Ranking-Dominance Relation for (Evolutionary) Multi-Objective Optimization

An alternative relation to Pareto-dominance relation is proposed. The new relation is based on ranking a set of solutions according to each separate objective and an aggregation function to calculate a scalar fitness value for each solution. The relation is called as ranking-dominance and it tries to tackle the curse of dimensionality commonly observedin evolutionary multi-objective optimization. Ranking-dominance can beused to sort a set of solutions even for a large number of objectives when Pareto-dominance relation cannot distinguish solutions from one another anymore. This permits search to advance even with a large number of objectives. It is also shown that ranking-dominance does not violate Pareto-dominance. Results indicate that selection based on ranking-dominance is able to advance search towards the Pareto-front in some cases, where selection based on Pareto-dominance stagnates. However, in some cases it is also possible that search does not proceed into direction of Pareto-front because the ranking-dominance relation permits deterioration of individual objectives. Results also show that when the number of objectives increases, selection based on just Pareto-dominance without diversity maintenance is able to advance search better than with diversity maintenance. Therefore, diversity maintenance is connive at the curse of dimensionality.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Improvements in the Assessment of Bacterial Viability and Killing

It is axiomatic that our planet is extensively inhabited by diverse micro-organisms such as bacteria, yet the absolute diversity of different bacterial species is widely held to be unknown. Different bacteria can be found from the depths of the oceans to the top of the mountains; even the air is more or less colonized by bacteria. Most bacteria are either harmless or even advantageous to human beings but there are also bacteria, which can cause severe infectious diseases or spoil the supplies intended for human consumption. Therefore, it is vitally important not only to be able to detect and enumerate bacteria but also to assess their viability and possible harmfulness. Whilst the growth of bacteria is remarkably fast under optimum conditions and easy to detect by cultural methods, most bacteria are believed to lie in stationary phase of growth in which the actual growth is ceased and thus bacteria may simply be undetectable by cultural techniques. Additionally, several injurious factors such as low and high temperature or deficiency of nutrients can turn bacteria into a viable but non-culturable state (VBNC) that cannot be detected by cultural methods. Thereby, various noncultural techniques developed for the assessment of bacterial viability and killing have widely been exploited in modern microbiology. However, only a few methods are suitable for kinetic measurements, which enable the real-time detection of bacterial growth and viability. The present study describes alternative methods for measuring bacterial viability and killing as well as detecting the effects of various antimicrobial agents on bacteria on a real-time basis. The suitability of bacterial (lux) and beetle (luc) luciferases as well as green fluorescent protein (GFP) to act as a marker of bacterial viability and cell growth was tested. In particular, a multiparameter microplate assay based on GFP-luciferase combination as well as a flow cytometric measurement based on GFP-PI combination were developed to perform divergent viability analyses. The results obtained suggest that the antimicrobial activities of various drugs against bacteria could be successfully measured using both of these methods. Specifically, the data reliability of flow cytometric viability analysis was notably improved as GFP was utilized in the assay. A fluoro-luminometric microplate assay enabled kinetic measurements, which significantly improved and accelerated the assessment of bacterial viability compared to more conventional viability assays such as plate counting. Moreover, the multiparameter assay made simultaneous detection of GFP fluorescence and luciferase bioluminescence possible and provided extensive information about multiple cellular parameters in single assay, thereby increasing the accuracy of the assessment of the kinetics of antimicrobial activities on target bacteria.

Development of phase retrieval algorithm and techniques for optical spectrum analysis

Coherent anti-Stokes Raman scattering is the powerful method of laser spectroscopy in which significant successes are achieved. However, the non-linear nature of CARS complicates the analysis of the received spectra. The objective of this Thesis is to develop a new phase retrieval algorithm for CARS. It utilizes the maximum entropy method and the new wavelet approach for spectroscopic background correction of a phase function. The method was developed to be easily automated and used on a large number of spectra of different substances.. The algorithm was successfully tested on experimental data.