In this thesis, optimization of the geometry of non-uniform arrays for direction finding yielding unambiguous results is studied. A measure of similarity between the array response vectors is defined. In this measure, the effects of antenna array geometry, source placements and antenna gains are included as variable parameters. Then, assuming that the antenna gains are known and constant, constraints on the similarity function are developed and described to result in unambiguous configurations and maximum resolution. The problem stated is solved with two different methods, the MATLAB optimization toolbox, and genetic algorithm in which different genetic codings are also studied. The performance of the MUSIC algorithm with the optimized array geometries are investigated through computer simulations. The direction of arrival estimates are obtained using the optimized array geometry on the MUSIC algorithm along with the effects of different parameters. Statistics of the true and probable erroneous arrival angles and the probability of gross error are obtained as a measure of performance. It is observed that the proposed optimization process for the array geometry gave rise to unambiguous results for direction finding.

In order to achieve global carbon reduction targets, buildings must be designed to be energy efficient. Building performance simulation methods, together with sensitivity analysis and evolutionary optimization methods, can be used to generate design solution and performance information that can be used in identifying energy and cost efficient design solutions. Sensitivity analysis is used to identify the design variables that have the greatest impacts on the design objectives and constraints. Multi-objective evolutionary optimization is used to find a Pareto set of design solutions that optimize the conflicting design objectives while satisfying the design constraints; building design being an inherently multi-objective process. For instance, there is commonly a desire to minimise both the building energy demand and capital cost while maintaining thermal comfort. Sensitivity analysis has previously been coupled with a model-based optimization in order to reduce the computational effort of running a robust optimization and in order to provide an insight into the solution sensitivities in the neighbourhood of each optimum solution. However, there has been little research conducted to explore the extent to which the solutions found from a building design optimization can be used for a global or local sensitivity analysis, or the extent to which the local sensitivities differ from the global sensitivities. It has also been common for the sensitivity analysis to be conducted using continuous variables, whereas building optimization problems are more typically formulated using a mixture of discretized-continuous variables (with physical meaning) and categorical variables (without physical meaning). This thesis investigates three main questions; the form of global sensitivity analysis most appropriate for use with problems having mixed discretised-continuous and categorical variables; the extent to which samples taken from an optimization run can be used in a global sensitivity analysis, the optimization process causing these solutions to be biased; and the extent to which global and local sensitivities are different. The experiments conducted in this research are based on the mid-floor of a commercial office building having 5 zones, and which is located in Birmingham, UK. The optimization and sensitivity analysis problems are formulated with 16 design variables, including orientation, heating and cooling setpoints, window-to-wall ratios, start and stop time, and construction types. The design objectives are the minimisation of both energy demand and capital cost, with solution infeasibility being a function of occupant thermal comfort. It is concluded that a robust global sensitivity analysis can be achieved using stepwise regression with the use of bidirectional elimination, rank transformation of the variables and BIC (Bayesian information criterion). It is concluded that, when the optimization is based on a genetic algorithm, that solutions taken from the start of the optimization process can be reliably used in a global sensitivity analysis, and therefore, there is no need to generate a separate set of random samples for use in the sensitivity analysis. The extent to which the convergence of the variables during the optimization can be used as a proxy for the variable sensitivities has also been investigated. It is concluded that it is not possible to identify the relative importance of variables through the optimization, even though the most important variable exhibited fast and stable convergence. Finally, it is concluded that differences exist in the variable rankings resulting from the global and local sensitivity methods, although the top-ranked solutions from each approach tend to be the same. It also concluded that the sensitivity of the objectives and constraints to all variables is obtainable through a local sensitivity analysis, but that a global sensitivity analysis is only likely to identify the most important variables. The repeatability of these conclusions has been investigated and confirmed by applying the methods to the example design problem with the building being located in four different climates (Birmingham, UK; San Francisco, US; and Chicago, US).

Energy functions are found to be a key of protein structure prediction. In this work, we propose a novel 3-dimensional energy function based on hydrophobic-hydrophilic properties of amino acid where we consider at least three different possible interaction of amino acid in a 3-dimensional sphere categorized as hydrophilic versus hydrophilic, hydrophobic versus hydrophobic and hydrophobic versus hydrophilic. Each of these interactions are governed by a 3-dimensional parameter alpha used to model the interaction and 3-dimensional parameter beta used to model weight of contribution. We use Genetic Algorithm (GA) to optimize the value of alpha, beta and Z-score. We obtain three energy scores libraries from a database of 4332 protein structures obtained from Protein Data Bank (PDB) server. Proposed energy function is found to outperform nearest competitor by 40.9% for the most challenging Rosetta decoy as well as better in terms of the Z-score based on Moulder and Rosetta decoy sets.

