8,832 research outputs found
Efficient Localization of Discontinuities in Complex Computational Simulations
Surrogate models for computational simulations are input-output
approximations that allow computationally intensive analyses, such as
uncertainty propagation and inference, to be performed efficiently. When a
simulation output does not depend smoothly on its inputs, the error and
convergence rate of many approximation methods deteriorate substantially. This
paper details a method for efficiently localizing discontinuities in the input
parameter domain, so that the model output can be approximated as a piecewise
smooth function. The approach comprises an initialization phase, which uses
polynomial annihilation to assign function values to different regions and thus
seed an automated labeling procedure, followed by a refinement phase that
adaptively updates a kernel support vector machine representation of the
separating surface via active learning. The overall approach avoids structured
grids and exploits any available simplicity in the geometry of the separating
surface, thus reducing the number of model evaluations required to localize the
discontinuity. The method is illustrated on examples of up to eleven
dimensions, including algebraic models and ODE/PDE systems, and demonstrates
improved scaling and efficiency over other discontinuity localization
approaches
Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels
The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE
InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference
Single-particle tracking (SPT) grants unprecedented insight into cellular
function at the molecular scale [1]. Throughout the cell, the movement of
single-molecules is generally heterogeneous and complex. Hence, there is an
imperative to understand the multi-scale nature of single-molecule dynamics in
biological systems. We have previously shown that with high-density SPT,
spatial maps of the parameters that dictate molecule motion can be generated to
intricately describe cellular environments [2,3,4]. To date, however, there
exist no publically available tools that reconcile trajectory data to generate
the aforementioned maps. We address this void in the SPT community with
InferenceMAP: an interactive software package that uses a powerful Bayesian
method to map the dynamic cellular space experienced by individual
biomolecules.Comment: 56 page
Likelihood Consensus and Its Application to Distributed Particle Filtering
We consider distributed state estimation in a wireless sensor network without
a fusion center. Each sensor performs a global estimation task---based on the
past and current measurements of all sensors---using only local processing and
local communications with its neighbors. In this estimation task, the joint
(all-sensors) likelihood function (JLF) plays a central role as it epitomizes
the measurements of all sensors. We propose a distributed method for computing,
at each sensor, an approximation of the JLF by means of consensus algorithms.
This "likelihood consensus" method is applicable if the local likelihood
functions of the various sensors (viewed as conditional probability density
functions of the local measurements) belong to the exponential family of
distributions. We then use the likelihood consensus method to implement a
distributed particle filter and a distributed Gaussian particle filter. Each
sensor runs a local particle filter, or a local Gaussian particle filter, that
computes a global state estimate. The weight update in each local (Gaussian)
particle filter employs the JLF, which is obtained through the likelihood
consensus scheme. For the distributed Gaussian particle filter, the number of
particles can be significantly reduced by means of an additional consensus
scheme. Simulation results are presented to assess the performance of the
proposed distributed particle filters for a multiple target tracking problem
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
Variable neural networks for adaptive control of nonlinear systems
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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