VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Visual attractiveness in routing problems: A review

Enhancing visual attractiveness in a routing plan has proven to be an effective way to facilitate practical implementation and positive collaboration among planning and operational levels in transportation. Several authors, driven by the requests of practitioners, have considered, either explicitly or implicitly, such aspect in the optimization process for different routing applications. However, due to its subjective nature, there is not a unique way of evaluating the visual attractiveness of a routing solution. The aim of this paper is to provide an overview of the literature on visual attractiveness. In particular, we analyze and experimentally compare the different metrics that were used to model the visual attractiveness of a routing plan and provide guidelines that planners and researchers can use to select the method that better suits their needs.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Vigo, Daniele. Universidad de Bologna; ItaliaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case

Automated Design of Neural Network Architecture for Classification

This Ph.D. thesis deals with finding a good architecture of a neural network classifier. The focus is on methods to improve the performance of existing architectures (i.e. architectures that are initialised by a good academic guess) and automatically building neural networks. An introduction to the Multi-Layer feed-forward neural network is given and the most essential properties for neural networks; there ability to learn from examples is discussion. Topics like traning and generalisation are treated in more explicit. On the basic of this dissuscion methods for finding a good architecture of the network described. This includes methods like; Early stopping, Cross validation, Regularisation, Pruning and various constructions algorithms (methods that successively builds a network). New ideas of combining units with different types of transfer functions like radial basis functions and sigmoid or threshold functions led to the development of a new construction algorithm for classification. The algorithm called "GLOCAL" is fully described. Results from these experiments real life data from a Synthetic Aperture Radar (SAR) are provided.The thesis was written so people from the industry and graduate students who are interested in neural networks hopeful would find it useful.Key words: Neural networks, Architectures, Training, Generalisation deductive and construction algorithms

Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization

The surveillance mission requires aircraft to fly from a starting point through defended terrain to targets and return to a safe destination (usually the starting point). The process of selecting such a flight path is known as the Mission Route Planning (MRP) Problem and is a three-dimensional, multi-criteria (fuel expenditure, time required, risk taken, priority targeting, goals met, etc.) path search. Planning aircraft routes involves an elaborate search through numerous possibilities, which can severely task the resources of the system being used to compute the routes. Operational systems can take up to a day to arrive at a solution due to the combinatoric nature of the problem. This delay is not acceptable because timeliness of obtaining surveillance information is critical in many surveillance missions. Also, the information that the software uses to solve the MRP may become invalid during computation. An effective and efficient way of solving the MRP with multiple aircraft and multiple targets is desired. One approach to finding solutions is to simplify and view the problem as a two-dimensional, minimum path problem. This approach also minimizes fuel expenditure, time required, and even risk taken. The simplified problem is then the Traveling Salesman Problem (TSP)

Applying the Herman-Beta probabilistic method to MV feeders

Includes bibliographical references.The assessment of voltage drop in radial feeders is an important element in the process of network design and planning. This task is however not straight forward as the operation of modern power systems is highly influenced by a variety of uncertain and random variables such as stochasticity in load demand and power generation from renewable energy resources. Classic deterministic methods which model load demand and generation with fixed mean values consequently turn out to be inadequate and inaccurate tools for the analysis of power flow in the uncertainty-filled system. Statistically based methods become more suitable for such a task as they account for input variable uncertainties in their calculation of load flow. In the South African context, the Herman Beta algorithm, a probabilistic load flow tool developed by Herman et al. was adopted as the method for voltage assessment in Low Voltage (LV) network. The method was shown to have significant advantages compared with many other probabilistic methods for LV feeders, as investigated by Sellick and Gaunt. Its performance with regards to speed and accuracy is superior to deterministic, numeric probabilistic and other analytical probabilistic methods. The evolving connections of smaller generators, referred to as Distributed Generators (DGs), to the utility grid inspired the extension of the HB algorithm to active LV distribution networks. The HB algorithm was however formulated specifically for LV feeders. The assumptions of purely resistive feeders and unity power factor loads make it unsuitable for the Medium Voltage (MV) distribution network. In South Africa, deterministic methods are still being used for network design in MV distribution networks. This means that the drawbacks of such methods, for example inaccuracy and computational burden with large systems, are characteristic of the quality of network design in MV feeders. The performance of the HB algorithm together with the advantages and superiority of load modelling using the Beta probability density function (Beta pdf) suggested that modifying the input parameters could allow the HB algorithm to be used for voltage calculations on MV networks. This work therefore involves the adaptation of the way the HB algorithm is used, to make it suitable for voltage calculations on MV feeders. The HB algorithm for LV feeders is firstly analysed, coded into MATLAB, tested and then validated. Following this, the input parameters for feeder impedance and load current are modified to include the effects of reactance and non-unity power factor loads, using approximate modelling techniques. For reactance, the modulus or absolute value of the complex impedance is used in place of the resistance, to compensate for the line reactance. The load current is adjusted by inflating it by the power factor. The results of calculations with the HB algorithm are tested against a Monte-Carlo Simulation (MCS) solution of the feeder with an accurate model (full representation of feeder impedance and load power factor). The approach is extended to include shunt capacitor connections and DG in voltage calculations using the HB algorithm and testing the results with MCS. The outcomes of this research are that the approach of adjusting the input parameters of line resistance and load current significantly improves the accuracy of calculations using the HB algorithm for MV feeders. Comparison with the results of MC simulations indicates that the error of voltage calculations on MV feeders will be less than 2% of the 'accurate probabilistic value'. However, it is not possible to predict the error for a particular application

DYNAMICAL SENSITIVITY ANALYSES OF KINETIC MODELS IN BIOLOGY

Ph.DDOCTOR OF PHILOSOPH