311 research outputs found
Bayesian parameter estimation in the second LISA Pathfinder Mock Data Challenge
A main scientific output of the LISA Pathfinder mission is to provide a noise
model that can be extended to the future gravitational wave observatory, LISA.
The success of the mission depends thus upon a deep understanding of the
instrument, especially the ability to correctly determine the parameters of the
underlying noise model. In this work we estimate the parameters of a simplified
model of the LISA Technology Package (LTP) instrument. We describe the LTP by
means of a closed-loop model that is used to generate the data, both injected
signals and noise. Then, parameters are estimated using a Bayesian framework
and it is shown that this method reaches the optimal attainable error, the
Cramer-Rao bound. We also address an important issue for the mission: how to
efficiently combine the results of different experiments to obtain a unique set
of parameters describing the instrument.Comment: 14 pages, 4 figures, submitted to PR
The cross-entropy method for continuous multi-extremal optimization
In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints
Modeling of radiative - conductive heat transfer in compositing materials
A layer of composite material is investigated, which is heated one-sidedly with one-dimensional energy transfer accounting for thermal conductivity and radiation. A mathematical model is suggested for non-stationary coefficient thermophysical problem under radiative-conductive heat transfer in a material layer. Temperature dependencies of thermal capacity and thermal conductivity coefficient of composite radio-transparent material have been determined through numerical modeling by solving the coefficient reverse problem of thermal conductivity
In-line estimation of sulfur and nitrogen contents during hydrotreating of middle distillates
DOLOMITE DESULFURIZATION BEHAVIOR IN A BUBBLING FLUIDIZED BED PILOT PLANT FOR HIGH ASH COAL
Sub-National Revenue Mobilization in Latin America and Caribbean Countries: The Case of Venezuela
A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems
Physical systems can fail. For this reason the problem of identifying and
reacting to faults has received a large attention in the control and computer
science communities. In this paper we study the fault diagnosis problem for
hybrid systems from a game-theoretical point of view. A hybrid system is a
system mixing continuous and discrete behaviours that cannot be faithfully
modeled neither by using a formalism with continuous dynamics only nor by a
formalism including only discrete dynamics. We use the well known framework of
hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis
Game on them, using two players: the environment and the diagnoser. The
environment controls the evolution of the system and chooses whether and when a
fault occurs. The diagnoser observes the external behaviour of the system and
announces whether a fault has occurred or not. Existence of a winning strategy
for the diagnoser implies that faults can be detected correctly, while
computing such a winning strategy corresponds to implement a diagnoser for the
system. We will show how to determine the existence of a winning strategy, and
how to compute it, for some decidable classes of hybrid automata like o-minimal
hybrid automata.Comment: In Proceedings GandALF 2011, arXiv:1106.081
A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/ J data
This paper presents a new stochastic multidimensional scaling procedure for the analysis of three-mode, three-way pick any/ J data. The method provides either a vector or ideal-point model to represent the structure in such data, as well as “floating” model specifications (e.g., different vectors or ideal points for different choice settings), and various reparameterization options that allow the coordinates of ideal points, vectors, or stimuli to be functions of specified background variables. A maximum likelihood procedure is utilized to estimate a joint space of row and column objects, as well as a set of weights depicting the third mode of the data. An algorithm using a conjugate gradient method with automatic restarts is developed to estimate the parameters of the models. A series of Monte Carlo analyses are carried out to investigate the performance of the algorithm under diverse data and model specification conditions, examine the statistical properties of the associated test statistic, and test the robustness of the procedure to departures from the independence assumptions. Finally, a consumer psychology application assessing the impact of situational influences on consumers' choice behavior is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45749/1/11336_2005_Article_BF02294486.pd
- …