Classification of mixed data points for coupled circles estimation

Concentric circles fitting is a challenge task since the nonlinear fitting problem needs to find out the implicit relationship between the noisy measurement data points and the unknown parameters, circles center and radii, to be estimated. For most of the concentric circles estimators, they require the knowledge of the number of circles and the data points belonging to the different circles. However, this information is often not available in practice. In this thesis, we shall try to solve these two problems. When the number of concentric circles is available, we propose and compare three classification methods, the K-Means method, the Distance Division method and the Naive Bayes classifier, to assign the data points to the circles. If the number of concentric circles is not known, the non-parametric data clustering methods, such as the Mean Shift method and the Distance Threshold method, are developed in this thesis to estimate the number of circles for the estimate later. A new method is proposed to combine the Mean Shift method and the Naive Bayes classifier to improve the joint estimation of the number of circles and classification of data points. The performances of the proposed solutions are supported by the simulations using synthetic data

DEKAS - An evolutionary case-based reasoning system to support protection scheme design

This paper describes a decision support system being developed in conjunction with two UK utility companies to aid the design of electrical power transmission protection systems. A brief overview of the application domain is provided, followed by a description of the work carried out to date concerning the development and deployment of the Design Engineering Knowledge Application System (DEKAS). The paper then discusses the provision of intelligent decision support to the design engineer through the application of case-based reasoning (CBR). The key benefits from this will be outlined in conjunction with a relevant case study

Quantum Engineering of Spin and Anisotropy in Magnetic Molecular Junctions

Single molecule magnets and single spin centers can be individually addressed when coupled to contacts forming an electrical junction. In order to control and engineer the magnetism of quantum devices, it is necessary to quantify how the structural and chemical environment of the junction affects the spin center. Metrics such as coordination number or symmetry provide a simple method to quantify the local environment, but neglect the many-body interactions of an impurity spin when coupled to contacts. Here, we utilize a highly corrugated hexagonal boron nitride (h-BN) monolayer to mediate the coupling between a cobalt spin in CoHx (x=1,2) complexes and the metal contact. While the hydrogen atoms control the total effective spin, the corrugation is found to smoothly tune the Kondo exchange interaction between the spin and the underlying metal. Using scanning tunneling microscopy and spectroscopy together with numerical simulations, we quantitatively demonstrate how the Kondo exchange interaction mimics chemical tailoring and changes the magnetic anisotropy