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Indiana University-Purdue University Indianapolis (IUPUI) / Road accident reconstruction and simulation investigates the accident causes, suggests improvements in vehicle design and investigates failures in vehicle control and safety systems such as the anti-lock brake system (ABS) and air-bag deployment. This thesis focuses on analysis of crash data from vehicles not equipped with collision warning systems. Vehicle parameters before and during an accident can be recorded using an Event Data Recorder (EDR) which helps in reconstructing an accident. This tool, installed in the vehicle, records different crash parameters like vehicle speed, lateral and longitudinal acceleration, seat-belt status, and air-bag deployment over a period that spens the accident. This thesis focuses on accident reconstruction with and without EDR data. A simulation software tool called HVE is used to visually recreate the reconstructed accidents. HVE is a platform to execute different accident simulation methods which are used for specific types of simulations. Two such simulation methods, EDSMAC4 and EDHIS, are discussed in this thesis. The former is an important method for vehicle-to-vehicle collisions and the latter is used for analysis of human behavior involved in the accident. Three real-life accidents were chosen for reconstruction and simulation. They were Bus and Car accident, Three Vehicle accident and Intersection accident. These particular accidents were chosen to represent a diverse selection of accidents based on the following parameters: the locations of the accidents, the vehicles involved in each accident, and the data available. A qualitative analysis of vehicle occupant's behavior is also presented for one of the three accidents. The thesis discusses in detail the reconstruction of these three accidents. Throughout these simulations, the thesis illustrates the advantages and limitations of the EDR and HVE simulation software for accident reconstruction and simulation.

A research report submitted in partial fulfillment of the requirements for the degree of Master of Science in School of Computer Science and Applied Mathematics to the Faculty of Science University of Witwatersrand, 2019 / Learning-based methods have recently become popular in control engineering, achieving good performance on a number of challenging tasks. However, in complex environments where data efficiency and safety are critical, current methods remain unsatisfactory. As a step toward addressing these shortcomings, we propose a learning-based approach that combines Gaussian process regression with model predictive control. Using sparse spectrum Gaussian processes, we extend previous work by learning a model of the dynamics incrementally from a stream ofsensory data. Utilizinglearned dynamics and model uncertainty, we develop a controller that can learn and plan in real-time under non-linear constraints. We test our approach on pendulum and cartpole swing up problems and demonstrate the benefits of learning on a challenging autonomous racing task. Additionally, we show that learned dynamics models can be transferred to new tasks without any additional training. / TL (2020)

A crucial component of instructional design for simulation-based training systems involves optimizing the presentation of complex material in order to maximize knowledge acquisition and application. One approach toward facilitating the learning of this complex information is to instantiate instructional strategies within the training systems themselves. However, there are few established guidelines in place which are meant specifically for real-time guidance strategies within simulation-based environments. Consequently, this study aims to apply findings from the literature on instructional information presentation to drive decisions for how to most effectively provide real-time feedback during training of simulated decision-making tasks. Research has shown that presenting text information in an auditory mode during direct instruction of operational tasks enhances learning and reduces the probability of learners experiencing cognitive overload. Similar effects have been found regarding the presentation modality of feedback during operational tasks. In the current study, this principle was extended by comparing text versus verbal real-time feedback presentation during learning of higher-level cognitive skills in a virtual environment. Participants were instructed on how to perform a simulated decisionmaking task, while receiving text, verbal or no instructional feedback in real-time, based on their performance. Participants then completed an assessment scenario in which no feedback was provided to any group. It was hypothesized that a linear relationship would exist across each of the three conditions, with the verbal group making the best decisions, followed by the text group, and then by the control group. Additionally, reduced cognitive load was expected throughout the instructional process for those receiving verbal feedback prompts compared to those receiving text prompts and the control. Analyses revealed several significant linear trends across iv conditions regarding measures of knowledge acquisition and application. The results provide support for the hypothesis that verbal real-time feedback is more effective than text during training of primarily visual tasks for the acquisition of higher-order cognitive skills such as decision making. There were no significant linear trends regarding the amount of cognitive load subjectively reported during training and assessment. The results of this study indicate that instructional systems intended to train primarily visual tasks should present real-time feedback in verbal rather than text form.

Any product sold is expected to be reliable and available when the customer wants to operate it. Companies that produce large capital products (LCP), such as rockets, satellites, or large gas turbines to generate electrical energy, tend to shy away from extending their testing and validation method above the requirements by law, mainly due to the very high costs of each additional test and the uncertain return on investment. This research shows that today’s state of the art validation methods for LCP, required by law, or suggested in literature, and adapted by these industries, are not capable of capturing all significant failure modes (or even enough failure modes), with the consequence that the subsequently sold commercial product will still experience failures with significant effects on product reliability, and subsequently on the companies’ bottom line earnings projections. The research determines the type of data (significant variables) necessary to correlate a company’s validation policy to product failures after commercialization, and predicts the financial impact of the current validation policy on the company’s profitability. An optimized validation plan and testing policy is suggested, and its impact on a company’s profitability is demonstrated through simulation. A generic methodology is derived and its viability is illustrated using a specific product and a dynamic model developed with data available to the researcher. The generic method can be applied by any company to develop its own model for optimizing product reliability prior to market introduction. iv