In pursuance of decreasing costs, both the client and the contractor would strive to speed up the construction project. However, accelerating the project schedule will impose additional cost and might be profitable up to a certain limit. Paramount for construction management, analyses of this trade-off between duration and cost is hailed as the time-cost trade-off (TCT) optimization. Inadequacies of existing commercial software packages for such analyses tied with eminence of discretization, motivated development of different paradigms of particle swarm optimizers (PSO) for three extensions of discrete TCT problems (DTCTPs). A sole-PSO algorithm for concomitant minimization of time and cost is proposed which involves minimal adjustments to shift focus to the completion deadline problem. A hybrid model is also developed to unravel the time-cost curve extension of DCTCPs. Engaging novel principles for evaluation of cost-slopes, and pbest/gbest positions, the hybrid SAM-PSO model combines complementary strengths of overhauled versions of the Siemens Approximation Method (SAM) and the PSO algorithm. Effectiveness and efficiency of the proposed algorithms are validated employing instances derived from the literature. Throughout computational experiments, mixed integer programming technique is implemented to introduce the optimal non-dominated fronts of two specific benchmark problems for the very first time in the literature. Another chief contribution of this thesis can be depicted as potency of SAM-PSO model in locating the entire Pareto fronts of the practiced instances, within acceptable time-frames with reasonable deviations from the optima. Possible further improvements and applications of SAM-PSO model are suggested in the conclusion.

This thesis contains three main sections. In the first section, we examine methodologies to discriminate Type II protein kinase inhibitors from the Type I inhibitors. We have studied the structure of 55 Type II kinase inhibitors and have notice specific descriptive geometric features. Using this information we have developed a pharmacophore and a shape based screening approach. We have found that these methods did not effectively discriminate between the two inhibitor types used independently, but when combined in a consecutive way – pharmacophore search first, then shape based screening, we have found a method that successfully filtered out all Type I molecules. The effect of protonation states and using different conformer generators were studied as well. This method was then tested on a freely available database of decoy molecules and again shown to be discriminative. In the second section of the thesis, we implement and assess swarm-based docking methods. We implement a repulsive particle swarm optimization (RPSO) based conformational search approach into Autodock 3.05. The performance of this approach with different parameters was then tested on a set of 51 protein ligand complexes. The effect of using different factoring for the cognitive, social and repulsive terms and the importance of the inertia weight were explored. We found that the RPSO method gives similar performance to the particle swarm optimization method. Compared to the genetic algorithm approach used in Autodock 3.05, our RPSO method gives better results in terms of finding lower energy conformations. In the final, third section we have implemented a Monte Carlo (MC) based conformer searching approach into Gaussian03. This enables high level quantum mechanics/molecular mechanics (QM/MM) potentials to be used in docking molecules in a protein active site. This program was tested on two Zn2+ ion-containing complexes, carbonic anhydrase II and cytidine deaminase. The effects of different QM region definitions were explored in both systems. A consecutive and a parallel docking approach were used to study the volume of the active site explored by the MC search algorithm. In case of the carbonic anhydrase II complex, we have used 1,2-difluorobenzene as a ligand to explore the favourable interactions within the binding site. With the cytidine deaminase complex, we have evaluated the ability of the approach to discriminate the native pose from other higher energy conformations during the exploration of the active site of the protein. We find from our initial calculations, that our program is able to perform a conformational search in both cases, and the effect of QM region definition is noticeable, especially in the description of the hydrophobic interactions within the carbonic anhydrase II system. Our approach is also able to find poses of the cytidine deaminase ligand within 1 Å of the native pose.

Tato diplomová práce se zabývá tématem výměny tepla v zásobníku tepla typu ”packed bed”. Cílem je popsat přenos tepla v zásobníku tepla obsahující kamínky malých průměrů, skrz který proudí horký vzduch. Toto je modelováno v prostředí MATLAB. Na začátku je krátký úvod do problematiky zahrnující ukládání tepla a jeho možné využití. Dále je uveden krátký přehled o základech přenosu tepla, typech přenosu tepla a termofyzikální vlastnosti systému vzduch-kámen. Ve třetí kapitole je představen zásobník tepla typu ”packed bed” a rozličné modely a dané podmínky jsou vysvětleny. Další kapitola se zabývá s numerickými metodami, převážně s metodou konečných diferencí použitou v této práci. Pátá kapitola se zaměřuje na obecnou optimalizaci daného problému přenosu tepla. Populačně založený metaheuristický optimalizační algoritmus zvaný Genetický algoritmus je popsán. Sestavení modelu je ukázáno v šesté kapitole, stejně jako prezentace výsledků získaných z programu MATLAB. V poslední kapitole je pak diskutován závěr a doporučení.

