93,315 research outputs found
Evidence cross-validation and Bayesian inference of MAST plasma equilibria
In this paper, current profiles for plasma discharges on the Mega-Ampere
Spherical Tokamak (MAST) are directly calculated from pickup coil, flux loop
and Motional-Stark Effect (MSE) observations via methods based in the
statistical theory of Bayesian analysis. By representing toroidal plasma
current as a series of axisymmetric current beams with rectangular
cross-section and inferring the current for each one of these beams,
flux-surface geometry and q-profiles are subsequently calculated by elementary
application of Biot-Savart's law. The use of this plasma model in the context
of Bayesian analysis was pioneered by Svensson and Werner on the Joint-European
Tokamak (JET) [J. Svensson and A. Werner. Current tomography for axisymmetric
plasmas. Plasma Physics and Controlled Fusion, 50(8):085002, 2008]. In
this framework, linear forward models are used to generate diagnostic
predictions, and the probability distribution for the currents in the
collection of plasma beams was subsequently calculated directly via application
of Bayes' formula. In this work, we introduce a new diagnostic technique to
identify and remove outlier observations associated with diagnostics falling
out of calibration or suffering from an unidentified malfunction. These
modifications enable good agreement between Bayesian inference of the last
closed flux-surface (LCFS) with other corroborating data, such as such as that
from force balance considerations using EFIT++ [L. Appel et al., Proc. 33rd EPS
Conf., Rome, Italy, 2006]. In addition, this analysis also yields errors on the
plasma current profile and flux-surface geometry, as well as directly
predicting the Shafranov shift of the plasma core.This work was jointly funded by the Australian Government
through International Science Linkages Grant No.
CG130047, the Australian National University, the United
Kingdom Engineering and Physical Sciences Research
Council under Grant No. EP/G003955, and by the European
Communities under the contract of Association between EURATOM and CCFE
Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion
Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature
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