On parallel computing for stochastic optimization models and algorithms

167 p.

Protein structure refinement algorithms

Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics.

Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMCGRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.

Conduction-electron localized-moment interaction in palladium-silicon base amorphous alloys containing transition metals

The electrical and magnetic properties of amorphous alloys obtained by rapid quenching from the liquid state have been studied. The composition of these alloys corresponds to the general formula M_xPd_80-xSi₂₀, in which M stands for a metal of the first transition series between chromium and nickel and x is its atomic concentration. The concentration ranges within which an amorphous structure could be obtained were: from 0 to 7 for Cr, Mn and Fe, from 0 to 11 for Co and from 0 to 15 for Ni. A well-defined minimum in the resistivity vs temperature curve was observed for all alloys except those containing nickel. The alloys for which a resistivity minimum was observed had a negative magnetoresistivity approximately proportional to the square of the magnetization and their susceptibility obeyed the Curie-Weiss law in a wide temperature range. For concentrated Fe and Co alloys the resistivity minimum was found to coexist with ferromagnetism. These observations lead to the conclusion that the present results are due to a s-d exchange interaction. The unusually high resistivity minimum temperature observed in the Cr alloys is interpreted as a result of a high Kondo temperature and a large s-d exchange integral. A low Fermi energy of the amorphous alloys (3.5 eV) is also responsible for the anomalies due to the s-d exchange interaction.

Phase unwrapping algorithms, respectively, based on path-following and discrete cosine transform

This paper describes a path-following phase unwrapping algorithm and a phase unwrapping algorithm based on discrete cosine transform (DCT) which accelerates the Computation and suppresses the propagation of noise. Through analysis of fringe pattern with serious noises simulated in mathematic model, we make a contrast between path-following algorithm and DCT algorithm. The advantages and disadvantages or analytical fringe pattern are also given through comparison of two algorithms. Three-dimensional experimental results have been given to prove the validity of these algorithms. Despite DCT phase unwrapping technique robustness and speed in some cases, it cannot be unwrapping inconsistencies phase. The path-following algorithm can be used in automation analysis of fringe patterns with little influence of noise. (c) 2007 Elsevier GmbH. All rights reserved.

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

179 p.

Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms

This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.