Now showing items 33062-33081 of 47593

  • Robot Reliability Through Fuzzy Markov Models 

    Leuschen, Martin L. (1997-04-20)
    In the past few years, new applications of robots have increased the importance of robotic reliability and fault tolerance. Standard approaches of reliability engineering rely on the probability model, which is often ...
  • Robot reliability through fuzzy Markov models 

    Leuschen, Martin Leslie (1997)
    In the past few years, new applications of robots have increased the importance of robotic reliability and fault tolerance. Standard approaches of reliability engineering rely on the probability model, which is often ...
  • Robot Reliability Through Fuzzy Markov Models 

    Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R. (1998-01-01)
    In the past few years, new applications of robots have increased the importance of robotic reliability and fault tolerance. Standard approaches of reliability engineering rely on the probability model, which is often ...
  • Robot Reliability Using Fuzzy Fault Trees and Markov Models 

    Leuschen, Martin L.; Walker, Ian D.; Cavallaro, Joseph R. (1996-11-01)
    Robot reliability has become an increasingly important issue in the last few years, in part due to the increased application of robots in hazardous and unstructured environments. However, much of this work leads to complex ...
  • Robotic Fault Detection Using Nonlinear Analytical Redundancy 

    Leuschen, Martin L.; Cavallaro, Joseph R.; Walker, Ian D. (2002-05-01)
    In this paper we discuss the application of our recently developed nonlinear analytical redundancy (NLAR) fault detection technique to a two-degree of freedom robot manipulator. NLAR extends the traditional linear AR ...
  • Robotic Fault Tolerance: Algorithms and Architectures 

    Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D. (1993-04-01)
    Fault tolerance is an essential factor in ensuring successful autonomous systems, especially for robots working in remote or hazardous environments. To avoid the cost and risk involved in sending humans into these environs ...
  • Robotic path planning and obstacle avoidance: A neural network approach 

    Norwood, John David (1989)
    Robotic path planning and obstacle avoidance has been the subject of intensive research in recent years. Most solutions to this problem have been reached through the use of traditional Artificial Intelligence search ...
  • A robust composite time integration scheme for snap-through problems 

    Chandra, Yenny; Zhou, Yang; Stanciulescu, Ilinca; Eason, Thomas; Spottswood, Stephen (2015)
    A robust time integration scheme for snap-through buckling of shallow arches is proposed. The algorithm is a composite method that consists of three sub-steps. Numerical damping is introduced to the system by employing an ...
  • Robust constrained optimization approach to control design for International Space Station centrifuge rotor auto balancing control system 

    Postma, Barry Dirk (2005)
    This thesis discusses application of a robust constrained optimization approach to control design to develop an Auto Balancing Controller (ABC) for a centrifuge rotor to be implemented on the International Space Station. ...
  • Robust continuous-time detection in linear process noise 

    Srinivasa, Patibandla Rao (1992)
    Linear processes are suitable for modeling the random received waveforms in a scattering channel, which represents radar, sonar and multipath communication channels. We address the continuous-time detection problem where ...
  • ROBUST CONTROL OF LINEAR SYSTEMS 

    STAATS, PRESTON WILLIAM, JR. (1974)
  • Robust Distributed Estimation in Sensor Networks using the Embedded Polygons Algorithm 

    Delouille, Veronique; Neelamani, Ramesh; Baraniuk, Richard G. (2004-04-01)
    We propose a new iterative distributed algorithm for linear minimum mean-squared-error (LMMSE) estimation in sensor networks whose measurements follow a Gaussian hidden Markov graphical model with cycles. The <i>embedded ...
  • Robust Distributed Estimation Using the Embedded Subgraphs Algorithm 

    Delouille, Veronique; Neelamani, Ramesh; Baraniuk, Richard G. (2006-08-01)
    We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a ...
  • Robust empirical likelihood 

    Glenn, Nancy Louise (2002)
    This research introduces a new nonparametric technique: robust empirical likelihood. Robust empirical likelihood employs the empirical likelihood method to compute robust parameter estimates and confidence intervals. The ...
  • Robust fitting of a Weibull model with optional censoring 

    Yang, Jingjing; Scott, David W. (2013)
    The Weibull family is widely used to model failure data, or lifetime data, although the classical two-parameter Weibull distribution is limited to positive data and monotone failure rate. The parameters of the Weibull model ...
  • Robust GARCH methods and analysis of partial least squares regression 

    Egbulefu, Joseph (2014-04-24)
    New approaches to modeling volatility are evaluated and properties of partial least squares (PLS) regression are investigated. Common methods for modeling volatility, the standard deviation of price changes over a period, ...
  • Robust Helical Edge Transport in Gated InAs/GaSb Bilayers 

    Du, Lingjie; Knez, Ivan; Sullivan, Gerard; Du, Rui-Rui (2015)
    We have engineered electron-hole bilayers of inverted InAs/GaSb quantum wells, using dilute silicon impurity doping to suppress residual bulk conductance. We have observed robust helical edge states with wide conductance ...
  • Robust methods tailored for non-Gaussian narrowband array processing 

    Williams, Douglas Bennett (1989)
    Array processing algorithms generally assume that the received signal, composed of both narrowband signals and noise, is Gaussian, which is not true in general. In the context of the narrowband array processing problem, ...
  • Robust model predictive control as a class of semi-infinite programming problems 

    Kassmann, Dean Edward (1999)
    This thesis introduces a new interpretation of the problems arising in robust model predictive control (MPC). In practice, MPC algorithms are typically embedded within a multi-level hierarchy of control functions. The MPC ...
  • Robust model predictive control for nonlinear systems based on Volterra series 

    Lu, Shijiang (2001)
    In this thesis we develop a Nonlinear Robust Model Predictive Control (NRMPC) algorithm for nonlinear plants modeled by second order Volterra series. Robust stability is achieved through the addition of cost function ...