A probabilistic approach to model-based adaptive control for damping of interarea oscillations

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Inversion of 2 wavelength Lidar data for cloud properties

The inversion of the lidar equation to derive quantitative properties of the atmosphere has continued to present considerable difficulty. The results of a study in which Klett's procedure was utilized for the analysis of cloud backscatter measurements made simulataneously at two ruby lidar wavelengths (694nm,347nmm) are presented. With one lidar system a cloud is probed at the two wavelength and the backscatter measured simulataneously by separate receivers. As a result two sigma profiles which should differ only because the wavlength dependence of the scattering. Experimental data presented to demonstrate the effects and the implications of the applications of the inversion method will be discussed

Determination of cloud microphysical properties by laser backscattering and extinction measurements

The extinction and backscattering of 514 nm laser radiation in polydisperse water droplet clouds was studied in the laboratory. Three cloud size distributions with modal diameters of 0.02, 5.0, and 12.0 microns were investigated. The relationships between the cloud optical parameters (attentuation coefficient, sigma and volume backscattering coefficient, Beta (sub pi)) and the cloud water content, C, were measured for each size distribution. It was found that a linear relationship exists between sigma and C and between beta (sub pi) and C for cloud water content values up to 3gm/cubic m. The linear relationships obtained, however, have slopes which depend on the droplet size distribution. For a given water content both sigma and beta (sub pi) increase as the modal diameter decreases. The measured data are compared with existing theoretical analyses and discussed in terms of thie application to lidar measurements of atmospheric clouds. It is concluded that the empirical information obtained can serve as a basis for quantitative lidar measurements

Measuring financial inclusion: An Axiomatic approach

This paper clearly demonstrates that the axiomatic measurement approach developed in the human development literature can be usefully applied to the measurement of financial inclusion. A conceptual framework for aggregating data on financial services in different dimensions is developed. The suggested index of financial inclusion allows calculation of percentage contributions of different dimensions to the overall achievement. This in turn enables us to identify the dimensions of inclusion that are more/less susceptible to overall inclusion and hence to isolate the dimensions that deserve attention from a policy perspective. The paper also illustrates the index using cross-country and sub-national level data.Financial inclusion, axioms, index, policy, application

Measuring Financial Inclusion : An Axiomatic Approach

This paper clearly demonstrates that the axiomatic measurement approach developed in the human development literature can be usefully applied to the measurement of financial inclusion. A conceptual framework for aggregating data on financial services in different dimensions is developed. The suggested index of financial inclusion allows calculation of percentage contributions of different dimensions to the overall achievement. This in turn enables us to identify the dimensions of inclusion that are more/less susceptible to overall inclusion and hence to isolate the dimensions that deserve attention from a policy perspective. The paper also illustrates the index using cross-country and sub-national level data.financial inclusion, axioms, index, policy, application

Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better.Comment: 14 pages, 2 figure