Design of a pitching and surging point absorber.

The linear dynamics, operation, and engineering aspects of P.S. FROG, a point absorber wave energy conversion buoy, are summarized. The device consists of a floating flap or paddle facing the waves and reacting against them through an interior moving mass in an enlarged section at the bottom of the buoy.

The wave transmission coefficients and coupling loss factors of point connected structures

This analysis is concerned with the calculation of the elastic wave transmission coefficients and coupling loss factors between an arbitrary number of structural components that are coupled at a point. A general approach to the problem is presented and it is demonstrated that the resulting coupling loss factors satisfy reciprocity. A key aspect of the method is the consideration of cylindrical waves in two-dimensional components, and this builds upon recent results regarding the energetics of diffuse wavefields when expressed in cylindrical coordinates. Specific details of the method are given for beam and thin plate components, and a number of examples are presented. © 2002 Acoustical Society of America.

Scaling POMDPs for dialog management with composite summary point-based value iteration (CSPBVI)

Although partially observable Markov decision processes (POMDPs) have shown great promise as a framework for dialog management in spoken dialog systems, important scalability issues remain. This paper tackles the problem of scaling slot-filling POMDP-based dialog managers to many slots with a novel technique called composite point-based value iteration (CSPBVI). CSPBVI creates a "local" POMDP policy for each slot; at runtime, each slot nominates an action and a heuristic chooses which action to take. Experiments in dialog simulation show that CSPBVI successfully scales POMDP-based dialog managers without compromising performance gains over baseline techniques and preserving robustness to errors in user model estimation. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.

Segmentation and recognition using structure from motion point clouds

We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our main contribution is to show how semantic segmentation is possible based solely on motion-derived 3D world structure. Our method works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors. Experiments were performed on a challenging new video database containing sequences filmed from a moving car in daylight and at dusk. The results confirm that indeed, accurate segmentation and recognition are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance. © 2008 Springer Berlin Heidelberg.

Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

An analysis of the cooperative mechano-sensitive feedback between intracellular signaling, focal adhesion development, and stress fiber contractility

Cells communicate with their external environment via focal adhesions and generate activation signals that in turn trigger the activity of the intracellular contractile machinery. These signals can be triggered by mechanical loading that gives rise to a cooperative feedback loop among signaling, focal adhesion formation, and cytoskeletal contractility, which in turn equilibrates with the applied mechanical loads. We devise a signaling model that couples stress fiber contractility and mechano-sensitive focal adhesion models to complete this above mentioned feedback loop. The signaling model is based on a biochemical pathway where IP3 molecules are generated when focal adhesions grow. These IP3 molecules diffuse through the cytosol leading to the opening of ion channels that disgorge Ca2+ from the endoplasmic reticulum leading to the activation of the actin/myosin contractile machinery. A simple numerical example is presented where a one-dimensional cell adhered to a rigid substrate is pulled at one end, and the evolution of the stress fiber activation signal, stress fiber concentrations, and focal adhesion distributions are investigated. We demonstrate that while it is sufficient to approximate the activation signal as spatially uniform due to the rapid diffusion of the IP3 through the cytosol, the level of the activation signal is sensitive to the rate of application of the mechanical loads. This suggests that ad hoc signaling models may not be able to capture the mechanical response of cells to a wide range of mechanical loading events. © 2011 American Society of Mechanical Engineers.

Application of the fixed point method for solution in time stepping finite element analysis using the inverse vector Jiles-Atherton model

An implementation of the inverse vector Jiles-Atherton model for the solution of non-linear hysteretic finite element problems is presented. The implementation applies the fixed point method with differential reluctivity values obtained from the Jiles-Atherton model. Differential reluctivities are usually computed using numerical differentiation, which is ill-posed and amplifies small perturbations causing large sudden increases or decreases of differential reluctivity values, which may cause numerical problems. A rule based algorithm for conditioning differential reluctivity values is presented. Unwanted perturbations on the computed differential reluctivity values are eliminated or reduced with the aim to guarantee convergence. Details of the algorithm are presented together with an evaluation of the algorithm by a numerical example. The algorithm is shown to guarantee convergence, although the rate of convergence depends on the choice of algorithm parameters. © 2011 IEEE.