This dissertation considers modeling groundwater flow under imprecisely known parameters and managing a plume of contaminant. A new approach has been developed to study the effects of parameters uncertainty on the dependent variable, here the head. The proposed approach is developed based on fuzzy set theory combined with interval analysis. The kind of uncertainty modeled here is the imprecision associated with model parameters as a result of machine or human imprecision or lack of information. In this technique each parameter is described by a membership function. The fuzzy inputs into the model are in the form of intervals so are the outputs. The resulting head interval represents the change in the output due to interval inputs of model parameters. The proposed technique is illustrated using a two dimensional flow problem solved with a finite element technique. Three different cases are studied: homogeneous, mildly heterogeneous and highly heterogeneous transmissivity field. The groundwater flow problem analysis requires interval input values for the parameters, the output may be presented in terms of mean value, upper and lower bounds of the hydraulic head. The width of the resulting head interval can be used as a measure of uncertainty due to imprecise inputs. The degree of uncertainty associated with the predicted hydraulic head is found to increase as the width of the input parameters interval increases. Compared to Monte Carlo simulation approach, the proposed technique requires less computer storage and CPU time, however at this stage autocorrelation and crosscorolation are not configured in the presented formulation. In the plume containment problem two formulations are presented using the hydraulic gradient technique to control the movement of the contaminants. The first one is based on multiobjective analysis and the second, on fuzzy set theory. Multiobjective analysis yields a set of alternative strategies each of which satisfies the multiple objectives to a certain degree. Three different techniques have been used to choose a compromise strategy. Although they follow different principles, the same preferred strategies are selected. It is also noticed that rapid restoration results in a large pumping volumes and high costs. Using a fuzzy formulation for plume containment yields the optimum pumping rates and locations in addition to the membership function at each pumping location. The resulting membership functions at these pumping locations can be used to study the sensitivity of each location to a change in objective function and constraints bounds. Overall, both the fuzzy and multiobjective methodologies, presented in this dissertation, provide new and encouraging approaches to groundwater quality management.

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published_or_final_version / abstract / toc / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Η εξοικονόμηση ενέργειας σε ένα κτίριο μέσω της αποτελεσματικής χρήσης του κτιριακού αυτοματισμού και των ενεργειακών βελτιώσεων που πρέπει να γίνουν σε αυτό είναι δυο τομείς που συγκεντρώνουν μεγάλο ενδιαφέρον τα τελευταία χρόνια. Ειδικά αν η προσπάθεια αυτή συνδυαστεί με τη χρήση μεθόδων της Ασαφούς Λογικής και των Ασαφών Γνωστικών Δικτύων (ΑΓΔ) τότε αναμένουμε πολλά και αξιόλογα αποτελέσματα τα οποία υπόσχονται μια ραγδαία βελτίωση του τρόπου ζωής μας. Στόχος της παρούσας διπλωματικής εργασίας είναι να χρησιμοποιούν προηγμένες θεωρίες ελέγχου για να διαμορφωθεί τη συνολική ενεργειακή συμπεριφορά ενός κτιρίου για οικιστική ή εμπορική χρήση. Η προτεινόμενη μέθοδος είναι η χρήση ασαφούς λογικής και ο έξυπνος έλεγχος για να διαμορφωθούν τέτοια έξυπνα κτίρια. Για το σκοπό αυτό κατασκευάζονται ένα σύστημα Ασαφούς Λογικής και ένα Ασαφές Γνωστικό Δίκτυο και μελετώνται οι ενεργειακές απαιτήσεις ενός κτιρίου από το νότιο τμήμα της Ελλάδας. Το προτεινόμενο έξυπνο λογισμικό που διαμορφώνεται αποτελείται από δύο μέρη: Α) Το Ασαφές Σύστημα Συμπερασμού, το οποίο λαμβάνοντας υπόψη τα μετεωρολογικά δεδομένα της περιοχής (ηλιακή ακτινοβολία, θερμοκρασία κλπ) ελέγχει τον αυτοματισμό του κτιρίου (HVAC και φωτισμός), προκειμένου να επιτευχθεί πιο αποτελεσματική χρήση του. Β) Το Ασαφές Γνωστικό Δίκτυο (ΑΓΔ), το οποίο βάσει της χρήσης του κάθε στοιχείου του αυτοματισμού κτιρίου, ανιχνεύει αν το κτίριο χρειάζεται ενεργειακές βελτιώσεις και εάν ναι, προτείνει τις κατάλληλες λύσεις με βάση την κατανάλωση ενέργειας. Οι μελέτες προσομοίωσης διεξήχθησαν χρησιμοποιώντας και τις δύο μεθόδους. Τα ληφθέντα αποτελέσματα έδειξαν ότι και οι δύο μέθοδοι μπορούν να συνεισφέρουν θετικά στη βελτίωση του αυτοματισμού κτιρίων, καθώς και στη συνολική ενεργειακή συμπεριφορά του κτιρίου. / Reducing the energy consumption of a building through the effective use of its automation and the necessary energy improvements that can be made, are two scientific areas that gathered a lot of interest in recent years, especially, if this effort is combined with the use of advanced system and control theories such as fuzzy logic, intelligent control and Fuzzy Cognitive Maps (FCM). The purpose of this diploma thesis is to use advanced control theories to model the total energy behavior of an autonomous building for residential or commercial use. The proposed method is using fuzzy logic and intelligent control to model such intelligent buildings. A Fuzzy Logic and a Fuzzy Cognitive Map are constructed. The energy requirements for a building from the southern part of Greece are determined using the Design Builder software. The proposed intelligent software tool is composed of two parts: A)The Fuzzy Inference System (FIS) which taking into account the weather data of the area (sun radiation, temperature etc.) controls the building automation (HVAC and lighting) in order to achieve its most effective use. Β)The Fuzzy Cognitive Map (FCM) which based on the use of each element of the building automation, detects whether the building needs energy improvements and if so, proposes appropriate solutions based on its energy consumption. Simulation studies were conducted using both methods. The obtained results have shown that both methods can contribute positively to the improvement of the building automation as well to the overall energy behavior of the building.

