Problem decomposition by mutual information and force-based clustering

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter-dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.PhDCommittee Co-Chair: Braun, Robert D.; Committee Co-Chair: Clark, Ian G.; Committee Member: Chen, George T.; Committee Member: Clarke, John-Paul; Committee Member: Isbell, Charles L

On the Design of a Novel Solid Oxide Fuel Cell Combined Cooling, Heating and Power System for UK Residential Needs

Combined cooling, heating and power (CCHP) systems have become a topic of increasing research interest especially now that they may offer substantial improvements for conservation of fuel and electrical power in the domestic residential sector. However, only a few of the fuel cell (FC)-based CCHP systems have considered the inclusion of other power sources as part of their design with respect to diverse criteria for system optimisation. Most of the research undertaken thus far has focused on the performance improvement of CCHP systems when operated as a single energy source and has not considered the operation when connected to the electrical power distribution grid or under dynamic load conditions. The aim of this research project is to design a solid oxide fuel cell (SOFC)-based CCHP hybrid system that maximises system efficiency and minimises emissions and system costs in an objective manner with minimal operator and customer intervention. A new system structure has been designed to improve the flexibility of the system such that its functioning is closer to practical applications in both island and grid-connected modes, and still returns optimised performance with no need for system redesign or reconfiguration. A novel combination of grey relationship analysis (GRA) linked to an entropy weighting approach has been developed to evaluate the sizing values of fuel cells, heat exchangers and absorption chillers to improve the technical, economic and environmental system performance and reduce subjectivity and inaccuracy that could be imported through reliance on subjective human judgement. A new algorithm, denoted as the multi-objective particle swarm optimisation (MOPSO)-GRA has been designed to reduce local optimisation problem caused by standard MOPSO algorithms. The proposed system has been verified with published experimental results and comparative analysis has been carried out to verify the advance and the new algorithms. The main conclusion is that the optimum design of the SOFC-based CCHP hybrid system delivers optimised performance in terms of efficiency, operation and through life economy as well as environmental impact that gives a high degree of flexible compatibility within the energy supply environment in the UK

Identifying Diabetic Patients: A Data Mining Approach

Mounting amounts of data made traditional data analysis methods impractical. Data mining (DM) tools provide a useful for alternative framework that addresses this problem. This study follows a DM technique to identify diabetic patients. We develop a model that clusters diabetes patients of a large healthcare company into different subpopulation. Consequently, we show the value of applying a DM model to identify diabetic patients

A Data Mining Approach To identify Diabetes

Mounting amounts of data made traditional data analysis methods impractical. Data mining (DM) tools provide a useful for alternative framework that addresses this problem. This study follows a DM technique to identify diabetic patients. We develop a model that clusters diabetes patients of a large healthcare company into different subpopulation. Consequently, we show the value of applying a DM model to identify diabetic patients

Transforming Evolutionary Search into Higher-Level Evolutionary Search by Capturing Problem Structure

The intuitive idea that good solutions to small problems can be reassembled into good solutions to larger problems is widely familiar in many fields including evolutionary computation. This idea has motivated the building-block hypothesis and model-building optimization methods that aim to identify and exploit problem structure automatically. Recently, a small number of works make use of such ideas by learning problem structure and using this information in a particular manner: these works use the results of a simple search process in primitive units to identify structural correlations (such as modularity) in the problem that are then used to redefine the variational operators of the search process. This process is applied recursively such that search operates at successively higher scales of organization, hence multi-scale search. Here, we show for the first time that there is a simple class of (modular) problems that a multi-scale search algorithm can solve in polynomial time that requires super-polynomial time for other methods. We discuss strengths and limitations of the multi-scale search approach and note how it can be developed further