Single shot parameter estimation via continuous quantum measurement

We present filtering equations for single shot parameter estimation using continuous quantum measurement. By embedding parameter estimation in the standard quantum filtering formalism, we derive the optimal Bayesian filter for cases when the parameter takes on a finite range of values. Leveraging recent convergence results [van Handel, arXiv:0709.2216 (2008)], we give a condition which determines the asymptotic convergence of the estimator. For cases when the parameter is continuous valued, we develop quantum particle filters as a practical computational method for quantum parameter estimation.Comment: 9 pages, 5 image

Physics-based prognostic modelling of filter clogging phenomena

In industry, contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the system are the unfortunate outcomes due to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful lives of the filters used in the experimental rig have been reported in the paper. The results show that the presented methodology provides significantly accurate and precise prognostic results

Relation Between Particle Mass and Number for Submicrometer Airborne Particles

The relationship between particle mass and the number of ambient air particles for the submicrometer size range was examined using a Tapered Element Oscillating Microbalance to determine the mass concentration, and a Scanning Mobility Particle Sizer to determine the volume concentration and total number of particles. After validating the techniques through their application to the estimation of submicrometer particle density for two laboratory generated aerosols of known bulk density (Sodium Chloride and Di-2-ethylhexyl-sebacate), the submicrometer fraction of laboratory generated Environmental Tobacco Smoke and ambient air were examined and an estimate of the average submicrometer particle densities for these aerosols found to be 1.18 g cm-3 and 1.7 g cm-3 respectively