An adaptive neighborhood search algorithm for optimizing stochastic mining complexes

Les métaheuristiques sont très utilisées dans le domaine de l'optimisation discrète. Elles permettent d’obtenir une solution de bonne qualité en un temps raisonnable, pour des problèmes qui sont de grande taille, complexes, et difficiles à résoudre. Souvent, les métaheuristiques ont beaucoup de paramètres que l’utilisateur doit ajuster manuellement pour un problème donné. L'objectif d'une métaheuristique adaptative est de permettre l'ajustement automatique de certains paramètres par la méthode, en se basant sur l’instance à résoudre. La métaheuristique adaptative, en utilisant les connaissances préalables dans la compréhension du problème, des notions de l'apprentissage machine et des domaines associés, crée une méthode plus générale et automatique pour résoudre des problèmes. L’optimisation globale des complexes miniers vise à établir les mouvements des matériaux dans les mines et les flux de traitement afin de maximiser la valeur économique du système. Souvent, en raison du grand nombre de variables entières dans le modèle, de la présence de contraintes complexes et de contraintes non-linéaires, il devient prohibitif de résoudre ces modèles en utilisant les optimiseurs disponibles dans l’industrie. Par conséquent, les métaheuristiques sont souvent utilisées pour l’optimisation de complexes miniers. Ce mémoire améliore un procédé de recuit simulé développé par Goodfellow & Dimitrakopoulos (2016) pour l’optimisation stochastique des complexes miniers stochastiques. La méthode développée par les auteurs nécessite beaucoup de paramètres pour fonctionner. Un de ceux-ci est de savoir comment la méthode de recuit simulé cherche dans le voisinage local de solutions. Ce mémoire implémente une méthode adaptative de recherche dans le voisinage pour améliorer la qualité d'une solution. Les résultats numériques montrent une augmentation jusqu'à 10% de la valeur de la fonction économique.

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Image and video compression play a major role in the world today, allowing the storage and transmission of large multimedia content volumes. However, the processing of this information requires high computational resources, hence the improvement of the computational performance of these compression algorithms is very important. The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based compression algorithm for multimedia contents, namely images, achieving high compression ratios, maintaining good image quality, Rodrigues et al. [2008]. However, in comparison with other existing algorithms, this algorithm takes some time to execute. Therefore, two parallel implementations for GPUs were proposed by Ribeiro [2016] and Silva [2015] in CUDA and OpenCL-GPU, respectively. In this dissertation, to complement the referred work, we propose two parallel versions that run the MMP algorithm in CPU: one resorting to OpenMP and another that converts the existing OpenCL-GPU into OpenCL-CPU. The proposed solutions are able to improve the computational performance of MMP by 3 and 2:7 , respectively. The High Efficiency Video Coding (HEVC/H.265) is the most recent standard for compression of image and video. Its impressive compression performance, makes it a target for many adaptations, particularly for holoscopic image/video processing (or light field). Some of the proposed modifications to encode this new multimedia content are based on geometry-based disparity compensations (SS), developed by Conti et al. [2014], and a Geometric Transformations (GT) module, proposed by Monteiro et al. [2015]. These compression algorithms for holoscopic images based on HEVC present an implementation of specific search for similar micro-images that is more efficient than the one performed by HEVC, but its implementation is considerably slower than HEVC. In order to enable better execution times, we choose to use the OpenCL API as the GPU enabling language in order to increase the module performance. With its most costly setting, we are able to reduce the GT module execution time from 6.9 days to less then 4 hours, effectively attaining a speedup of 45 .

Circadian Phase Control - Genetic Algorithm and Non-linear Control Approach

This paper deals with the phase control for Neurospora circadian rhythm. The nonlinear control, given by tuning the parameters (considered as controlled variables) in Neurospora dynamical model, allows the circadian rhythms tracking a reference one. When there are many parameters (e.g. 3 parameters in this paper) and their values are unknown, the adaptive control law reveals its weakness since the parameters converging and control objective must be guaranteed at the same time. We show that this problem can be solved using the genetic algorithm for parameters estimation. Once the unknown parameters are known, the phase control is performed by chaos synchronization technique.