This dissertation investigated the impact of active stereoscopic 3-dimensional (3D) imagery equipment and individual differences in visuo-spatial working memory (VSWM) capacity on retention of a set of similar, novel objects (i.e., armored military vehicles). Seventy-one participants were assessed on their visuo-spatial working memory using the Visual Patterns Test (Della Sala, Gray, Baddeley, & Wilson, 1997). They were then assigned to one of four different conditions (3D high VSWM, 3D low VSWM, 2D high VSWM, 2D low VSWM) based upon their visuo-spatial working memory. Participants were then trained to identify military vehicles using a simulation that presented the training stimuli in one of two dimensionalities, i.e. two dimensional (2D) or active stereoscopic three-dimensional (3D). Testing consisted of a vehicle memory training assessment, which challenged participants to choose the correct components of each vehicle immediately after studying; a measure of retention for military vehicles which asked participants to categorize the alliance and identify previously studied vehicles; and a transfer measure using video footage of actual military vehicles. The latter measures depicted military vehicles in an array of combat situations, and participants were asked to decide on whether or not to shoot each vehicle, as well as identify the vehicles. Testing occurred immediately after training. The moderating, as well as main effects, of VSWM were assessed. The mediating/moderating effects of several experiential factors were measured as well, including: immersion, presence, engagement, flow state, and technology acceptance. Findings indicate that perceptions of the simulation experience and VSWM are strong positive predictors of performance, while 3D was not predictive, and in some instances, significantly worse than the 2D condition. These findings indicate that individual differences in visual memory and user experiences during the SBT both are predictive factors in memory tasks iv for confusable objects. The SBT designed in this study also led to robust prediction of training outcomes on the final transfer task.

Information security has become a major challenge for all private and public organizations. The protection of proprietary and secret data and the proper awareness of what is entailed in protecting this data are necessary in all organizations. This treatise examines how simulation and training would influence information security awareness over time in virtual communities of practice under a variety of security threats. The hypothesis of the study was that security-trained members of a virtual community of practice would respond significantly better to routine security processes and attempts to breach security or to violate the security policy of their organization or of their virtual community of practice. Deterrence theory was used as the grounded theory and integrated in the information security awareness training with simulated scenarios. The study provided training with simulated scenarios and then tested the users of a virtual community of practice over an approximately twelve-week period to see if the planned security awareness training with simulated security problem scenarios would be effective in improving their responses to the follow-up tests. The research subjects were divided into four groups, the experimental group and three control groups. The experimental group received all of the training and testing events throughout the twelve-week period. The three control groups received various portions of the training and testing. The data from all of the tests were analyzed using the Kruskal-Wallis iv ranked order test, and it was determined that there was no significant difference between the groups at the end of the data collection. Even though the null hypothesis, which stated that there would be no difference between the groups scores on the information security awareness tests, was not rejected, the groups that received the initial training with the simulated scenarios did perform slightly better from the pre-training test to the post-training test when compared with the control group that did not receive the initial training. More research is suggested to determine how information security awareness training with simulated scenarios and follow-up testing can be used to improve and sustain the security practices of members of virtual communities of practice. Specifically, additional research could include: comparing the effect of training with the simulated scenarios and with training that would not use the simulated security scenarios; the potential benefits of using adaptive and intelligent training to focus on the individual subjects’ weaknesses and strengths; the length of the training with simulated scenarios events, the time between each training event, and the overall length of the training; the demographics of the groups used in the training, and how different user characteristics impact the efficacy of the training with simulated scenarios and testing; and lastly examining how increasing the fidelity of the simulated scenarios might impact the results of the follow-up tests.

There are many different training interventions that can be used in simulation based training systems (e.g., cueing, hinting, highlighting, deliberate practice, etc.). However, the most widely used training intervention in the military is feedback, most often presented in the form of a debrief. With advances in technology, it is possible to measure and diagnose performance in real-time. Thus it is possible to provide immediate feedback during scenarios. However, training systems designers should not consider the timing of feedback in isolation. There are other parameters of feedback that must also be considered which may have an impact on performance. Specifically, feedback content and modality may also have an impact on the appropriate timing of feedback and its’ effectiveness in simulation training environments. Moreno and Mayer (2000) propose a cognitive theory of multimedia learning which describes how instruction is perceived and processed by a trainee. Using this theoretical framework, I investigate the optimal use of feedback while considering the interaction of feedback timing, content, and modality in scenario-based training environments. In order to investigate the relationship between the timing, modality, and content of feedback, a 2 (immediate, delayed) X 2 (visual, auditory) X 2 (process, outcome) betweensubjects design was used (a no feedback control condition was also included). Ninety participants were randomly assigned to the nine experimental groups. These participants performed a visual-spatial military task called the Forward Observer PC-based Simulation. Results indicated that receiving feedback was beneficial to improve performance as compared to receiving no feedback. As hypothesized, during a visual-spatial task, auditory feedback presented during a scenario led to higher performance than visual feedback. Finally, iv while I did not support my hypothesis that an interaction between all three components of feedback would affect performance, it is promising that the pattern of results mirrored the hypothesized pattern. Theoretical and practical implications, as well as limitations of the current study and directions for future research are discussed.

