X-ray and optical studies of soft x-ray transients in M31

Soft X-ray transients (SXTs) are a subgroup of low-mass X-ray binaries consisting of a neutron star or a black hole and a companion low-mass star. SXTs exhibit a sudden outburst by increasing the luminosity from ∼ 1033 to ∼ 1036−38ergs1. After spending a few months in outburst, SXTs switch back to quiescence. Optical study of the binary system during the quiescence state of SXTs provides an opportunity to discriminate between BH binaries and neutron star binaries. The first part ot this research is composed of result of 10 years joint project between Hubble space telescope and Chandra, to study SXTs in M31. The other part of this thesis focused on the light curve of bright SXTs in M31. Disc irradiation is thought to be capable of explaining the global behaviour of the light curves of SXTs. Depending on the strength of the central X-ray emission in irradiating the disc, the light curve may exhibit an exponential or a linear decay. The model predicts that in brighter transients a transition from exponential decline to a linear one may be detectable. In this study, having excluded super-soft sources and hard X-ray transients, a sample of bright SXTs in M31 (Lpeak > 1038ergs1) has been studied. The expected change in the shape of the decay function is only observed in two of the light curves from the six light curves in the sample. Also, a systematic correlation between the shape of the light curve and the X-ray luminosity has not been seen.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Dry mixing of spice powders - investigation of effect of powder properties on mixture quality of binary powder mixtures

Dry mixing of binary food powders was conducted in a 2L lab-scale paddle mixer. Different types of food powders such as paprika, oregano, black pepper, onion powder and salt were used for the studies. A novel method based on a digital colour imaging system (DCI) was developed to measure the mixture quality (MQ) of binary food powder mixtures. The salt conductivity method was also used as an alternative method to measure the MQ. In the first part of the study the DCI method was developed and it showed potential for assessing MQ of binary powder mixes provided there was huge colour difference between the powders. In the second and third part of the study the effect of composition, water content, particle size and bulk density on MQ was studied. Flowability of powders at various moisture contents was also investigated. The mixing behaviour was assessed using coefficient of variation. Results showed that water content and composition influence the mixing behavior of powders. Good mixing was observed up to size ratios of 4.45 and at higher ratios MQ disimproved. The bulk density had a larger influence on the MQ. In the final study the MQ evaluation of binary and ternary powder mixtures was compared by using two methods – salt conductivity method and DCI method. Two binary food and two quaternary food powder mixtures with different coloured ingredients were studied. Overall results showed that DCI method has a potential for use by industries and it can analyse powder mixtures with components that have differences in colour and that are not segregating in nature.