Many concrete problems, ranging from Portfolio selection to Water resource management, may be cast into a multiobjective programming framework. The simplistic way of superseding blindly conflictual goals by one objective function let no chance to the model but to churn out meaningless outcomes. Hence interest of discussing ways for tackling Multiobjective Programming Problems. More than this, in many real-life situations, uncertainty and imprecision are in the state of affairs. In this dissertation we discuss ways for solving Multiobjective Programming Problems with fixed and fuzzy coefficients. No preference, a priori, a posteriori, interactive and metaheuristic methods are discussed for the deterministic case. As far as the fuzzy case is concerned, two approaches based respectively on possibility measures and on Embedding Theorem for fuzzy numbers are described. A case study is also carried out for the sake of illustration. We end up with some concluding remarks along with lines for further development, in this field. / Operations Research / M. Sc. (Operations Research)

In the thesis the design of process tolerant, use-aware radio-frequency front-ends were explored. First, the design of fuzzy logic and equation based controllers, which can adapt to multi-dimensional channel conditions, are proposed. Secondly, the thesis proves that adaptive systems can have multiple modes of operation depending upon the throughput requirements of the system. Two such modes were demonstrated: one optimizing the energy-per-bit (energy priority mode) and another achieving the lowest power consumption at the highest throughput (data priority mode). Finally, to achieve process tolerant channel adaptive operation a self-learning methodology is proposed which learns the optimal re-configuration setting for the system on-the-fly. Implications of the research are discussed and future avenues of further research are proposed.

A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways: It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions. It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine. Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer. It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly. The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns.

This thesis contributes to two completely unrelated debates in the economic literature, similar only in the relatively high degree of controversy characterizing each one. The first part is methodological and macroeconomic in nature, addressing the question of whether the distribution of income across countries is converging (i.e. are the poor catching up to the rich?) or diverging (i.e. are we witnessing the formation of two exclusive clubs, one for poor countries and another one for rich countries?). Applications of the simple Markov model to this question have generated evidence in favor of the divergence hypothesis. In the first chapter, I critically review these results. I use statistical inference to show that the divergence results are not statistically robust, and I explain that this instability of the results comes from the application of a model for discrete data to data that is actually continuous. In the second chapter, I reposition the whole convergence-divergence debate by placing it in the context of Silverman’s classic survey of non-parametric density estimation techniques. This allows me to use the basic notions of fuzzy logic to adapt the simple Markov chain model to continuous data. When I apply the newly adapted Markov chain model to the cross-country distribution question, I find evidence against the divergence hypothesis, and this evidence is statistically robust. The second part of the thesis is empirical and microeconomic in nature. I question whether observed differences between husbands’ and wives’ participation in labor markets are due to different preferences or to different constraints. My identification strategy is based on the idea that the more power an individual has relative to his/her partner, the more his/her actions will reflect his/her preferences. I use 2001 PSID data on cohabiting couples to estimate a simultaneous equations model of the spousal time allocation decision. My results confirm the stylized fact that specialization and trade does not explain time allocation for couples in which the wife is the primary breadwinner, and suggest that power could provide a more general explanation of the observations. My results show that wives with relatively more power choose to work more on the labor market and less at home, whereas husbands with more power choose to do the opposite. Since women start out from a lower level of labor market participation than men do, it would seem that spouses’ agree that the ideal mix of market work and housework lies somewhere between the husbands’ and the wives’ current positions.

No description available.

Locating specific information within museum collections represents a significant challenge for collection users. Even when the collections and catalogues exist in a searchable digital format, formatting differences and the imprecise nature of the information to be searched mean that information can be recorded in a large number of different ways. This variation exists not just between different collections, but also within individual ones. This means that traditional information retrieval techniques are badly suited to the challenges of locating particular information in digital humanities collections and searching, therefore, takes an excessive amount of time and resources. This thesis focuses on a particular search problem, that of co-reference identification. This is the process of identifying when the same real world item is recorded in multiple digital locations. In this thesis, a real world example of a co-reference identification problem for digital humanities collections is identified and explored. In particular the time consuming nature of identifying co-referent records. In order to address the identified problem, this thesis presents a novel method for co-reference identification between digitised records in humanities collections. Whilst the specific focus of this thesis is co-reference identification, elements of the method described also have applications for general information retrieval. The new co-reference method uses elements from a broad range of areas including; query expansion, co-reference identification, short text semantic similarity and fuzzy logic. The new method was tested against real world collections information, the results of which suggest that, in terms of the quality of the co-referent matches found, the new co-reference identification method is at least as effective as a manual search. The number of co-referent matches found however, is higher using the new method. The approach presented here is capable of searching collections stored using differing metadata schemas. More significantly, the approach is capable of identifying potential co-reference matches despite the highly heterogeneous and syntax independent nature of the Gallery, Library Archive and Museum (GLAM) search space and the photo-history domain in particular. The most significant benefit of the new method is, however, that it requires comparatively little manual intervention. A co-reference search using it has, therefore, significantly lower person hour requirements than a manually conducted search. In addition to the overall co-reference identification method, this thesis also presents: • A novel and computationally lightweight short text semantic similarity metric. This new metric has a significantly higher throughput than the current prominent techniques but a negligible drop in accuracy. • A novel method for comparing photographic processes in the presence of variable terminology and inaccurate field information. This is the first computational approach to do so.

