A discrete event simulation model for unstructured supervisory control of unmanned vehicles

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 33).Most current Unmanned Vehicle (UV) systems consist of teams of operators controlling a single UV. Technological advances will likely lead to the inversion of this ratio, and automation of low level tasking. These advances will also lead to a growth in UV use in large-scale applications such as urban search and rescue, which will require the use of both teams of operators and teams of UVs. This growth will in turn require research and development in the area of team supervisory control of multiple UVs. Human-in-the- loop experimentation is often used during this research but can be time consuming and expensive. The time and cost of experimentation can often be drastically reduced by using predictive models. However there is a lack of such models in the area of multiple-operator supervisory control of multiple- UVs. This problem is addressed in this thesis through the following method: First, current predictive models of human supervisory control of UVs are analyzed, and attributes of systems related to this modeling space are identified. Second, a queuing-based multiple-operator multiple-vehicle discrete event simulation model (MO-MUVDES) is developed which captures these attributes, including the ability to predict performance in situations with low observable exogenous event arrivals. MO-MUVDES also incorporates traditional system variables such as level of vehicle autonomy, vehicle and operator team structure, and operator switching strategy. The accuracy and robustness of the MO-MUVDES model were measured by a two-stage validation process using data from a human-in-the-loop supervisory control experiment, and a Monte Carlo simulation. The first stage of the validation process used data from the experiment as input for the MOMUVDES model which was then used to generate predictions of operator performance. In the second stage of validation, a sensitivity analysis was performed on the MO-MUVDES model. This validation process achieved confidence in the model's ability to predict operator performance and a measurement of the robustness of the model under varying input conditions. Additionally, the process indicated that discrete event simulation is an effective technique for modeling team supervisory control of UVs in a situation where exogenous event arrivals are not clearly observable. As a result, the MO-MUVDES model could be used to reduce development time for systems within its modeled space.by Anthony D. McDonald.S.B

An exploratory study to determine students' perceptions of the value of interaction in an Australian classroom context and the perceived impact on learning outcomes

Interaction has long been a defining and critical component of the educational process, whatever the classroom context (Anderson 2003). This paper presents findings of a study to explore the attitudes of students at an Australian university towards various types interactivity in the classroom. The study also investigates students perceptions of how interactivity in the classroom impacts on cognitive, affective and behavioural learning outcomes. In a recent review of the literature Muirhead & Juwah (2003) argue that interactivity is critical in underpinning the learning process in face-to-face, campus based and distance and online education. They say that interactions serve a diverse range of functions in the educational process, which include learner to learner, learner to content, learner to tutor, learner to technology, tutor to content, tutor to technology, content to content. These functions promote and enhance the quality of active, participative learning in a learning environment. However, literature indicates that attitudes towards active learning involving greater interactivity varies across students and between students and lecturers (Billings, Connors, & Skiba 2001). Investigation into student attitudes of the value and effectiveness of interaction is of particular interest for educators who are adapting the learning of a diverse range of students, including oncampus, distance, international, under and postgradute students. Much of the existing research into classroom interaction was grounded in the behaviourist and cognitive sciences approach to learning and teaching, where traditional classroom interaction placed the teacher at the centre of all activities as transmitter of knowledge and co-coordinator of student interaction (McLoughlin 2002). Those studies predate the recent application of constructivism (Bonk and Cunningham 1998) and social learning theory (Bandura (1977), and the emphasis on building life long learning skills. This research will contribute to current discussion about the role of interaction in learning, based on a constructivist approach to developing life long learning skills. This paper will present the findings of an exploratory study of students’ attitudes to various types of interaction in a classroom context. The first step of this exploratory study will employ a focus group approach to gather data from on campus students to identify the key issues that emerge from this data. These findings will be used to design a survey instrument to implement a follow-up research project

Proposal for a low cost close air support aircraft for the year 2000: The Raptor

The Raptor is a proposed low cost Close Air Support (CAS) aircraft for the U.S. Military. The Raptor incorporates a 'cranked arrow' wing planform, and uses canards instead of a traditional horizontal tail. The Raptor is designed to be capable of responsive delivery of effective ordnance in close proximity to friendly ground forces during the day, night, and 'under the weather' conditions. Details are presented of the Raptor's mission, configuration, performance, stability and control, ground support, manufacturing, and overall cost to permit engineering evaluation of the proposed design. A description of the design process and analysis methods used is also provided

