No description available.

M.Sc.

M.Sc.

Thesis (Ph.D.)--York University, 2007. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 109-110). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:NR32041

Thesis (Ph. D.) -- California Institute of Technology, 2010. / Title from home page (viewed 06/21/2010). Advisor and committee chair names found in the thesis' metadata record in the digital repository. Includes bibliographical references.

Le calcul ensembliste par analyse par intervalles et ses applications

Thesis (M.S. in C.E.E.)--School of Civil and Environmental Engineering, Georgia Institute of Technology, 2004. Directed by Rafi Muhanna. / Includes bibliographical references (leaves 125-126).

In a mobile robotic system, the interaction with the surrounding environment is essential in order to complete tasks such as localisation and mapping. This interaction can only be conducted by means of sensors that permit the accumulation of a large amount of information from several sources. However, this information is useless without adequate interpretation; thus, in order to accurately determine the positioning of the robot, it is necessary to identify and characterise landmarks in the environment required to serve as anchoring points for both localisation and mapping. Having constructed the map, an accurate analysis of the information gathered is vital. In this manner, this work is focused on two main aspects of any mobile robotic system: first, the detection and characterisation of highly descriptive landmarks by using image and point cloud processing techniques; and second, the geometrical and spatial analysis of the information gathered from the environment. For the former, two novel techniques based on image processing and geometrical analysis are presented; for the latter, a guaranteed technique for the parameter estimation of primitive shapes using interval analysis is proposed.

No description available.

This thesis is aimed at the optimum design of uncertain mechanical components and systems involving multiple objectives and constraints. There are various mechanical and design problems that are encountered every now and then which require the output that equalize several conflicting objectives. In recent years several methods have been developed to find a solution to multiobjective problems. The most efficient method for obtaining a compromise solution is the game theory method, which is based on the Pareto minimum or optimum solution. A thorough methodology is developed, and subsequently applied to three examples problems. The first problem is to design four helical springs which are further used to support a milling machine. The objective is to minimize the weight of the spring, also to minimize the deflection, and to maximize the natural frequency thus making the problem as a multiobjective problem. Further the subjected constraint is the shear stress constraint. After finding the optimized solution of the deterministic problem, the problem is again solved using Stochastic Nonlinear Programming, and after that it is solved using Interval Analysis. Game theory is used individually in all the three cases. The second problem is to design a gear box where the objectives are defined as the weight of the gear box, stress developed in the shaft 1, and the stress developed in shaft 2. It is subjected to nine constraints which are bending stress in teeth, contact stress of teeth, transverse displacement of shafts 1 and 2, and constraints related to the torque. The third problem is to design a power screw and the objective is to minimize the volume of the screw, and to maximize the critical buckling load and thus making it a multiobjective problem. It is subjected to constraints of being screw to be self locking, then the shear stress in screw thread, and the bearing stress in threads. The results of all the three problems that are achieved using Deterministic, Stochastic Nonlinear Programming, and Interval Analysis Method are tabulated, and the value of each objective achieved using these three methods for each problem at a time are compared to find out the most optimized solution.