Context-Enabled Visualization Strategies for Automation Enabled Human-in-the-loop Inspection Systems to Enhance the Situation Awareness of Windstorm Risk Engineers

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. Traditionally, the risk inspection process is highly subjective and depends on the skills of the engineer performing it. This dissertation investigates the sensemaking process of risk engineers while performing risk inspection with special focus on various factors influencing it. This research then investigates how context-based visualizations strategies enhance the situation awareness and performance of windstorm risk engineers. An initial study investigated the sensemaking process and situation awareness requirements of the windstorm risk engineers. The data frame theory of sensemaking was used as the framework to carry out this study. Ten windstorm risk engineers were interviewed, and the data collected were analyzed following an inductive thematic approach. The themes emerged from the data explained the sensemaking process of risk engineers, the process of making sense of contradicting information, importance of their experience level, internal and external biases influencing the inspection process, difficulty developing mental models, and potential technology interventions. More recently human in the loop systems such as drones have been used to improve the efficiency of windstorm risk inspection. This study provides recommendations to guide the design of such systems to support the sensemaking process and situation awareness of windstorm visual risk inspection. The second study investigated the effect of context-based visualization strategies to enhance the situation awareness of the windstorm risk engineers. More specifically, the study investigated how different types of information contribute towards the three levels of situation awareness. Following a between subjects study design 65 civil/construction engineering students completed this study. A checklist based and predictive display based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of windstorm risk engineers. However, the predictive display only helped with certain tasks like understanding the interaction among different components on the rooftop. For remaining tasks, checklist alone was sufficient. Moreover, the decision aids did not place any additional cognitive demand on the participants. This study helped us understand the advantages and disadvantages of the decision aids tested. The final study evaluated the transfer of training effect of the checklist and predictive display based decision aids. After one week of the previous study, participants completed a follow-up study without any decision aids. The performance and situation awareness of participants in the checklist and predictive display group did not change significantly from first trial to second trial. However, the performance and situation awareness of participants in the control condition improved significantly in the second trial. They attributed this to their exposure to SAGAT questionnaire in the first study. They knew what issues to look for and what tasks need to be completed in the simulation. The confounding effect of SAGAT questionnaires needs to be studied in future research efforts

Computer vision for bird strike prevention

Collisions with birds cause damage to aircraft and in some cases can even cause air travel accidents. According to data from international organizations such as the Federal Aviation Administration (FAA), the radar-based tools currently used to address this problem do not solve it, as there is no indication of a decrease in the number of bird strikes. Early detection and notification to pilots of the presence of birds is key to trying to minimize the possibility that bird impacts can occur. The objective of this project is to improve bird detection capacity in the airport environment. To achieve this goal, this work proposes that the solution could be the use of artificial intelligence based devices and computer vision. To test this hypothesis, a model based on convolutional neural networks (CNN) is selected, trained and deployed on a device for testing. To do this, research is carried out on the different strategies used to solve problems with artificial intelligence and the performance of pre-trained classifier and detector models available. To select the computer board where the model will be deployed, a discussion of Raspberry Pi¿s market performance is made. A collection of bird images is made for training the model. The prototype will finally consist of deploying the model on a Raspberry Pi that through a script in Python programming language is able to automatically notice birds in the real world using a camera connected to the Raspberry Pi. If any detection occurs, the model is capable of making a notification that could serve to anticipate impacts and thus allow appropriate preventive measures to be taken beforehand. In conclusion, this technology shows great potential to support existing solutions today. Theoretical results with validation images show accuracy and recall parameters above 90% but experimental tests with the prototype do not allow for a conclusive judgment due to limitations regarding the training data set

Missile Modeling and Simulation of Nominal and Abnormal Scenarios Resulting from External Damage

This thesis presents the development of a six-degree-of-freedom flight simulation environment for missiles and the application thereof to investigate the flight performance of missiles when exposed to external damage. The simulation environment was designed to provide a realistic representation of missile flight dynamics including aerodynamic effects, flight control systems, and self-guidance. The simulation environment was designed to be modular, expandable, and include realistic models of external damage to the missile body obtained by adversarial counteraction. The primary objective of this research was to examine missile flight performance when subjected to unspecified external damage, including changes in trajectory, stability, and controllability, and to provide a basis for the future development of fault tolerant control laws to improve target tracking and overall flight performance when experiencing abnormal conditions. To accomplish this, a variety of scenarios were developed to simulate damage to different parts of the missile, such as the fuselage, wings, and control surfaces. Three types of damage are considered: arbitrary failures which affect the major overall missile dynamic force and moment coefficients, structural failures including wings and fin breakage, and stuck fin failures where a given fin is arbitrarily fixed to a specified deflection. The missile behavior in response to these scenarios was analyzed and compared to the baseline behavior of an undamaged missile. The results of this research demonstrate how simulated missiles behave during flight, under both nominal and abnormal scenarios resulting from external damage. The simulation environment is shown to be a useful tool in examining the performance of missiles under real-world scenarios, such as during combat, in the event of an accident, or when exposed to other adversarial counteractions. This is done by producing envelopes for mission success for each tested scenario and analyzing the results. The results of this research can be used to assist in and improve the design and performance of missiles and enhance their survivability in the field. These results can also be used to determine the amount of damage necessary to prevent a given missile from reaching its target