Satellite based multi-scale methods to support governance of Brazil's low-carbon agriculture (ABC Plan).

Brazil's Low Carbon Agriculture is one the initiatives that puts the climate in the agricultural agenda towards a more sustainable and adapted agriculture under global changes. Among the several practices listed and supported by the ABC Plan, zero tillage and integrated crop-livestock-forestry systems including the recovery of degraded pasture are the most relevant ones. The objective of this paper is to present the Geo-ABC Project, a procedure to monitor the implementation of the Brazil?s Low Carbon Agriculture (ABC Plan) and aiming at the development of remote sensing methods to monitor agricultural systems listed in the ABC Plan and adopted at local scale.

Design of experiment in drug development: optimal allocations in multi-arm clinical trials and design space for multi-step processes

In this thesis, we deal with the design of experiments in the drug development process, focusing on the design of clinical trials for treatment comparisons (Part I) and the design of preclinical laboratory experiments for proteins development and manufacturing (Part II). In Part I we propose a multi-purpose design methodology for sequential clinical trials. We derived optimal allocations of patients to treatments for testing the efficacy of several experimental groups by also taking into account ethical considerations. We first consider exponential responses for survival trials and we then present a unified framework for heteroscedastic experimental groups that encompasses the general ANOVA set-up. The very good performance of the suggested optimal allocations, in terms of both inferential and ethical characteristics, are illustrated analytically and through several numerical examples, also performing comparisons with other designs proposed in the literature. Part II concerns the planning of experiments for processes composed of multiple steps in the context of preclinical drug development and manufacturing. Following the Quality by Design paradigm, the objective of the multi-step design strategy is the definition of the manufacturing design space of the whole process and, as we consider the interactions among the subsequent steps, our proposal ensures the quality and the safety of the final product, by enabling more flexibility and process robustness in the manufacturing.

Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks

Several decision and control tasks in cyber-physical networks can be formulated as large- scale optimization problems with coupling constraints. In these "constraint-coupled" problems, each agent is associated to a local decision variable, subject to individual constraints. This thesis explores the use of primal decomposition techniques to develop tailored distributed algorithms for this challenging set-up over graphs. We first develop a distributed scheme for convex problems over random time-varying graphs with non-uniform edge probabilities. The approach is then extended to unknown cost functions estimated online. Subsequently, we consider Mixed-Integer Linear Programs (MILPs), which are of great interest in smart grid control and cooperative robotics. We propose a distributed methodological framework to compute a feasible solution to the original MILP, with guaranteed suboptimality bounds, and extend it to general nonconvex problems. Monte Carlo simulations highlight that the approach represents a substantial breakthrough with respect to the state of the art, thus representing a valuable solution for new toolboxes addressing large-scale MILPs. We then propose a distributed Benders decomposition algorithm for asynchronous unreliable networks. The framework has been then used as starting point to develop distributed methodologies for a microgrid optimal control scenario. We develop an ad-hoc distributed strategy for a stochastic set-up with renewable energy sources, and show a case study with samples generated using Generative Adversarial Networks (GANs). We then introduce a software toolbox named ChoiRbot, based on the novel Robot Operating System 2, and show how it facilitates simulations and experiments in distributed multi-robot scenarios. Finally, we consider a Pickup-and-Delivery Vehicle Routing Problem for which we design a distributed method inspired to the approach of general MILPs, and show the efficacy through simulations and experiments in ChoiRbot with ground and aerial robots.

