Analyses included two steps: (i) microscopic investigation with vital dyes; (ii) molecular investigation, using a real time PCR (qPCR), after parasite treatment (or not) with propidium monoazide (PMA). The method was called qPCR-PMA. Oocyst aliquots were incubated (15 min) at 25 ºC or 100 ºC and analyzed by microscopy, after trypan blue and neutral red staining. Microscopic investigation determined viable and non-viable oocysts. For the molecular investigation, both aliquots of oocysts were treated with PMA. Non-viable oocysts, after PMA treatment, exhibited an inhibition of DNA amplification by qPCR. Although analyses were carried out with oocysts treated experimentally, these results suggest that qPCR-PMA can be a useful strategy to distinguish viable and non-viable T. gondiioocysts in water safety testing, showing if water is safe to drink

Receding horizon control for free-flight path optimisation

This paper presents a Receding Horizon Control (RHC) algorithm to the problem of on-line flight path optimization for aircraft in a Free Flight (FF) environment. The motivation to introduce the concept of RHC is to improve the robust performance of solutions in a dynamic and uncertain environment, and also to satisfy the restrictive time limit to the real-time optimization of this complicated air traffic control problem. Firstly, the mathematical model for the on-line FF path optimization problem is set up and discussed. Then, the proposed RHC algorithm is described in details. Simulation results illustrate that the new algorithm is very efficient and promising for practical applications. While achieving almost the same optimal solution as an existing algorithm in the absence of environmental uncertainties, it works better in a dynamic and uncertain environment. In either case, the online computational time of the proposed RHC algorithm is only a fraction of that of the existing algorithm