Investigation of the Prebiotic Synthesis of Amino Acids and RNA Bases from CO2 using FeS/H2S as a Reducing Agent

An autotrophic theory of the origin of metabolism and life has been proposed in which carbon dioxide is reduced by ferrous sulfide and hydrogen sulfide by means of a reversed citric acid cycle, leading to the production of amino acids. Similar processes have been proposed for purine synthesis. Ferrous sulfide is a strong reducing agent in the presence of hydrogen sulfide and can produce hydrogen as well as reduce alkenes, alkynes, and thiols to saturated hydrocarbons and reduce ketones to thiols. However, the reduction of carbon dioxide has not been demonstrated. We show here that no amino acids, purines, or pyrimidines are produced from carbon dioxide with the ferrous sulfide and hydrogen sulfide system. Furthermore, this system does not produce amino acids from carboxylic acids by reductive amination and carboxylation. Thus, the proposed autotrophic theory, using carbon dioxide, ferrous sulfide, and hydrogen sulfide, lacks the robustness needed to be a geological process and is, therefore, unlikely to have played a role in the origin of metabolism or the origin of life

Investigation of the Prebiotic Synthesis of Amino Acids and RNA Bases from CO2 Using FeS/H2S As a Reducing Agent

An autotrophic theory of the origin of metabolism and life has been proposed in which carbon dioxide is reduced by ferrous sulfide and hydrogen sulfide by means of a reversed citric acid cycle, leading to the production of amino acids. Similar processes have been proposed for purine synthesis. Ferrous sulfide is a strong reducing agent in the presence of hydrogen sulfide and can produce hydrogen as well as reduce alkenes, alkynes, and thiols to saturated hydrocarbons and reduce ketones to thiols. However, the reduction of carbon dioxide has not been demonstrated. We show here that no amino acids, purities, or pyrimidines are produced from carbon dioxide with the ferrous sulfide and hydrogen sulfide system. Furthermore, this system does not produce amino acids from carboxylic acids by reductive amination and carboxylation. Thus, the proposed autotrophic theory, using carbon dioxide, ferrous sulfide, and hydrogen sulfide, lacks the robustness needed to be a geological process and is, therefore, unlikely to have played a role In the origin of metabolism or the origin of life

Does voter polarisation induce party extremism? The moderating role of abstention

This article contributes to the literature on representation by examining how the ideological polarisation of the electorate affects parties’ programmatic positions in multiparty systems. The main argument is that parties face incentives to adopt more extreme positions when the electorate becomes more ideologically polarised and the share of non-moderate voters is higher. The reason is that by adopting moderate positions parties will prompt their non-moderate core constituents to sit out the election. This risk is conditioned by voters’ propensity to abstain. A higher (lower) propensity to abstain means that parties alienate a larger (smaller) share of their core constituents when adopting a moderate position. Parties therefore respond to greater voter polarisation by adopting more extreme positions, but the effect declines as voters’ propensity to abstain decreases. An empirical analysis of parties’ programmatic positions in 11 Western European countries between 1977 and 2016 strongly supports this expectation

An active inference model of car following: Advantages and applications

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets

Detection of High Energy Ionizing Radiation using Deeply Depleted Graphene-Oxide-Semiconductor Junctions

Graphene's linear bandstructure and two-dimensional density of states provide an implicit advantage for sensing charge. Here, these advantages are leveraged in a deeply depleted graphene-oxide-semiconductor (D2GOS) junction detector architecture to sense carriers created by ionizing radiation. Specifically, the room temperature response of the silicon-based D2GOS junction is analyzed during irradiation with 20 MeV Si4+ ions. Detection was demonstrated for doses ranging from 12-1200 ions with device functionality maintained with no substantive degradation. To understand the device response, D2GOS pixels were characterized post-irradiation via a combination of electrical characterization, Raman spectroscopy, and photocurrent mapping. This combined characterization methodology underscores the lack of discernible damage caused by irradiation to the graphene while highlighting the nature of interactions between the incident ions and the silicon absorber.Comment: 15 pages, 4 figure