An Archival Analysis of Stall Warning System Effectiveness During Airborne Icing Encounters

An archival study was conducted to determine the influence of stall warning system performance on aircrew decision-making outcomes during airborne icing encounters. A Conservative Icing Response Bias (CIRB) model was developed to explain the historical variability in aircrew performance in the face of airframe icing. The model combined Bayes’ Theorem with Signal Detection Theory (SDT) concepts to yield testable predictions that were evaluated using a Binary Logistic Regression (BLR) multivariate technique applied to two archives: the NASA Aviation Safety Reporting System (ASRS) incident database, and the National Transportation Safety Board (NTSB) accident databases, both covering the period January 1, 1988 to October 2, 2015. The CIRB model predicted that aircrew would experience more incorrect response outcomes in the face of missed stall warnings than with stall warning False Alarms. These predicted outcomes were observed at high significance levels in the final sample of 132 NASA/NTSB cases. The CIRB model had high sensitivity and specificity and explained 71.5% (Nagelkerke R2) of the variance of aircrew decision-making outcomes during the icing encounters. The reliability and validity metrics derived from this study suggest indicate that the findings are generalizable to the population of U.S. registered turbine-powered aircraft. These findings suggest that icing-related stall events could be reduced if the incidence of stall warning misses could be minimized. Observed stall warning misses stemmed from three principal causes: aerodynamic icing effects, which reduced the stall angle-of-attack (AoA) to below the stall warning calibration threshold; tail stalls, which are not monitored by contemporary protection systems; and icing-induced system issues (such as frozen pitot tubes), which compromised stall warning system effectiveness and airframe envelope protections. Each of these sources of missed stall warnings could be addressed by Aerodynamic Performance Monitoring (APM) systems that directly measure the boundary layer airflow adjacent to the affected aerodynamic surfaces, independent of other aircraft stall protection, air data, and AoA systems. In addition to investigating APM systems, measures should also be taken to include the CIRB phenomenon in aircrew training to better prepare crews to cope with airborne icing encounters. The SDT/BLR technique would allow the forecast gains from these improved systems and training processes to be evaluated objectively and quantitatively. The SDT/BLR model developed for this study has broad application outside the realm of airborne icing. The SDT technique has been extensively validated by prior research, and the BLR is a very robust multivariate technique. Combined, they could be applied to evaluate high order constructs (such as stall awareness for this study), in complex and dynamic environments. The union of SDT and BLR reduces the modeling complexities for each variable into the four binary SDT categories of Hit, Miss, False Alarm, and Correct Rejection, which is the optimum format for the BLR. Despite this reductionist approach to complex situations, the method has demonstrated very high statistical and practical significance, as well as excellent predictive power, when applied to the airborne icing scenario

GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems

The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations

Next generation flight management systems for manned and unmanned aircraft operations - automated separation assurance and collision avoidance functionalities

The demand for improved safety, efficiency and dynamic demand-capacity balancing due to the rapid growth of the aviation sector and the increasing proliferation of Unmanned Aircraft Systems (UAS) in different classes of airspace pose significant challenges to avionics system developers. The design of Next Generation Flight Management Systems (NG-FMS) for manned and unmanned aircraft operations is performed by addressing the challenges identified by various Air Traffic Management (ATM) modernisation programmes and UAS Traffic Management (UTM) system initiatives. In particular, this research focusses on introducing automated Separation Assurance and Collision Avoidance (SA&CA) functionalities (mathematical models) in the NG-FMS. The innovative NG-FMS is also capable of supporting automated negotiation and validation of 4-Dimensional Trajectory (4DT) intents in coordination with novel ground-based Next Generation Air Traffic Management (NG-ATM) systems. One of the key research contributions is the development of a unified method for cooperative and non-cooperative SA&CA, addressing the technical and regulatory challenges of manned and unmanned aircraft coexistence in all classes of airspace. Analytical models are presented and validated to compute the overall avoidance volume in the airspace surrounding a tracked object, supporting automated SA&CA functionalities. The scientific basis of this approach is to assess real-time measurements and associated uncertainties affecting navigation states (of the host aircraft platform), tracking observables (of the static or moving object) and platform dynamics, and translate them to unified range and bearing uncertainty descriptors. The SA&CA unified approach provides an innovative analytical framework to generate high-fidelity dynamic geo-fences suitable for integration in the NG-FMS and in the ATM/UTM/defence decision support tools

Preliminary design of a 100 kW turbine generator

The National Science Foundation and the Lewis Research Center have engaged jointly in a Wind Energy Program which includes the design and erection of a 100 kW wind turbine generator. The machine consists primarily of a rotor turbine, transmission, shaft, alternator, and tower. The rotor, measuring 125 feet in diameter and consisting of two variable pitch blades operates at 40 rpm and generates 100 kW of electrical power at 18 mph wind velocity. The entire assembly is placed on top of a tower 100 feet above ground level