Feature Extraction Via Multiresolution MODWT Analysis in a Rainfall Forecast System

During 30 years, expert meteorologists have been sampling meteorological measurements directly related to the rainfall event, in order to improve the current forecast procedures. This study performs the Feature Extraction and Feature Selection processes to extract the relevant information in the rainfall event. The Feature Extraction has been performed with a Multiresolution Analysis applying the Maxima OverlapWavelet Transform. The selection of the wavelet decomposition, was obtained applying a Sequential Feature Selection algorithm based on General Regression Neural Networks. In this paper, it is also presented a novel architecture to perform short and medium term weather forecasts based on Neural Networks and time series estimation filters. The preliminary results obtained, present this architecture as a feasible alternative to the current forecast procedures performed by super computer simulation centers

Ion current sensing for controlled auto ignition in internal combustion engines

Envirom-nental pollution is a subject that needs urgent addressing. Since the internal combustion engine has its fair share of accountability on this, research on techniques for increasing engine efficiency and emissions is necessary. Controlled Auto Ignition is a promising combustion mode, which increases fuel efficiency while also reducing NOx emissions to negligible levels. This Thesis concentrates on the implementation of this mode through experimental research, on an engine equipped with a fully variable valvetrain. Investigation of the operational window, emissions, fuel consumption, thermodynamic efficiency is carried out and ways to improve on these are discussed. The governing consideration, however, is the control method for this rather intricate combustion mode. As such, experimental data acquisition and analysis of ion current under the whole operating spectrum, from spark ignition to full autoignition is made. It is found that the expected gains in fuel consumption and emissions are realized. In addition, ion current proves to be a very powerful and cost effective tool for engine monitoring, diagnosis and control. The author concludes that Controlled Auto Ignition is a viable proposition for mass production engine designs and that ion current, although not absolutely vital for engine control, considerably increases engine control thus allowing for greater operating window under autoignition, without compromising reliability or cost.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Algorithm development on the use of feedback signals in the context of gasoline HCCI combustion

Homogeneous Charge Compression Ignition (HCCI) combustion is a promising research subject due to its characteristics of high efficiency and low emissions. These are highly desirable, given the global picture of increased energy requirements coupled with serious environmental implications. However, one of the main considerations of HCCI implementation is its control strategies which are not straightforward as in conventional Spark Ignition (SI) or Compression Ignition (Cl) engines. In order for closed loop control strategies to be successful, appropriate signals must be selected. In this research, experimental in-cylinder signals have been collected for pressure and ion current. These have been processed and evaluated as regards their suitability for HCCI control. During this process, physical based models have been developed both for treating experimental data as well as simulating theoretical cases. Using these tools, the behaviour of unstable HCCI operation has also been explored

Metamodeling Techniques to Aid in the Aggregation Process of Large Hierarchical Simulation Models

This research investigates how aggregation is currently conducted for simulation of large systems. The purpose is to examine how to achieve suitable aggregation in the simulation of large systems. More specifically, investigating how to accurately aggregate hierarchical lower-level (higher resolution) models into the next higher-level in order to reduce the complexity of the overall simulation model. The focus is on the exploration of the different aggregation techniques for hierarchical lower-level (higher resolution) models into the next higher-level. We develop aggregation procedures between two simulation levels (e.g., aggregation of engagement level models into a mission level model) to address how much and what information needs to pass from the high resolution to the low-resolution model in order to preserve statistical fidelity. We present a mathematical representation of the simulation model based on network theory and procedures for simulation aggregation that are logical and executable. This research examines the effectiveness of several statistical techniques, to include regression and three types of artificial neural networks, as an aggregation technique in predicting outputs of the lower-level model and evaluating its effects as an input into the next higher-level model. The proposed process is a collection of various conventional statistical and aggregation techniques, to include one novel concept and extensions to the regression and neural network methods, which are compared to the truth simulation model, where the truth model is when actual lower-level model outputs are used as a direct input into the next higher-level model. The aggregation methodology developed in this research provides an analytic foundation that formally defines the necessary steps essential in appropriately and effectively simulating large hierarchical systems