<p>Cilj ovog istraživanja bio je da se razvije model za ocjenu<br />efikasnosti i efektivnosti željezničkih operatera koji bi<br />bio u funkciji povećanja konkurentskih sposobnosti istih. Da<br />bi se to ostvarilo na osnovu stanja u literaturi i praksi,<br />definisani su i vrednovani kriterijumi koji utiču na<br />efikasnost i efektivnost i izvršen njihov izbor primjenom<br />fazi AXP. Razvijeni su modeli za ocjenu efikasnosti<br />željezničkog operatera. U razvoju modela korišćeni su<br />principi DEA i fazi logike da se modeluje neodređenost u<br />podacima i koristi ekspertsko znanje za ocjenu. Testiranjem<br />modela na željezničkom operateru u BiH zaključeno je da je<br />model primjenljiv kao alat za ocjenu efikasnosti.</p> / <p>The objective of this study was to develop a model for evaluating the<br />effectiveness and efficiency of railway undertakings, which would be<br />aimed at increasing the competitive capabilities of the railway<br />undertakings. In order to achieve this on the basis of the state in<br />literature and practice, the criteria that affect the efficiency and<br />effectiveness are defined and evaluated and their selection is made<br />with the application of Fuzzy AHP. Models for the assessment of the<br />efficiency of railway undertakings have been developed. In developing<br />the model, the principles of DEA and Fuzzy logic have been used to<br />model the uncertainty in the data and to use the expertise for<br />assessment. By testing the model on the railway undertaking in Bosnia<br />and Herzegovina it was concluded that the model is useful as a tool for<br />the assessment of efficiency.</p>

Spatial databases store geometric objects and capture spatial relationships that can be used to represent key features of the human face. One can search spatial databases for these objects, and seek the relationships between them, using fuzzy logic to provide a natural way to describe the human face for the purposes of facial characterization. This study focuses on community perception of short, average, or long nose length. Three algorithms were used to update community opinion of nose length. All three methods showed similar trends in nose length classification which could indicate that the effort to extract spatial data from images to classify nose length is not as crucial as previously thought since community consensus will ultimately give similar results. However, additional testing with larger groups is needed to further validate any conclusion that spatial data can be eliminated.

The thesis deals with optimization of the continuous slab casting process. The thesis summarizes the basic analytical and empirical findings concerning to the solidification process, the numerical modeling and the selected optimization techniques. Physical conditions and factors that affect the quality of steel including their relationships are also described. The basis of the solution strategy is the original numerical model of the temperature field in its off-line version. The numerical model was verified by the real historical data. The optimization part is based on the fuzzy logic implemented above the numerical model. The optimization algorithm is used for the optimal control of the casting process. The universal usage of the optimization model is demonstrated on several cases, e.g. the finding of optimal casting parameters that ensure the high quality of products, the optimal reactions on breakdown situations, the determination of an optimal relationship between casting parameters, etc. Based on optimization results, the suitable caster modification to increase the surface temperature at the unbending point was proposed. The whole concept of the numerical and optimization model is general and it can be applied to arbitrary slab or billet continuous casting.

Master of Science / Department of Electrical Engineering / Shelli K. Starrett / Modern power systems are integrated, complex, dynamic systems. Due to the complexity, power system operation and control need to be analyzed using numerical simulation. The load model is one of the least known models among the many components in the power system operation. The two different load models are the static and dynamic models. The ZIP load model has been extensively studied. This has widely applied to composite load models that could maintain constant impedance, constant current, and/or constant power. In this work, various Neural Networks algorithms and fuzzy logic have been used to obtain these ZIP load model coefficients for determining the percentage of constant impedance, current, or power for the various load buses. The inputs are a combination of voltage, voltage change, and power change, or voltage and power, and the outputs consist of the ZIP load model coefficients for determining the type and the percentage of load at the bus. The trained model is used to predict the type and percentage of constant load at other buses using simulated transient data from the 16-generator system. A small study was also done using a dynamic induction machine model in addition to the ZIP load model. As expected, the results show that the dynamic model is more difficult to determine than the static model.