Mathematics behind System Dynamics

The goal of this project is to introduce modeling and representation methods to solve dynamics problems. The intuitive System Dynamics representation is introduced and backed up with advanced mathematical concepts such as differential equations and Control theory techniques. This project attempts to illustrate both abstract and intuitive approaches based on examples arising in social and business systems. The topics include graphical representations, delays, numerical methods, and common behavioral pattern of typical structures

Hierarchical Manipulation for Constructing Free Standing Structures

abstract: In order for a robot to solve complex tasks in real world, it needs to compute discrete, high-level strategies that can be translated into continuous movement trajectories. These problems become increasingly difficult with increasing numbers of objects and domain constraints, as well as with the increasing degrees of freedom of robotic manipulator arms. The first part of this thesis develops and investigates new methods for addressing these problems through hierarchical task and motion planning for manipulation with a focus on autonomous construction of free-standing structures using precision-cut planks. These planks can be arranged in various orientations to design complex structures; reliably and autonomously building such structures from scratch is computationally intractable due to the long planning horizon and the infinite branching factor of possible grasps and placements that the robot could make. An abstract representation is developed for this class of problems and show how pose generators can be used to autonomously compute feasible robot motion plans for constructing a given structure. The approach was evaluated through simulation and on a real ABB YuMi robot. Results show that hierarchical algorithms for planning can effectively overcome the computational barriers to solving such problems. The second part of this thesis proposes a deep learning-based algorithm to identify critical regions for motion planning. Further investigation is done whether these learned critical regions can be translated to learn high-level landmark actions for automated planning.Dissertation/ThesisMasters Thesis Computer Science 201

Testing an Intentional Naming Scheme Using Genetic Algorithms

Abstract. Various attempts have been made to use genetic algorithms (GAs) for software testing, a problem that consumes a large amount of time and eort in software development. We demonstrate the use of GAs in automating testing of complex data structures and methods for manipulating them, which to our knowledge has not been successfully displayed before on non-trivial software structures. We evaluate the ef-fectiveness of our GA-based test suite generation technique by applying it to test the design and implementation of the Intentional Naming Sys-tem (INS), a new scheme for resource discovery and service location in a dynamic networked environment. Our analysis using GAs reveals serious problems with both the design of INS and its inventors ' implementation.

Implementing abstract multigrid or multilevel methods

Multigrid methods can be formulated as an algorithm for an abstract problem that is independent of the partial differential equation, domain, and discretization method. In such an abstract setting, problems not arising from partial differential equations can be treated. A general theory exists for linear problems. The general theory was motivated by a series of abstract solvers (Madpack). The latest version was motivated by the theory. Madpack now allows for a wide variety of iterative and direct solvers, preconditioners, and interpolation and projection schemes, including user callback ones. It allows for sparse, dense, and stencil matrices. Mildly nonlinear problems can be handled. Also, there is a fast, multigrid Poisson solver (two and three dimensions). The type of solvers and design decisions (including language, data structures, external library support, and callbacks) are discussed. Based on the author's experiences with two versions of Madpack, a better approach is proposed. This is based on a mixed language formulation (C and FORTRAN + preprocessor). Reasons for not using FORTRAN, C, or C++ (individually) are given. Implementing the proposed strategy is not difficult

CPSP-tools – Exact and complete algorithms for high-throughput 3D lattice protein studies

<p>Abstract</p> <p>Background</p> <p>The principles of protein folding and evolution pose problems of very high inherent complexity. Often these problems are tackled using simplified protein models, e.g. lattice proteins. The CPSP-tools package provides programs to solve exactly and completely the problems typical of studies using 3D lattice protein models. Among the tasks addressed are the prediction of (all) globally optimal and/or suboptimal structures as well as sequence design and neutral network exploration.</p> <p>Results</p> <p>In contrast to stochastic approaches, which are not capable of answering many fundamental questions, our methods are based on fast, non-heuristic techniques. The resulting tools are designed for high-throughput studies of 3D-lattice proteins utilising the Hydrophobic-Polar (HP) model. The source bundle is freely available <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p> <p>Conclusion</p> <p>The CPSP-tools package is the first set of exact and complete methods for extensive, high-throughput studies of non-restricted 3D-lattice protein models. In particular, our package deals with cubic and face centered cubic (FCC) lattices.</p

A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

New consensus-based algorithms for quality assessment in protein structure prediction

Title from PDF of title page (University of Missouri--Columbia, viewed on April 6, 2011).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Yi Shang.M.S. University of Missouri--Columbia 2010.Two of the essential tasks in protein tertiary structure prediction are predicting quality and selecting the best quality model from given model structures. Finding solutions to these problems are fundamental to understanding the nature of proteins and advancing in protein research area. In this thesis, we present efficient algorithms that tackle both problems effectively. The algorithms are developed on the well-known consensus-based idea that has been continuously successful since CASP6. For assessing the quality of structures, we develop several new methods based on the idea of removing redundant structures and outliers. The algorithms aims at finding suitable reference sets in computing the consensus-score in order to improve the existing algorithms. The methods can use any suitable pair-wise similarity measurement between a pair of models such as GDT-TS and Q score. We also develop a very efficient method for computing Q score for large size problem. In our experimental results, the algorithms are applied to CASP8 dataset and have achieved the superior performance over existing state-of-the-art methods including the top1 method in the QA category of CASP8. For the selecting the best model structure, our new methods are effective and perform better than other best-performing scoring functions by up to 7.6% based on the actual GDT-TS of top1 selected model to the native structure. The selection result is obtained by our method using Q score are slightly worse than those obtained using GDT-TS, but using pair-wise Q score method is in general about 15 times faster than using pair-wise GDT-TS method.Includes bibliographical reference