Robust Adaptive Control with Applications to Multi-Agent Systems and Mobile Robotics

This thesis deals with robust adaptive control and its applications, and it is divided into three main parts. The first part is about the design of robust estimation algorithms based on recursive least squares. First, we present an estimator for the frequencies of biased multi-harmonic signals, and then an algorithm for distributed estimation of an unknown parameter over a network of adaptive agents. In the second part of this thesis, we consider a cooperative control problem over uncertain networks of linear systems and Kuramoto systems, in which the agents have to track the reference generated by a leader exosystem. Since the reference signal is not available to each network node, novel distributed observers are designed so as to reconstruct the reference signal locally for each agent, and therefore decentralizing the problem. In the third and final part of this thesis, we consider robust estimation tasks for mobile robotics applications. In particular, we first consider the problem of slip estimation for agricultural tracked vehicles. Then, we consider a search and rescue application in which we need to drive an unmanned aerial vehicle as close as possible to the unknown (and to be estimated) position of a victim, who is buried under the snow after an avalanche event. In this thesis, robustness is intended as an input-to-state stability property of the proposed identifiers (sometimes referred to as adaptive laws), with respect to additive disturbances, and relative to a steady-state trajectory that is associated with a correct estimation of the unknown parameter to be found.

Multi-phase electric drives for mobility

Multi-phase electrical drives are potential candidates for the employment in innovative electric vehicle powertrains, in response to the request for high efficiency and reliability of this type of application. In addition to the multi-phase technology, in the last decades also, multilevel technology has been developed. These two technologies are somewhat complementary since both allow increasing the power rating of the system without increasing the current and voltage ratings of the single power switches of the inverter. In this thesis, some different topics concerning the inverter, the motor and the fault diagnosis of an electric vehicle powertrain are addressed. In particular, the attention is focused on multi-phase and multilevel technologies and their potential advantages with respect to traditional technologies. First of all, the mathematical models of two multi-phase machines, a five-phase induction machine and an asymmetrical six-phase permanent magnet synchronous machines are developed using the Vector Space Decomposition approach. Then, a new modulation technique for multi-phase multilevel T-type inverters, which solves the voltage balancing problem of the DC-link capacitors, ensuring flexible management of the capacitor voltages, is developed. The technique is based on the proper selection of the zero-sequence component of the modulating signals. Subsequently, a diagnostic technique for detecting the state of health of the rotor magnets in a six-phase permanent magnet synchronous machine is established. The technique is based on analysing the electromotive force induced in the stator windings by the rotor magnets. Furthermore, an innovative algorithm able to extend the linear modulation region for five-phase inverters, taking advantage of the multiple degrees of freedom available in multi-phase systems is presented. Finally, the mathematical model of an eighteen-phase squirrel cage induction motor is defined. This activity aims to develop a motor drive able to change the number of poles of the machine during the machine operation.

Modelling, simulation and control of rotary-wing aircraft in multi-agent scenario

The topic of this thesis is the design and the implementation of mathematical models and control system algorithms for rotary-wing unmanned aerial vehicles to be used in cooperative scenarios. The use of rotorcrafts has many attractive advantages, since these vehicles have the capability to take-off and land vertically, to hover and to move backward and laterally. Rotary-wing aircraft missions require precise control characteristics due to their unstable and heavy coupling aspects. As a matter of fact, flight test is the most accurate way to evaluate flying qualities and to test control systems. However, it may be very expensive and/or not feasible in case of early stage design and prototyping. A good compromise is made by a preliminary assessment performed by means of simulations and a reduced flight testing campaign. Consequently, having an analytical framework represents an important stage for simulations and control algorithm design. In this work mathematical models for various helicopter configurations are implemented. Different flight control techniques for helicopters are presented with theoretical background and tested via simulations and experimental flight tests on a small-scale unmanned helicopter. The same platform is used also in a cooperative scenario with a rover. Control strategies, algorithms and their implementation to perform missions are presented for two main scenarios. One of the main contributions of this thesis is to propose a suitable control system made by a classical PID baseline controller augmented with L1 adaptive contribution. In addition a complete analytical framework and the study of the dynamics and the stability of a synch-rotor are provided. At last, the implementation of cooperative control strategies for two main scenarios that include a small-scale unmanned helicopter and a rover.

