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Content augmentation strategies (CAS) are instructional methods which specify the overlaying of content objects by content augmentation objects in order to increase the effectiveness and efficiency of instruction. The goals of this research were to build a comprehensive framework around CASs, determine the experimental effects of CASs in an instructional virtual environment (VE), and make recommendations regarding the employment and further study of CASs in instructional virtual environments. The VE experiment examined the effectiveness and efficiency impact of six different content augmentation strategies which overlayed different content augmentation objects onto four immersive VE scenarios. Sixty university students, 40 men and 20 women, executed three CAS-enhanced training missions and one no-CAS test mission. The task involved the recall and correct application of specific rules for three subtasks of a military helicopter landing zone scouting mission. The strategies included a no-strategy control condition, an arrow condition, an audio coaching condition, a text coaching condition, an arrow plus audio coaching condition, and an arrow plus text coaching condition. Statistical and decision analyses were conducted on the effectiveness and efficiency performance data. Statistically significant differences were found which supported the general superiority of the audio content augmentation strategy for these tasks. This dissertation may be the first use of a decision analysis approach for analyzing the results of behavioral data for instructional design decisions. The decision analysis approach used decision trees, simulation and optimization to obtain content augmentation strategy rankings. As this approach is normally used for course of action analysis and comparing alternative system configurations, the validity of this approach in this context has yet to be determined. The decision analysis approach obtained plausible and similar, but not identical recommendations to the statistical approach. The decision analysis approach may constitute a limited instantiation of a proposed optimal stimulus set instructional design model which conceptually framed the experiment. Training guideline recommendations, experimental procedure recommendations, and a comprehensive framework for future research are also presented.

Safety training is a vital component to the well-being of individuals in all industries. With technology advancing at the current pace, conventional training methods are no longer the most effective way to communicate information. There is a strong need for safety training that incorporates new methods and forms of communication to obtain higher levels of comprehension. Virtual reality systems offer a highly customizable and interactive form of delivering information to users. This research addresses major gaps in the field of safety training using virtual reality systems and provides a design framework for creating a virtual safety-training system. A model for the virtual environment is designed and developed and the process and justification is described. The environment and an applied use case for this model is developed and verified using a sample of trainees that would use the model. This exploratory framework provides a significant contribution to the field of safety education through virtual reality systems and can be expanded with further research.

Unmanned ground vehicles (UGV) are being explored for use in military domains. Military UGVs operate in complex off-road environments. Vehicle mobility forecasting plays an important role in understanding how and where a vehicle can operate. Traditional mobility forecasting has been done using an analytical model known as the NATO Reference Mobility Model (NRMM). There has been a push to extend the forecasting capabilities of NRMM by integrating more simulation methods. Simulation enables the repeated testing of UGVs in scenarios that would be difficult or dangerous to study in real world testing. To accurately capture UGV performance in simulation, the operating environment must be accurately modeled. Current widely used methods for generating forested virtual environments rely on random methods. These methods result in forests that can appear to be realistic when visually inspected but lack the appropriate distribution of different sizes of vegetation. The size and distribution of vegetation plays a major role in the ability of a vehicle to operate in a forested environment. Therefore, there is a need for alternative forest generation algorithms that generate more realistic virtual forests. To address this, a novel environment generation model based on forest ecology was implemented. This model accurately captures vegetation growth, disbursement, and competition. Simulated UGV self-driving performance for scenes generated using the ecological model was compared to performance for scenes generated using a widely adopted random model. Resulting speeds across each scene were averaged to predict a speed made good (SMG). Vehicle SMG predictions were made in NRMM using scene descriptions matching each of the random and ecological scenes. Using a continuous vegetation override function in simulation, SMG predictions for both methods were similar to the results of NRMM. However, the predicted speeds for scenes generated with the ecological model were different from the predicted speeds for scenes generated with the random model. When examining the distribution and frequency of different sizes of trees, ecological scenes more closely match the distribution and frequency of trees that are expected for real forested environments suggesting that predictions for speed in ecological scenes better represent potential speeds for real environments.

