A Parallel Implementation of k-Means in MATLAB

The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated

Gain-Scheduled Controller for Fault Accommodation in Linear Parameter Varying Systems with Imprecise Measurements

We present the design of H∞ and H2 gain-scheduled fault-accommodation controllers for discrete-time Linear Parameter Varying systems. We design our conditions as Bilinear Matrix Inequalities, assuming that the scheduled parameters are imprecise, which is a commonly found characteristic of practical applications that happens due to measurement noise and inaccuracy on its estimation/acquisition procedure. The proposed solution is based on the use of a multi-simplex approach for solving the main conditions, which guarantees the stability of the system under imprecise measurements on the scheduling parameters. The efficacy of the proposed approach is illustrated with a numerical example

A parallel searching algorithm for the insetting procedure in Matlab Parallel Toolbox

In this paper we present the implementation of a parallel searching algorithm, which is used for the insetting procedure in cartography. The calculation time of the above procedure is very long due to the fact that the datasets in cartography are maps with large and very large resolution. The purpose of this proposal is to reduce the calculation time in a multicore machine with shared memory. The proposed algorithm and the performance tests are developed in Matlab Parallel Toolbox. © 2012 Polish Info Processing Socit

High-Dimensional Design Evaluations For Self-Aligning Geometries

Physical connectors with self-aligning geometry aid in the docking process for many robotic and automatic control systems such as robotic self-reconfiguration and air-to-air refueling. This self-aligning geometry provides a wider range of acceptable error tolerance in relative pose between the two rigid objects, increasing successful docking chances. In a broader context, mechanical alignment properties are also useful for other cases such as foot placement and stability, grasping or manipulation. Previously, computational limitations and costly algorithms prevented high-dimensional analysis. The algorithms presented in this dissertation will show a reduced computational time and improved resolution for this kind of problem. This dissertation reviews multiple methods for evaluating modular robot connector geometries as a case study in determining alignment properties. Several metrics are introduced in terms of the robustness of the alignment to errors across the full dimensional range of possible offsets. Algorithms for quantifying error robustness will be introduced and compared in terms of accuracy, reliability, and computational cost. Connector robustness is then compared across multiple design parameters to find trends in alignment behavior. Methods developed and compared include direct simulation and contact space analysis algorithms (geometric by a \u27pre-partitioning\u27 method, and discrete by flooding). Experimental verification for certain subsets is also performed to confirm the results. By evaluating connectors using these algorithms we obtain concrete metric values. We then quantitatively compare their alignment capabilities in either SE(2) or SE(3) under a pseudo-static assumption

Nonlinear and distributed sensory estimation

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Bayesian Uncertainty Analysis and Decision Support for Complex Models of Physical Systems with Application to Production Optimisation of Subsurface Energy Resources

Important decision making problems are increasingly addressed using computer models for complex real world systems. However, there are major limitations to their direct use including: their complex structure; large numbers of inputs and outputs; the presence of many sources of uncertainty; which is further compounded by their long evaluation times. Bayesian methodology for the analysis of computer models has been extensively developed to perform inference for the physical systems. In this thesis, the Bayesian uncertainty analysis methodology is extended to provide robust decision support under uncertainty. Bayesian emulators are employed as a fast and efficient statistical approximation for computer models. We establish a hierarchical Bayesian emulation framework that exploits known constrained simulator behaviour in constituents of the decision support utility function. In addition, novel Bayesian emulation methodology is developed for computer models with structured partial discontinuities. We advance the crucial uncertainty quantification methodology to perform a robust decision analysis developing a technique to assess and remove linear transformations of the utility function induced by sources of uncertainty to which conclusions are invariant, as well as incorporating structural model discrepancy and decision implementation error. These are encompassed within a novel iterative decision support procedure which acknowledges utility function uncertainty resulting from the separation of the analysts and final decision makers to deliver a robust class of decisions, along with any additional information, for further consideration. The complete toolkit is successfully demonstrated via an application to the problem of optimal petroleum field development, including an international and commercially important benchmark challenge

Toll competition in highway transportation networks

Within a highway transportation network, the social welfare implications of two different groups of agents setting tolls in competition for revenues are studied. The first group comprises private sector toll road operators aiming to maximise revenues. The second group comprises local governments or jurisdictions who may engage in tax exporting. Extending insights from the public economics literature, jurisdictions tax export because when setting tolls to maximise welfare for their electorate, they simultaneously benefit from revenues from extra-jurisdictional users. Hence the tolls levied by both groups will be higher than those intended solely to internalise congestion, which then results in welfare losses. Therefore the overarching question investigated is the extent of welfare losses stemming from such competition for toll revenues. While these groups of agents are separately studied, the interactions between agents in each group in competition can be modelled within the common framework of Equilibrium Problems with Equilibrium Constraints. Several solution algorithms, adapting methodologies from microeconomics as well as evolutionary computation, are proposed to identify Nash Equilibrium toll levels. These are demonstrated on realistic transportation networks. As an alternative paradigm to competition, the possibilities for co-operation between agents in each group are also explored. In the case of toll road operators, the welfare consequences of competition could be positive or adverse depending on the interrelationships between the toll roads in competition. The results therefore generalise those previously obtained to a more realistic setting investigated here. In the case of competition between jurisdictions, it is shown that the fiscal externality of tax exporting resulting from their toll setting decisions can substantially reduce the welfare gains from internalising congestion. The ability of regulation, co-operation and bilateral bargaining to reduce the welfare losses are assessed. The research thus contributes to informing debates regarding the appropriate level of institutional governance for toll pricing policies