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Filtering by: Creator Freilich, Michael H. Remove constraint Creator: Freilich, Michael H. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Scale effects on reinforced concrete beams strengthened for shear with discrete externally bonded carbon fiber-reinforced polymer U-wraps

    Creator:
    Dawson, Matthew Ryan
    Abstract:
    Current ACI design provisions for shear in reinforced concrete (RC) beams strengthened with externally bonded carbon fiber-reinforced polymer (CFRP) U-wraps may not adequately account for the effect of scale. This paper describes tests of 6 geometrically scaled beams to identify possible scale effects of such beams. Results indicated that effective...
    Resource Type:
    Masters Thesis
    Full Text:
    Specimens1 Six specimens at three scales (F = full, H = half, and Q = quarter scale) were used in a

  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    OF Michael S. Wynkoop for the degree of Masters of Science in Computer Science presented on June 9

  • Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models

    Creator:
    Anderson, Michael M.
    Abstract:
    Physical activity recognition using accelerometer data is a rapidly emerging field with many real-world applications. Much of the previous work in this area has assumed that the accelerometer data has already been segmented into pure activities, and the activity recognition task has been to classify these segments. In reality, activity...
    Resource Type:
    Masters Thesis
    Full Text:
    Change-Point Detection Algorithms and Hidden Markov Models AN ABSTRACT OF THE THESIS OF Michael M

  • Porting and optimizing a routing library for C* on the iPSC/860

    Creator:
    Uberoy, Chetan
    Abstract:
    High level data-parallel languages are easy to use and shield the programmer from machine specific details. A simple and efficient way of providing an interface to such languages is to develop a machine-independent compiler and a routing library, which isolates the low-level machine dependent communication functions, The compiler translates the...
    Resource Type:
    Capstone Project
    Full Text:
    requirements for the degree of Master of Science Major Professor: Dr. Michael J. Quinn Minor Professor

  • A C* development environment for the Meiko CS-2 multicomputer

    Creator:
    Zhang, Chung
    Abstract:
    With sequential computing technology reaching its speed limits, parallel processing is emerg­ing as the key to very-high-speed computation. However, developing a parallel program is by no means a simple task; neither is analyzing the performance of parallel programs. C* is a high-level data-parallel language that hides explicit message passing and...
    Resource Type:
    Capstone Project
    Full Text:
    Corvallis, OR 97331 zhangch@research. cs. orst. edu Major Professor: Dr. Michael J. Quinn Minor

  • Porting and optimizing a routing library for Dataparallel C on the nCUBE 2 system

    Creator:
    Sivaramapuram, Krishna
    Abstract:
    Dataparallel C has been designed for various kinds of architectures and was devel­oped jointly at University of New Hampshire and Oregon State University. This project dealt with porting the libraries to the nCUBE 2 system and helping a user compile his programs on nCUBE 2 directly from a Sun workstation...
    Resource Type:
    Capstone Project
    Full Text:
    Professor : Dr. Michael J. Quinn Minor Professor : Dr. Lawrence A. Crowl Committee Member : Dr. Tadepalli

  • Re-understanding Finite-State Representations of Recurrent Policy Networks

    Creator:
    Danesh, Mohamad H.
    Abstract:
    We propose an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minimization process can obscure a deeper understanding...
    Resource Type:
    Masters Thesis
    Full Text:
    H, a distinguished initial state h0 ∈ H, a finite set of observation symbols O, and a transition

  • A General Purpose Agent-Based Blackboard Optimization Method Applied to Sodium Fast Reactor Design and Analysis

    Creator:
    Stewart, Ryan H.
    Abstract:
    We present a novel multi-objective optimization methodology built upon a multi-agent blackboard framework. This multi-agent blackboard system (MABS) synthesizes blackboard architectures, multi-agent environments, and optimization theory. The blackboard architecture creates the framework for initializing, storing, and solving a multi-objective optimization problem. Multiple agents allow for an optimization problem to be...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Ryan H. Stewart for the

  • Update programming -- a safe approach to software maintenance

    Creator:
    Ren, Deling
    Abstract:
    Software maintenance accounts for a large portion of the software development cost, particularly the process of updating programs either to adapt for requirement change or to enhance design or efficiency. Currently, program updates are generally performed manually by programmers using text editors. This is an unreliable method because syntax and...
    Resource Type:
    Dissertation
    Full Text:
    <143 n�cZ3S58()*�sa3Sl.()+;W l.7;1y5jc=M;+47H5.L�$~$'p4$/3SGH587;(D*;+e…†H*;1qlºcZob1ZM,5 F,3N14

  • Optimal inference with local expressions

    Creator:
    Borujerdi, Mohammad
    Abstract:
    Probabilistic inference using Bayesian networks is now a well-established approach for reasoning under uncertainty. Among many e ciency-driven tech- niques which have been developed, the Optimal Factoring Problem (OFP) is distinguished for presenting a combinatorial optimization point of view on the problem. The contribution of this thesis is to extend...
    Resource Type:
    Dissertation
    Full Text:
    Approved by Committee: Major Professor (Bruce D'Ambrosio) Committee Member (Michael Quinn) Committee