No description available.

yes / The importance of real-time processing of solar data especially for space weather applications is increasing continuously. In this paper, we present an automated hybrid computer platform for the short-term prediction of significant solar flares using SOHO/Michelson Doppler Imager images. This platform is called the Automated Solar Activity Prediction tool (ASAP). This system integrates image processing and machine learning to deliver these predictions. A machine learning-based system is designed to analyze years of sunspot and flare data to create associations that can be represented using computer-based learning rules. An imaging-based real-time system that provides automated detection, grouping, and then classification of recent sunspots based on the McIntosh classification is also created and integrated within this system. The properties of the sunspot regions are extracted automatically by the imaging system and processed using the machine learning rules to generate the real-time predictions. Several performance measurement criteria are used and the results are provided in this paper. Also, quadratic score is used to compare the prediction results of ASAP with NOAA Space Weather Prediction Center (SWPC) between 1999 and 2002, and it is shown that ASAP generates more accurate predictions compared to SWPC. / EPSRC

This thesis analyzes the connection between monetary policies and belief-driven fluctuation, and discusses the effects of monetary policies in a small open economy. We construct an endogenous growth model that introduces the role of money into the production function and allows elastic labor supply. In departing from the findings proposed by Benhabib and Farmer (1994), we find that belief-driven fluctuation can be easily encouraged, as long as there is lower increasing return to scale under money growth rate targeting. However, if there is a higher level of increasing return to scale, the increase of the growth rate of nominal money supply will only increase the economic growth rate temporarily, and money is super-neutral in the long run. More importantly, we show that under inflation rate targeting, the central bank will eliminate possibilities of belief-driven fluctuation in the small open economy, but lose the efficacy of monetary policy on the short-term economic growth at the same time.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Solar images are one of the most important sources of available information on the current state and behaviour of the sun, and the PICARD satellite is one of several ground and space-based observatories dedicated to the collection of that data. The PICARD satellite hosts the Solar Diameter Imager and Surface Mapper (SODISM), a telescope aimed at continuously monitoring the Sun. It has generated a huge cache of images and other data that can be analysed and interpreted to improve the monitoring of features, such as sunspots and the prediction and diagnosis of solar activity. In proportion to the available raw material, the little-published analysis of SODISM data has provided the impetus for this study, specifically a novel method of contributing to the development of a system to enhance, detect and segment sunspots using new hybrid methods. This research aims to yield an improved understanding of SODISM data by providing novel methods to tabulate a sunspot and filling factor (FF) catalogue, which will be useful for future forecasting activities. The developed technologies and the findings achieved in this research will work as a corner stone to enhance the accuracy of sunspot segmentation; create efficient filling factor catalogue systems, and enhance our understanding of SODISM image enhancement. The results achieved can be summarised as follows: i) Novel enhancement method for SODISM images. ii) New efficient methods to segment dark regions and detect sunspots. iii) Novel catalogue for filling factor including the number, size and sunspot location. v) Novel statistical method to summarise FFs catalogue. Image processing and partitioning techniques are used in this work; these methods have been applied to remove noise and detect sunspots and will provide more information such as sunspot numbers, size and filling factor. The performance of the model is compared to the fillers extracted from other satellites, such as SOHO. Also, the results were compared with the NOAA catalogue and achieved a precision of 98%. Performance measurement is also introduced and applied to verify results and evaluate proposal methods. Algorithms, implementation, results and future work have been explained in this thesis.

No description available.

Yes / In this paper, a machine-learning-based system that could provide automated short-term solar flare prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots with their corresponding flares based on their timing and NOAA numbers. The McIntosh classification for every relevant sunspot is extracted and converted to a numerical format that is suitable for machine learning algorithms. Using this system we aim to predict whether a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and if so whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNNs), Support Vector Machines (SVMs) and Radial Basis Function Networks (RBFN) are optimised and then compared to determine the learning algorithm that would provide the best prediction performance. It is concluded that SVMs provide the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not but CCNNs are more capable of predicting the class of the flare to erupt. A hybrid system that combines a SVM and a CCNN is suggested for future use. / EPSRC

yes / A comprehensive spectral analysis of both the solar background magnetic field (SBMF) in cycles 21-23 and the sunspot magnetic field in cycle 23 reported in our recent paper showed the presence of two principal components (PCs) of SBMF having opposite polarity, e. g., originating in the northern and southern hemispheres, respectively. Over a duration of one solar cycle, both waves are found to travel with an increasing phase shift toward the northern hemisphere in odd cycles 21 and 23 and to the southern hemisphere in even cycle 22. These waves were linked to solar dynamo waves assumed to form in different layers of the solar interior. In this paper, for the first time, the PCs of SBMF in cycles 21-23 are analyzed with the symbolic regression technique using Hamiltonian principles, allowing us to uncover the underlying mathematical laws governing these complex waves in the SBMF presented by PCs and to extrapolate these PCs to cycles 24-26. The PCs predicted for cycle 24 very closely fit (with an accuracy better than 98%) the PCs derived from the SBMF observations in this cycle. This approach also predicts a strong reduction of the SBMF in cycles 25 and 26 and, thus, a reduction of the resulting solar activity. This decrease is accompanied by an increasing phase shift between the two predicted PCs (magnetic waves) in cycle 25 leading to their full separation into the opposite hemispheres in cycle 26. The variations of the modulus summary of the two PCs in SBMF reveals a remarkable resemblance to the average number of sunspots in cycles 21-24 and to predictions of reduced sunspot numbers compared to cycle 24: 80% in cycle 25 and 40% in cycle 26.

Yes / This paper presents a new method that provides the means to detect sunspots on full-disk solar images recorded by the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The method is a totally automated detection process that achieves a sunspot recognition rate of 97.6%. The number of sunspots detected by this method strongly agrees with the NOAA catalogue. The sunspot areas calculated by this method have a 99% correlation with SOHO over the same period, and thus help to calculate the filling factor for wavelength (W.L.) 607nm.

Yes / The surface of the sun often shows visible sunspots which are located in magnetically active regions of the Sun, and whose number is an indicator of the Sun’s magnetic activity. The detection and classification of sunspots are useful techniques in the monitoring and prediction of solar activity. The automated detection of sunspots from digital images is complicated by their irregularities in shape and variable contrast and intensity compared with their surrounding area. The main aim of this paper is to detect sunspots using images from the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite and calculate their filling factors. A comparison over time with sunspot numbers obtained using images from the SOHO satellite is also presented.