Exact and heuristic algorithms for the integrated planning of multi-energy systems

The aim of this thesis is to present exact and heuristic algorithms for the integrated planning of multi-energy systems. The idea is to disaggregate the energy system, starting first with its core the Central Energy System, and then to proceed towards the Decentral part. Therefore, a mathematical model for the generation expansion operations to optimize the performance of a Central Energy System system is first proposed. To ensure that the proposed generation operations are compatible with the network, some extensions of the existing network are considered as well. All these decisions are evaluated both from an economic viewpoint and from an environmental perspective, as specific constraints related to greenhouse gases emissions are imposed in the formulation. Then, the thesis presents an optimization model for solar organic Rankine cycle in the context of transactive energy trading. In this study, the impact that this technology can have on the peer-to-peer trading application in renewable based community microgrids is inspected. Here the consumer becomes a prosumer and engages actively in virtual trading with other prosumers at the distribution system level. Moreover, there is an investigation of how different technological parameters of the solar Organic Rankine Cycle may affect the final solution. Finally, the thesis introduces a tactical optimization model for the maintenance operations’ scheduling phase of a Combined Heat and Power plant. Specifically, two types of cleaning operations are considered, i.e., online cleaning and offline cleaning. Furthermore, a piecewise linear representation of the electric efficiency variation curve is included. Given the challenge of solving the tactical management model, a heuristic algorithm is proposed. The heuristic works by solving the daily operational production scheduling problem, based on the final consumer’s demand and on the electricity prices. The aggregate information from the operational problem is used to derive maintenance decisions at a tactical level.

Analysis Of The Dna Fourier Transform-infrared Microspectroscopic Signature Using An All-reflecting Objective.

The Fourier transform-infrared (FT-IR) signature of dry samples of DNA and DNA-polypeptide complexes, as studied by IR microspectroscopy using a diamond attenuated total reflection (ATR) objective, has revealed important discriminatory characteristics relative to the PO2(-) vibrational stretchings. However, DNA IR marks that provide information on the sample's richness in hydrogen bonds have not been resolved in the spectral profiles obtained with this objective. Here we investigated the performance of an all reflecting objective (ARO) for analysis of the FT-IR signal of hydrogen bonds in DNA samples differing in base richness types (salmon testis vs calf thymus). The results obtained using the ARO indicate prominent band peaks at the spectral region representative of the vibration of nitrogenous base hydrogen bonds and of NH and NH2 groups. The band areas at this spectral region differ in agreement with the DNA base richness type when using the ARO. A peak assigned to adenine was more evident in the AT-rich salmon DNA using either the ARO or the ATR objective. It is concluded that, for the discrimination of DNA IR hydrogen bond vibrations associated with varying base type proportions, the use of an ARO is recommended.

Doehlert Design-desirability Function Multi-criteria Optimal Separation Of 17 Phenolic Compounds From Extra-virgin Olive Oil By Capillary Zone Electrophoresis.

In Brazil, the consumption of extra-virgin olive oil (EVOO) is increasing annually, but there are no experimental studies concerning the phenolic compound contents of commercial EVOO. The aim of this work was to optimise the separation of 17 phenolic compounds already detected in EVOO. A Doehlert matrix experimental design was used, evaluating the effects of pH and electrolyte concentration. Resolution, runtime and migration time relative standard deviation values were evaluated. Derringer's desirability function was used to simultaneously optimise all 37 responses. The 17 peaks were separated in 19min using a fused-silica capillary (50μm internal diameter, 72cm of effective length) with an extended light path and 101.3mmolL(-1) of boric acid electrolyte (pH 9.15, 30kV). The method was validated and applied to 15 EVOO samples found in Brazilian supermarkets.

The Crystal-t Algorithm: A New Approach To Calculate The Sle Of Lipidic Mixtures Presenting Solid Solutions.

Lipidic mixtures present a particular phase change profile highly affected by their unique crystalline structure. However, classical solid-liquid equilibrium (SLE) thermodynamic modeling approaches, which assume the solid phase to be a pure component, sometimes fail in the correct description of the phase behavior. In addition, their inability increases with the complexity of the system. To overcome some of these problems, this study describes a new procedure to depict the SLE of fatty binary mixtures presenting solid solutions, namely the Crystal-T algorithm. Considering the non-ideality of both liquid and solid phases, this algorithm is aimed at the determination of the temperature in which the first and last crystal of the mixture melts. The evaluation is focused on experimental data measured and reported in this work for systems composed of triacylglycerols and fatty alcohols. The liquidus and solidus lines of the SLE phase diagrams were described by using excess Gibbs energy based equations, and the group contribution UNIFAC model for the calculation of the activity coefficients of both liquid and solid phases. Very low deviations of theoretical and experimental data evidenced the strength of the algorithm, contributing to the enlargement of the scope of the SLE modeling.

