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Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Print out. Additional advisors: James J. Buckley, Jeffrey G. Gray, Robert M. Hyatt, Randy K. Smith. Includes bibliographical references (leaves 185-207). Also available via the World Wide Web.

Thesis (Ph. D. engineering science)--Washington State University, December 2007. / Includes bibliographical references (p. 181-191).

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Xiang-Gen Xia, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Rut formation can severely influence soil conditions and vegetation, and reduce vehicle mobility. Vehicle operations can affect rut formation. Ruts formed in straight vehicle paths are different than when the vehicle turns. This research is mainly to investigate the effects of vehicle turning maneuvers on soil rut formation, including field tests, lab tests, and model development. Field tests were conducted at Yuma Training Center, Fort Riley and Fort Lewis on wheeled and tracked military vehicles. In field tests, rut depth, rut width and rut index were used as the main indicators to quantify a rut. A Vehicle Tracking System was mounted onto each vehicle to utilize the Global Positioning System. The vehicles were operated in spiral patterns to get constantly decreasing turning radius. The Vehicle Terrain Interaction terrain mechanics model was chosen to modify to predict rut formation during vehicle turning operations on yielding soils. In the modified VTI model, the resultant force on a single wheel is a dynamic variable correlated with the vehicle’s weight, velocity, and turning radius. In addition, lab tests were conduced on a tire and a track shoe in sand. Lateral forces and lateral displacements were applied under constant normal forces. The tire was pulled laterally and the track shoe was pulled back and forth to represent actual movement during vehicle turning. Results indicate that (1) rut depth, rut width and rut index increase with the decrease of TR, especially when TR is less than 20 meters; (2) vehicle parameters and soil parameters are statistically significant to affect rut formation; (3) the modified VTI model is able to predict rut formation when turning, with an improved R square of 0.43; (4) in lab tests, the final sinkage caused by the lateral force or displacement is 3 to 5 times the static sinkage; (5) rut depths increase from 65% to 548% of the initial rut depths under the effects of the combination of the multi-pass and turning maneuvers after multiple passes. This dissertation is a collection of five individual papers. More detailed description of test procedures and conclusions are found in these papers.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Xiang-Gen Xia, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Xiang-Gen Xia, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Two problem solving strategies, forward chaining and backward chaining, were compared to see how they affect students' learning of geometry theorem proving with construction. It has been claimed that backward chaining is inappropriate for novice students due to its complexity. On the other hand, forward chaining may not be appropriate either for this particular task because it can explode combinatorially. In order to determine which strategy accelerates learning the most, an intelligent tutoring system was developed. It is unique in two ways: (1) It has a fine grained cognitive model of proof-writing, which captured both observable and unobservable inference steps. This allows the tutor to provide elaborate scaffolding. (2) Depending on the student's competence, the tutor provides a variety of scaffolding from showing precise steps to just prompting students for a next step. In other words, the students could learn proof-writing through both worked-out examples (by observing a model of proof-writing generated by the tutor) and problem solving (by writing proofs by themselves). 52 students were randomly assigned to one of the tutoring systems. They solved 11 geometry proof problems with and without construction with the aid from the intelligent tutor. The results show that (1) the students who learned forward chaining showed better performance on proof-writing than those who learned backward chaining, (2) both forward and backward chaining conditions wrote wrong proofs equally frequently, (3) both forward and backward chaining conditions seldom wrote redundant or wrong statements when they wrote correct proofs, (4) the major reason for the difficulty in applying backward chaining lay in the assertion of premises as unjustified propositions (i.e., subgoaling). These results provide theoretical implications for the design of tutoring systems for problem solving.

A novel decision-theoretic architecture for intelligent tutoring systems, DT Tutor (DT), was fleshed out into a complete ITS and evaluated. DT uses a dynamic decision network to probabilistically look ahead to anticipate how its tutorial actions will influence the student and other aspects of the tutorial state. It weighs its preferences regarding multiple competing objectives by the probabilities that they will occur and then selects the tutorial action with maximum expected utility. The evaluation was conducted in two phases. First, logs were recorded from interactions of students with a Random Tutor (RT) that was identical to DT except that it selected randomly from relevant tutorial actions. The logs were used to learn many of DTs key probabilities for its model of the tutorial state. Second, the logs were replayed to record the actions that DT and a Fixed-Policy Tutor (FT) would select for a large sample of scenarios. FT was identical to DT except that it selected tutorial actions by emulating the fixed policies of Cognitive Tutors, which are theoretically based, widely used, and highly effective. The possible action selections for each scenario were rated by a panel of judges who were skilled human tutors. The main hypotheses tested were that DTs action selections would be rated higher than FTs and higher than RTs. This was the first comparison of a decision-theoretic tutor with a non-trivial competitor. DT was rated higher than FT overall and for all subsets of scenarios except help requests, for which it was rated equally. DT was also rated much higher than RT. The judges preferred that the tutors provide proactive help and the study design permitted this information to be put to use right away to develop and evaluate enhanced versions of DT and FT. The enhanced versions of DT and FT were rated about equally and higher than non-enhanced DT except on help requests. The variability of the actions selected by both non-enhanced and enhanced versions of DT demonstrated more sensitivity to the tutorial state than the actions selected by non-enhanced and enhanced versions of FT.

Advanced display design, such as Ecological Interface Design (EID), makes extensive use of complex graphical objects. Research has shown that by following EID methodologies, supervisory operators have better performance with the EID displays (Pawlak and Vicente, 1996). However, past research does not consider the visual aspects of the graphical objects used in EID. Of particular interest is how different design decisions of graphical objects affect the performance of the objects used within that design. This thesis examines the visual sensitivity of dynamic graphical objects by examining features that make certain graphical objects visually superior for certain monitoring tasks. Previous research into the visual aspects of supervisory control with respect to emergent features, psychophysics and attention were considered in the investigation of the visual sensitivities of the dynamic graphical objects used. Research into static graphical objects, combined with prior work on emergent features has been merged to find emergent features that best show changes in dynamic graphical objects for the monitoring tasks investigated. It was found that for simple dynamic objects such as bars and polygon objects, a line changing in angle was the most noticeable emergent feature to show a departure from ?normal? state. For complex graphical objects, those target-indicator displays that mimic a ?bull?s eye? when at the target value should be used for displays that show observers when a target value has been reached. Abrupt changes in shape should be used in trend meters to show when variables or processes have changed direction. Finally, ?solid objects? that make use of vertical lines and shading should be used for comparison meters that compare two values and keep them in a particular ratio. These findings provide guidance for designers of dynamic advanced graphical displays by encouraging the consideration of visual aspects of graphical objects, as well as prescribing graphical objects that should be used in the types of tasks investigated.