This thesis estimates the ecosystem evaporation response to two forms of aridity: (1) atmospheric aridity in the form of vapor pressure deficit (Chapter 2), and (2) soil moisture aridity (Chapter 4). We also develop new methods to estimate the ecosystem response to aridity. For the response to atmospheric aridity (Chapter 2), we build a theoretical framework that allows us to derive an analytical expression for the ecosystem evaporation response to vapor pressure deficit with all environmental conditions held fixed (Chapter 2). For the response to soil moisture aridity, we develop a method to estimate the response of evaporation to interventions on soil moisture using only passive data generated in a simulated reality (Chapter 4). To guide the development of this data-driven approach, we review and analyze causal inference’s applications in the Earth system, considering generic scenarios that are applicable to the land-atmosphere system as well as many other subdomains of the Earth system (Chapter 3). The subsections below elaborate more on the contents of each Chapter. Chapter 2: When does vapor pressure deficit drive or reduce evaporation? Increasingvapor pressure deficit increases atmospheric demand for water. While increased evaporation in response to increased atmospheric demand seems intuitive, plants are capable of reducing evaporation in response to increased vapor pressure deficit by closing their stomata. We examine which effect dominates the response to increasing vapor pressure deficit: atmospheric demand and increases in evaporation, or plant response (stomata closure) and decreases in evaporation. We use Penman-Monteith, combined with semi-empirical optimal stomatal regulation theory and underlying water use efficiency, to develop a theoretical framework for assessing evaporation response to vapor pressure deficit. The theory suggests that depending on the environment and plant characteristics, evaporation response to increasing vapor pressure deficit can vary from strongly decreasing to increasing, highlighting the diversity of plant water regulation strategies. The evaporation response varies due to: 1) climate, with tropical and temperate climates more likely to exhibit a positive evaporation response to increasing vapor pressure deficit than boreal and arctic climates; 2) photosynthesis strategy, with C3 plants more likely to exhibit a positive evaporation response than C4 plants; and 3) plant type, with crops more likely to exhibit a positive evaporation response, and shrubs and gymniosperm trees more likely to exhibit a negative evaporation response. These results, derived from previous literature connecting plant parameters to plant and climate characteristics, highlight the utility of our simplified framework for understanding complex land atmosphere systems in terms of idealized scenarios in which evaporation responds to vapor pressure deficit only. This response is otherwise challenging to assess in an environment where many processes co-evolve together. Chapter 3: Causal inference for process understanding in Earth sciences There is growinginterest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships and causal structure from data. This paper instead examines causality through the lens of causal inference and how expert-defined causal graphs, a fundamental from causal theory, can be used to clarify assumptions, identify tractable problems, and aid interpretation of results and their causality in Earth science research. We apply causal theory to generic graphs of the Earth system to identify where causal inference may be most tractable and useful to address problems in Earth science, and avoid potentially incorrect conclusions. Specifically, causal inference may be useful when: (1) the effect of interest is only causally affected by the observed portion of the state space; or: (2) the cause of interest can be assumed to be independent of the evolution of the system’s state; or: (3) the state space of the system is reconstructable from lagged observations of the system. However, we also highlight through examples that causal graphs can be used to explicitly define and communicate assumptions and hypotheses, and help to structure analyses, even if causal inference is ultimately challenging given the data availability, limitations and uncertainties. Chapter 4: Estimating the ecosystem evaporation response to interventions on soilmoisture: confounding and causal modeling in a simulated world We build a simulated reality using a numerical model designed to represent feedbacks in the land atmosphere system, and observational boundary conditions that are confounded by the real world’s underlying climate state. Although no simulation can reproduce the real land-atmosphere system’s complexity and any simulation’s predictions will deviate from the real world, this simulated reality does share the same characteristics of the real world that make causal inference challenging: it contains feedbacks, non-linearity, and the real world’s confounding-induced covariations between boundary conditions. We use this simulated reality to estimate confounding’s impact on relationships between soil moisture and ecosystem evaporation, and also to validate a method for calculating ecosystem evaporation response to interventions on soil moisture from passive observations. We repeat this analysis at 12 sites spanning a range of humid and arid climates in western North American and Europe, and find that: • Confounding bias is largest at the more humid sites, and lower at the more arid sites where soil moisture limits evaporation and decouples the response from other environmental factors (Section 4.3.1). • At the more humid sites, bias due to confounding is of a larger magnitude than model specification bias, even when the specified model is a linear model applied to a known non-linear process. This highlights the importance of accounting for confounding. (Section4.3.1). • Statistically adjusting for potential sources of confounding improves causal estimates at the highly confounded sites without degrading causal estimates at arid, soil moisture-limited sites characterized by less confounding bias (Section 4.3.2). • The estimated causal effects appear to differentiate true variations in the causal effects across climates and ecosystems. (Section 4.3.2).

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