3,550 research outputs found
On Iterative Algorithms for Quantitative Photoacoustic Tomography in the Radiative Transport Regime
In this paper, we describe the numerical reconstruction method for
quantitative photoacoustic tomography (QPAT) based on the radiative transfer
equation (RTE), which models light propagation more accurately than diffusion
approximation (DA). We investigate the reconstruction of absorption coefficient
and/or scattering coefficient of biological tissues. Given the scattering
coefficient, an improved fixed-point iterative method is proposed to retrieve
the absorption coefficient for its cheap computational cost. And we prove the
convergence. To retrieve two coefficients simultaneously, Barzilai-Borwein (BB)
method is applied. Since the reconstruction of optical coefficients involves
the solution of original and adjoint RTEs in the framework of optimization, an
efficient solver with high accuracy is improved from~\cite{Gao}. Simulation
experiments illustrate that the improved fixed-point iterative method and the
BB method are the comparative methods for QPAT in two cases.Comment: 21 pages, 44 figure
An Investigation of the Electrical Response of A Variable Speed Motor Drive for Mechanical Fault Diagnosis
Motor current signal analysis has been an effective way for many years to monitor electrical machines. However, little research work has been reported in using this technique for monitoring variable speed drives and their downstream equipment. This paper investigates the dynamic responses of the electrical current signals measured from a variable speed drive for monitoring the faults from a downstream gearbox. An analytical study is firstly presented in the paper to show the characteristics of the current
signals due to load variation, fault effects and signal phase variation. Experimental study is then conducted under different gear fault conditions to explore the changes of the signals. Both conventional spectrum analysis and an amplitude modulation (AM) bispectrum representation are used to highlight the changes
for reliable fault detection. It has been found experimentally that mechanical faults lead to much higher increases in bispectral amplitudes compared to conventional spectra and hence that detection performance of the AM bispectrum is better when the drive operates non-slip compensation mode. For slip compensation, more accurate signal analysis techniques have to be developed to differentiate the small changes in the signals
Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle based on an Improved Stochastic System Identification
This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7Hz and 40Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to optimize the revenue of the search engine. However, due to some
unrealistic assumptions used, the practical values of these studies are not
very clear. In this paper, we propose a novel \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization framework. In
particular, we first learn a Markov model from historical data to describe how
advertisers change their bids in response to an auction mechanism, and then for
any given auction mechanism, we use the learnt model to predict its
corresponding future bid sequences. Next we learn the auction mechanism through
empirical revenue maximization on the predicted bid sequences. We show that the
empirical revenue will converge when the prediction period approaches infinity,
and a Genetic Programming algorithm can effectively optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence
(IJCAI 2013
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