Behavior Of Listeria Monocytogenes In A Multi-species Biofilm With Enterococcus Faecalis And Enterococcus Faecium And Control Through Sanitation Procedures.

The formation of mono-species biofilm (Listeria monocytogenes) and multi-species biofilms (Enterococcus faecium, Enterococcus faecalis, and L. monocytogenes) was evaluated. In addition, the effectiveness of sanitation procedures for the control of the multi-species biofilm also was evaluated. The biofilms were grown on stainless steel coupons at various incubation temperatures (7, 25 and 39°C) and contact times (0, 1, 2, 4, 6 and 8days). In all tests, at 7°C, the microbial counts were below 0.4 log CFU/cm(2) and not characteristic of biofilms. In mono-species biofilm, the counts of L. monocytogenes after 8days of contact were 4.1 and 2.8 log CFU/cm(2) at 25 and 39°C, respectively. In the multi-species biofilms, Enterococcus spp. were present at counts of 8 log CFU/cm(2) at 25 and 39°C after 8days of contact. However, the L. monocytogenes in multi-species biofilms was significantly affected by the presence of Enterococcus spp. and by temperature. At 25°C, the growth of L. monocytogenes biofilms was favored in multi-species cultures, with counts above 6 log CFU/cm(2) after 8days of contact. In contrast, at 39°C, a negative effect was observed for L. monocytogenes biofilm growth in mixed cultures, with a significant reduction in counts over time and values below 0.4 log CFU/cm(2) starting at day 4. Anionic tensioactive cleaning complemented with another procedure (acid cleaning, disinfection or acid cleaning+disinfection) eliminated the multi-species biofilms under all conditions tested (counts of all micro-organisms<0.4 log CFU/cm(2)). Peracetic acid was the most effective disinfectant, eliminating the multi-species biofilms under all tested conditions (counts of the all microorganisms <0.4 log CFU/cm(2)). In contrast, biguanide was the least effective disinfectant, failing to eliminate biofilms under all the test conditions.

Multimodal Mri-based Study In Patients With Spg4 Mutations.

Mutations in the SPG4 gene (SPG4-HSP) are the most frequent cause of hereditary spastic paraplegia, but the extent of the neurodegeneration related to the disease is not yet known. Therefore, our objective is to identify regions of the central nervous system damaged in patients with SPG4-HSP using a multi-modal neuroimaging approach. In addition, we aimed to identify possible clinical correlates of such damage. Eleven patients (mean age 46.0 ± 15.0 years, 8 men) with molecular confirmation of hereditary spastic paraplegia, and 23 matched healthy controls (mean age 51.4 ± 14.1years, 17 men) underwent MRI scans in a 3T scanner. We used 3D T1 images to perform volumetric measurements of the brain and spinal cord. We then performed tract-based spatial statistics and tractography analyses of diffusion tensor images to assess microstructural integrity of white matter tracts. Disease severity was quantified with the Spastic Paraplegia Rating Scale. Correlations were then carried out between MRI metrics and clinical data. Volumetric analyses did not identify macroscopic abnormalities in the brain of hereditary spastic paraplegia patients. In contrast, we found extensive fractional anisotropy reduction in the corticospinal tracts, cingulate gyri and splenium of the corpus callosum. Spinal cord morphometry identified atrophy without flattening in the group of patients with hereditary spastic paraplegia. Fractional anisotropy of the corpus callosum and pyramidal tracts did correlate with disease severity. Hereditary spastic paraplegia is characterized by relative sparing of the cortical mantle and remarkable damage to the distal portions of the corticospinal tracts, extending into the spinal cord.