This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.

The Generation IV International Forum (GIF) has initiated an international collaboration for the research and development of the Generation IV future nuclear energy systems. The Canadian PT-SCWR is Canada’s contribution to the GIF as a GEN-IV advanced energy system. The PT-SCWR is a pressure tube reactor type and considered as an evolution of the conventional CANDU reactor. The PT-SCWR is characterized by bi-directional coolant flow through the High Efficiency Re-entrant Channel (HERC). The Canadian SCWR is a unique design involving high pressure and temperature coolant, a light water moderator, and a thorium-plutonium fuel, and is unlike any operating or conceptual reactor at this time. The SCWR does share some features in common with the BWR configuration (direct cycle, control blades etc…), CANDU (separate low temperature moderator), and the HTGR/HTR (coolant with high propensity to up-scatter), and so it represents a hybrid of many concepts. Because of its hybrid nature there have been subtle feedback effects reported in the literature which have not been fully analyzed and are highly dependent on these unique characteristics in the core. Also given the significant isotopic changes in the fuel it is necessary to understand how the feedback mechanisms evolve with fuel depletion. Finally, given the spectral differences from both CANDU and HTR reactors further study on the few-energy group homogenization is needed. The three papers in this thesis address each one of these issues identified in literature. Models were created using the SCALE (Standardized Computer Analysis for Licensing Evaluation) code package. Through this work, it was found that the lattice is affected by more than one large individual phenomenon but that these phenomena cancel one another to have a small net final change. These phenomena are highly affected by the coolant properties which have major roles in neutron thermalization process since the PT-SCWR is characterized by a tight lattice pitch. It was observed that fresh and depleted fuel have almost similar behaviour with small differences due to the Pu depletion and the production of minor actinides, 233U and xenon. It was also found that a higher thermal energy barrier is recommended for the two-energy-group structure since the PT-SCWR is characterized by a large coolant temperature compared to the conventional water thermal reactors. Two, three and four optimum energy group structure homogenizations were determined based on the behaviour of the neutron multiplication factor and other reactivity feedback coefficients. Robust numerical computations and experience in the physics of the problem were used in the few-energy group optimization methodology. The results show that the accuracy of the expected solution becomes highly independent of the number of energy groups with more than four energy groups used. / Thesis / Doctor of Philosophy (PhD)

<p>The objective of the thesis research is to design such vertical handoff decision algorithms in order for mobile field workers and other mobile users equipped with contemporary multimode mobile devices to communicate seamlessly in the NGWN. In order to tackle this research objective, we used fuzzy logic and fuzzy inference systems to design a suitable handoff initiation algorithm that can handle imprecision and uncertainties in data and process multiple vertical handoff initiation parameters (criteria) / used the fuzzy multiple attributes decision making method and context awareness to design a suitable access network selection function that can handle a tradeoff among many handoff metrics including quality of service requirements (such as network conditions and system performance), mobile terminal conditions, power requirements, application types, user preferences, and a price model / used genetic algorithms and simulated annealing to optimise the access network selection function in order to dynamically select the optimal available access network for handoff / and we focused in particular on an interesting use case: vertical handoff decision between mobile WiMAX and UMTS access networks. The implementation of our handoff decision algorithm will provide a network selection mechanism to help mobile users select the best wireless access network among all available wireless access networks, that is, one that provides always best connected services to users.</p>

This doctoral study aims to develop a methodology for use in determining aerodynamic models and parameters from actual flight test data for different types of autonomous flight vehicles. The stepwise regression method and equation error method are utilized for the aerodynamic model identification and parameter estimation. A closed loop aerodynamic parameter estimation approach is also applied in this study which can be used to fine tune the model parameters. Genetic algorithm is used as the optimization kernel for this purpose. In the optimization scheme, an input error cost function is used together with a final position penalty as opposed to widely utilized output error cost function. Available methods in the literature are developed for and mostly applied to the aerodynamic system identification problem of piloted aircraft / a very limited number of studies on autonomous vehicles are available in the open literature. This doctoral study shows the applicability of the existing methods to aerodynamic model identification and parameter estimation problem of autonomous vehicles. Also practical considerations for the application of model structure determination methods to autonomous vehicles are not well defined in the literature and this study serves as a guide to these considerations.