Thesis (M.Eng.(Mechanical Engineering))--University of Pretoria, 2004. / Summaries in Afrikaans and English. Includes bibliographical references (leaves 80-82).

Thesis (Ph. D.)--George Washington University, 1988. / Includes bibliographical references (leaves 280-295).

Double rotor machines, appearing in versatile forms and configurations thanks to the great flexibility of having a pair of rotors, are seen in a number of applications. Double rotor machines show promising prospect in the application of advanced hybrid electric vehicle powertrains due to the requirement of dual electro-mechanical ports in such systems. Integrating these powertrain systems with double rotor machines not only brings design freedom of laying out components, but also reduces number of parts and thus improves compactness. The switched reluctance type double rotor machines, offering unique characteristics of having a simple structure and no permanent magnets, are strong candidates for high performance applications. In this thesis, a family of double rotor switched reluctance machine with segmented rotors is proposed and studied. Compared to double rotor switched reluctance machines with a more conventional structure, the proposed designs exhibit potentials of achieving higher compactness and performance. A prototype double rotor machine of the segmented rotor design is constructed and tested to benchmark an existing double rotor switched reluctance machine. The experiment results show that the proposed design is able to achieve the same output with similar or higher efficiency than the benchmark machine, while occupying only about 60% of overall volume. The double segmented rotor switched reluctance machine demonstrates to be a promising double rotor topology and is worth further research. / Thesis / Master of Applied Science (MASc)

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Our motivation for this work is to develop an autonomous robot system that is able to perform autism intervention therapy. Autism spectrum disorder (ASD) is a common type of neurodevelopmental disorder that affects millions of people in the United States alone. The best way of treating ASD and help people with ASD learn new skills is through applied behavior analysis (ABA, i.e. autism intervention therapy). Because of the fact that people with ASD feel less stressful in a predictable and simple environment compared to interacting with other people and autism intervention therapy provided by professional therapists are generally expensive and inaccessible, it would be beneficial to build robots that can perform intervention therapy with children without a therapist/instructor present. In this research, we focus on the task of detecting engagement/disengagement levels of a child in a therapy session as a first step in designing a therapy robot. In this work, we mainly utilize an RGB-D camera, namely the Microsoft Kinect 2.0, to extract kinematic joint data from the therapy session. We also set up a child study with the Kid’s Creek therapy center to recruit children with ASD and record their interactions with a therapist while working on a touch-screen based game on a tablet. After carefully selecting features derived from skeletons’ movements and poses, we showed that our system can produce an accuracy of 97% when detecting engagements and disengagements using cross-validation assessment.

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