7,505 research outputs found

    Letter from Cy Donner to Michi Weglyn, June 2, 1967

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    A letter from Cy Donner to Michi Weglyn encouraging her to come out to California to talk to producers about two shows called "Youthquake" and "Pretty Talk".These materials are from box 73 and 74 of the Frank Chin Papers. The Frank Chin Papers contain personal and professional correspondence between Frank Chin and Michi Weglyn relating to particular projects on which either author was working as well as files related to the Day of Remembrance Tribute to Michi Weglyn

    CY; Family and future

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    The main purpose of this project is to interview CY students and their families in order to find out the condition for the life of CY students and the relationship with their families along with their CY life. It is very easy to find out how newspapers and other media think about CY students. When I was still in Korea, I could hear about students who go to IVY leagues and good colleges. These students had strong thought to prolong and continue their dream through study abroad. However, sometimes, we hear about students who are having troubles through their CY life. When I was reading this news article, it looked like there are a lot of temptations for students to fall into bad pathways. People assume what would be like for young students to live as a CY student, but we never know. Because of that, I wanted to do this research. I wanted find out what factors drive them to come to United States, and what forced them to prolong this CY life. These interview questions are formed to find out the reality of CY life and focused on how family affect this young student’s CY life.Submitted by Tim McDonough ([email protected]) on 2008-06-04T19:51:49Z No. of bitstreams: 1 ResearchProcess.doc: 67072 bytes, checksum: a6b99c3c558c94438565dc8dee559dbb (MD5)Made available in DSpace on 2008-06-04T19:51:49Z (GMT). No. of bitstreams: 1 ResearchProcess.doc: 67072 bytes, checksum: a6b99c3c558c94438565dc8dee559dbb (MD5) Previous issue date: 2008unpublishe

    Multiple neuro-adaptive control of robot manipulators using visual cues

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    A new adaptive controller based on multiple neural networks (NNs) for an uncertain robot manipulator system is developed in this paper. The proposed multiple neuro-adaptive controller (MNAC) switches to a memorized control skill or blends multiple skills by using visual information on the given job to improve the transient response at the time of task variation like a change of manipulating object. MNAC is a type of adaptive feedback controller where system nonlinearity terms are approximated with multiple NNs. The proposed controller is effective for a job where some tasks are repeated but information on the load cannot be scheduled before the operation. During the learning phase, MNAC memorizes a control skill for each load with each NN. For a new task, most similar existing control skills may be used as a starting point of adaptation, which improves the performance of learning. Lyapunov-function-based design of MNAC guarantees the stability of the closed-loop system to be independent of switching or blending law. Simulation results on a two-link manipulator for changing the mass of the given load were illustrated to show the effectiveness of the proposed control scheme by comparison with the conventional neuro-adaptive controller

    Adaptive control for uncertain nonlinear systems based on multiple neural networks

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    A new adaptive multiple neural network controller (AMNNC) with a supervisory controller for a class of uncertain nonlinear dynamic systems was developed in this paper. The AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks was used to approximate system nonlinearity for the given task. Each neural network represents the system dynamics for each task. For a job where some tasks are repeated but information on the load is not defined and unknown or varying, the proposed controller is effective because of its capability to memorize control skill for each task with each neural network. For a new task, most similar existing control skills may be used as a starting point of adaptation. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. Simulation results on a cartpole system for the changing mass of the pole were illustrated to show the effectiveness of the proposed control scheme for the comparison with the conventional adaptive neural network controller (ANNC)
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