Mobility modeling is a critical step in the ground vehicle acquisition process for military vehicles. Mobility modeling tools, and in particular the NATO Reference Mobility Model (NRMM), have played a critical role in understanding the mission-level capabilities of ground vehicles. This understanding via modeling supports not only developers during early vehicle design but also decision makers in the field previewing the capabilities of ground vehicles in real-world deployments. Due to decades of field testing and operations, mobility modeling for traditional ground vehicles is well-understood; however, mobility modeling tools for evaluating autonomous mobility are sparse. Therefore, this dissertation proposes and derives a Reference Autonomous Mobility Model (RAMM). The RAMM leverages cutting-edge modeling and simulation tools to build a mobility model that serves as the mission-level mobility modeling tool currently lacking in the unmanned ground vehicle (UGV) community, thereby filling the current analysis gap in the autonomous vehicle acquisition cycle. The RAMM is built on (1) a thorough review of theories of verification and validation of simulations, (2) a novel framework for validating simulations of autonomous systems and (3) the mobility modeling framework already established by the NRMM. These building blocks brought to light the need for new, validated modeling and simulation (M&S) tools capable of simulating, at a highidelity, autonomous unmanned ground vehicle operations. This dissertation maps the derivation of the RAMM, starting with a history of verification of simulation models and a literature review of current autonomous mobility modeling methods. In light of these literature reviews, a new framework for V&V of simulations of autonomous systems is proposed, and the requirements for and derivation of the RAMM is presented. This dissertation concludes with an example application of the RAMM for route planning for autonomous UGVs. Once fully developed, the RAMM will serve as an integral part in the design, development, testing and evaluation, and ultimate fielding of autonomous UGVs for military applications.

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Communicative acts constitute organizations as social entities. I build upon the most structured previous analysis of this process, the four flows framework, by introducing a complex systems model of how organization emerges along a continuum, thereby enabling measurement of the growth and decline of partial organizations. I validate my approach using simulated data from two stochastic agent-based models and 30 historical case studies of insurgency. I show that the four flows may be used to assess the historical victor of a conflict, or to track the emergence of an organization from real-time communication network data. My results demonstrate the complex interrelationship of the four flows, and how they relate to social phenomena such as information asymmetry, individual versus group interest, governance, and the development of community structure. I reaffirm the centrality of these flows to the phenomenon of organization, while challenging the minimum requirements for it to begin, by showing that organization spontaneously emerges in a population as a result of markers of affiliation and human cognitive biases. / Doctor of Philosophy / Humans organize collective behavior by communicating. Prior research has shown that all organizations establish the costs and benefits of membership, distinctions from other organizations, enduring protocols, and approaches to short-term coordination. The strength with which organizations define each of these traits emerges on a continuum from a nascent organization to a robust one. My work is the first to place these acts of communication in an engineering model, showing how an organization works as a system to reduce collective uncertainty. I first explore my model in a computer simulation, demonstrating that each of the four processes can be measured. I then quantify the strength of each process in 30 case studies of insurgency, measuring the changing effectiveness of the insurgents and their state opponents at establishing themselves as the governing entity in an area. My technique accurately predicts the outcome of all 30 case studies. Finally, using a second simulation, I demonstrate measures of all four processes in communication records and show that organization may be the result of merely recognizing oneself as part of a group, amid basic patterns in human thinking, rather than evidence of cooperation toward shared objectives.

This dissertation investigates the evacuees' behavior under hurricane evacuation conditions and develops an agent-based travel demand model system for hurricane evacuation simulation using these behavioral findings. The dissertation econometrically models several important evacuation decisions including evacuate-stay, accommodation type choice, evacuation destination choice, evacuation mode choice, departure time choice, and vehicle usage choice. In addition, it explicitly considers the pre-evacuation preparation activities using activity-based approach. The models are then integrated into a two-module agent-based travel demand model system. The dissertation first develops the evacuate-stay choice model using the random-coefficient binary logit specification. It uses heterogeneous mean of the random parameter across households to capture shadow evacuation. It is found that the likelihood of evacuation for households that do not receive any evacuation notice decreases as their distance to coast increase on average. The distance sensitivity factor, or DSF, is introduced to construct the different scenarios of geographical extent of shadow evacuation. The dissertation then conducts statistical analysis of the vehicle usage choice. It identifies the contributing factors to households' choice of the number of vehicles used for evacuation and develop predictive models of this choice that explicitly consider the constraint imposed by the number of vehicles owned by the household. This constraint is not accommodated by ordered response models. Data comes from a post-storm survey for Hurricane Ivan. The two models developed are variants of the regular Poisson regression model: the Poisson model with exposure and right-censored Poisson regression. The right-censored Poisson model is preferred due to its inherent capabilities, better fit to the data, and superior predictive power. The multivariable model and individual variable analyses are used to investigate seven hypotheses. Households traveling longer distances or evacuating later are more likely to use fewer vehicles. Households with prior hurricane experience, greater numbers of household members between 18 and 80, and pet owners are more likely to use a greater number of vehicles. Income and distance from the coast are insignificant in the multivariable models, although their individual effects have statistically significant linear relationship. However, the Poisson based models are non-linear. The method for using the right-censored Poisson model for producing the desired share of vehicle usage is also provided for the purpose of generating individual predictions for simulation. The dissertation then presents a descriptive analysis of and econometric models for households' pre-evacuation activities based on behavioral intention data collected for Miami Beach, Florida. The descriptive analysis shows that shopping - particularly food, gasoline, medicine, and cash withdrawal - accounts for the majority of preparation activities, highlighting the importance of maintaining a supply of these items. More than 90% of the tours are conducted by driving, emphasizing the need to incorporate pre-evacuation activity travel into simulation studies. Households perform their preparation activities early in a temporally concentrated manner and generally make the tours during daylight. Households with college graduates, larger households, and households who drive their own vehicles are more likely to engage in activities that require travel. The number of household members older than 64 has a negative impact upon engaging in out-of-home activities. An action day choice model for the first tour suggests that households are more likely to buy medicine early but are more likely to pick up friends/relatives late. Households evacuating late are more likely to conduct their activities late. Households with multiple tours tend to make their first tour early. About 10% of households chain their single activity chains with their ultimate evacuation trips. The outcomes of this paper can be used in demand generation for traffic simulations. The dissertation finally uses the behavioral findings and develops an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating the comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate-stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures the pre-evacuation preparation activities using an activity-based approach. A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area is discussed. The simulation results clearly indicate the model system produced the distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 3.1 miles (5 km) of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools. The agent-based travel demand model system is capable of generating activity plans that works with agent-based traffic simulation tools and conventional trip-based simulation tools. It will facilitate the hurricane evacuation management. / Ph. D.