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För att ett fordon ska kunna bromsa, accelerera och svänga är friktion mellan däcken och vägen ett måste. Vid för mycket gaspådrag under en acceleration kan det hända att hjulen förlorar fäste och börjar spinna loss, något som leder till både försämrad kontroll över fordonet och att däcken slits ut i förtid. Traction controlsystemet förhindrar hjulen från att spinna loss och försöker maximera friktionen. Målet med detta examensarbete är att utvärdera olika reglerprinciper samt att undersöka olika möjligheter för att reglera friktionen mellan däck och väg. Det är ett svårt reglerproblem, dels på grund av dess olinjäritet, dels på grund av det faktum att friktionen är en okänd parameter. För att kunna undersöka olika reglermöjligheter har en modell över hjuldynamiken och en modell över drivlinan tagits fram i Matlabs simuleringsprogram Simulink. Därutöver har tre regulatorer designats: en fuzzy-regulator, en fuzzy-P-regulator och en PI-regulator. Regulatorerna utvärderades i tre tester som bland annat testade deras robusthet. Fuzzy-regulatorn och fuzzy-P-regulatorn lyckades reglera systemet bra. PI-regulatorn gjorde däremot inte ett tillfredsställande jobb, mest på grund av dess behov av ett börvärde. / Traction is necessary for a vehicle to be able to brake, accelerate and turn. When pushing the accelerator pedal too hard during an acceleration, the wheel can loose traction and start spinning, which leads to a worsen vehicle control and also wears out the tyres faster. The traction control system prevents the wheels from spinning and tries to make the tyres maintain maximum traction. The purpose of this master’s thesis is to evaluate different control methods and to investigate possible ways to control the traction. This is a difficult control problem due to its nonlinearity and the fact that the friction is an unknown parameter. For the investigation, a model of the wheel dynamics and a model of the powertrain have been developed in Matlab’s simulation program Simulink. Furthermore, three different controllers have been designed; a fuzzy controller, a fuzzy-P controller and a PI controller. The controllers were evaluated in three test cycles that among others tested their robustness. The fuzzy controller and the fuzzy-P controller managed to control the system very well. The PI controller, however, did not work satisfactory, mainly because of its need of a desired value.

Independent mobility is critical to quality of life for people of all ages, and impaired mobility leaves one with both physical and mental disadvantages. Unfortunately, there are some individuals unable to operate an electric wheelchair due to physical, perceptual, or cognitive deficits. The prime objective of this research was to develop a prototype system which can provide mobility assistant to individuals who would otherwise find it difficult or impossible to operate a power wheelchair. To accomplish this goal, a prototype system consisting of several components including an embedded microcontroller and multiple sensors has been designed which can be added to a standard power wheelchair and make it smart. The control system algorithm designed for this prototype model is based on the fuzzy logic control theory and its main purpose is to augment the user ability to navigate the wheelchair and will provide a safe and comfortable journey to the user.

In this thesis, an algorithm that improves the performance of the extended Kalman filter (EKF) on the mobile robot localization issue is proposed, which is aided by the cooperation of neural network and fuzzy logic. An EKF is used to fuse the information acquired from both the robot optical encoders and the external sensors in order to estimate the current robot position and orientation. Then the error covariance of the EKF is tracked by the covariance matching technique. When the output of the matching technique does not meet the desired condition, a fuzzy logic is employed to adjust the error covariance matrix to modify it back to the desired value range. Since the fuzzy logic is lack of the capability of learning, a neural network is presented in the algorithm to train the EKF. The simulation results demonstrate that, with the comparison to the odometry and the standard EKF method under the same error divergence condition, the proposed extended Kalman filter effectively improves the accuracy of the localization of the mobile robot system and effectively prevents the filter divergence.

This thesis presents work on the use of intelligent algorithms to solve a real-world machine vision problem in the automotive industry. Compared to commercial systems, the algorithm developed is both more robust to changes in the inspection environment and more intuitive for the user to configure. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2007-07-12 10:24:43.367