This thesis provides insight into quantitative accuracy assessment of the modeling and simulation of power electronic converters. Verification, Validation, and Uncertainty quantification (VVandUQ) provides a means to quantify the disagreement between computational simulation results and experimental results in order to have quantitative comparisons instead of qualitative comparisons. Due to the broad applications of modeling and simulation in power electronics, VVandUQ is used to evaluate the credibility of modeling and simulation results. The topic of VVandUQ needs to be studied exclusively for power electronic converters. To carry out this work, the formal procedure for VVandUQ of power electronic converters is presented. The definition of the fundamental words in the proposed framework is also provided. The accuracy of the switching model of a three-phase Voltage Source Inverter (VSI) is quantitatively assessed following the proposed procedure. Accordingly, this thesis describes the hardware design and development of the switching model of the three-phase VSI. / Master of Science

A comprehensive evaluation of roll and yaw stability of tractor/semitrailers with single and dual trailers in city and highway conditions is conducted. Commercial vehicles fundamentally behave differently in city driving conditions than at high speeds during highway driving conditions. In order to closely examine each, this study offers two distinct evaluations of commercial vehicles: 1) low-speed driving in tight turns, representative of city driving; and 2) high-speed lane change and evasive maneuvers, typical of highway driving. Specifically, for city driving, the geometric parameters of the roadway in places where tight turns occur—such as in roundabouts—are closely examined in a simulation study in order to evaluate the elements that could cause large vehicle body lean (or high rollover index), besides the truck elements that have most often been studied. Two roundabout geometries, 140-ft single-lane and a 180-ft double-lane, are examined for various truck load conditions and configurations. The vehicle configurations that are considered are a straight 4x2 truck, a tractor with a 53-ft semi-trailer (commonly known as WB-67), and two trucks in double-trailer configurations. Five potential factors are identified and thoroughly studied: circulatory roadway cross-section, roundabout tilt, truck configurations, truck apron geometry, and truck load condition. The results of the study indicate that when the rear axles of the trailer encounter the truck apron in the roundabout, the climbing and disembarking action can cause wheel unloading on the opposite side, therefore significantly increasing the risk of rollover. Interestingly, in contrast to most high-speed rollovers that happen with fully-loaded trailers, at low speeds, the highest risks are associated with lightly loaded or unloaded trucks. For high-speed driving conditions, typical of highway driving, a semi-truck with a double 28-ft trailer configuration is considered, mainly due to its increasing use on U.S. roads. The effect of active safety systems for commercial vehicles, namely Roll Stability Control (RSC) for trailers and Electronic Stability Control (ESC) for the tractor, is closely examined in a test study. Various trailer loading possibilities are evaluated for different combinations of ESC/RSC on the tractor and trailer, respectively. The results of the study indicate that 1) RSC systems reduce the risk of high-speed rollovers in both front and rear trailers, 2) the combination of ESC (on tractor) and RSC (on trailer) reduce the risk of rollover and jackknifing, and 3) RSC systems perform less effectively when the rear trailer is empty. / PHD