Conception optimale d’une chaîne de traction électrique pour une voiture de type Formule SAE

La Formule SAE (Society of Automotive Engineers) est une compétition étudiante consistant en la conception et la fabrication d’une voiture de course monoplace. De nombreux événements sont organisés à chaque année au cours desquels plusieurs universités rivalisent entre elles lors d’épreuves dynamiques et statiques. Celles-ci comprennent l’évaluation de la conception, l’évaluation des coûts de fabrication, l’accélération de la voiture, etc. Avec plus de 500 universités participantes et des événements annuels sur tous les continents, il s’agit de la plus importante compétition d’ingénierie étudiante au monde. L’équipe ULaval Racing a participé pendant plus de 20 ans aux compétitions annuelles réservées aux voitures à combustion. Afin de s’adapter à l’électrification des transports et aux nouvelles compétitions destinées aux voitures électriques, l’équipe a conçu et fabriqué une chaîne de traction électrique haute performance destinée à leur voiture 2015. L’approche traditionnelle employée pour concevoir une motorisation électrique consiste à imposer les performances désirées. Ces critères comprennent l’inclinaison maximale que la voiture doit pouvoir gravir, l’autonomie désirée ainsi qu’un profil de vitesse en fonction du temps, ou tout simplement un cycle routier. Cette approche n’est malheureusement pas appropriée pour la conception d’une traction électrique pour une voiture de type Formule SAE. Ce véhicule n’étant pas destiné à la conduite urbaine ou à la conduite sur autoroute, les cycles routiers existants ne sont pas représentatifs des conditions d’opération du bolide à concevoir. Ainsi, la réalisation de ce projet a nécessité l’identification du cycle d’opération routier sur lequel le véhicule doit opérer. Il sert de point de départ à la conception de la chaîne de traction composée des moteurs, de la batterie ainsi que des onduleurs de tension. L’utilisation d’une méthode de dimensionnement du système basée sur un algorithme d’optimisation génétique, suivie d’une optimisation locale couplée à une analyse par éléments-finis a permis l’obtention d’une solution optimale pour les circuits de type Formule SAE. La chaîne de traction conçue a été fabriquée et intégrée dans un prototype de voiture de l’équipe ULaval Racing lors de la saison 2015 afin de participer à diverses compétitions de voitures électriques.

In silico metabolic modelling of Saccharomyces cerevisiae for the overproduction of succinate using OptKnock and OptGene

Succinate is a naturally occurring metabolite in organism’s cell and is industrially important chemical with various applications in food and pharmaceutical industry. It is also widely used to produce bio-degradable plastics, surfactants, detergents etc. In last decades, emphasis has been given to bio-based chemical production using industrial biotechnology route rather than fossil-based production considering sustainability and environment friendly economy. In this thesis I am presenting a computational model for silico metabolic engineering of Saccharomyces cerevisiae for large scale production of succinate. For metabolic modelling, I have used OptKnock and OptGene optimization algorithms to identify the reactions to delete from the genome-scale metabolic model of S. cerevisiae to overproduce succinate by coupling with organism’s growth. Both OptKnock and OptGene proposed numerous straightforward and non-intuitive deletion strategies when number of constraints including growth constraint to the model were applied. The most interesting strategy identified by both algorithms was deletion combination of pyruvate decarboxylase and Ubiquinol:ferricytochrome c reductase(respiratory enzyme) reactions thereby also suggesting anaerobic fermentation of the organism in glucose medium. Such strategy was never reported earlier for growth-coupled succinate production in S.cerevisiae.

Optimisation of digitally coded test vectors for mixed-signal components

International audience

Algoritmos de otimização e o problema inverso aplicados à identificação de danos estruturais com o uso do MEF

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Path relinking for the fixed spectrum frequency assignment problem

International audience

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.

Distance and overcurrent relay coordination considering non standardized inverse time curves

Protective relaying comprehends several procedures and techniques focused on maintaining the power system working safely during and after undesired and abnormal network conditions, mostly caused by faulty events. Overcurrent relay is one of the oldest protective relays, its operation principle is straightforward: when the measured current is greater than a specified magnitude the protection trips; less variables are required from the system in comparison with other protections, causing the overcurrent relay to be the simplest and also the most difficult protection to coordinate; its simplicity is reflected in low implementation, operation, and maintenance cost. The counterpart consists in the increased tripping times offered by this kind of relays mostly before faults located far from their location; this problem can be particularly accentuated when standardized inverse-time curves are used or when only maximum faults are considered to carry out relay coordination. These limitations have caused overcurrent relay to be slowly relegated and replaced by more sophisticated protection principles, it is still widely applied in subtransmission, distribution, and industrial systems. In this work, the use of non standardized inverse-time curves, the model and implementation of optimization algorithms capable to carry out the coordination process, the use of different levels of short circuit currents, and the inclusion of distance relays to replace insensitive overcurrent ones are proposed methodologies focused on the overcurrent relay performance improvement. These techniques may transform the typical overcurrent relay into a more sophisticated one without changing its fundamental principles and advantages. Consequently a more secure and still economical alternative can be obtained, increasing its implementation area

Robustness in facility location

Facility location concerns the placement of facilities, for various objectives, by use of mathematical models and solution procedures. Almost all facility location models that can be found in literature are based on minimizing costs or maximizing cover, to cover as much demand as possible. These models are quite efficient for finding an optimal location for a new facility for a particular data set, which is considered to be constant and known in advance. In a real world situation, input data like demand and travelling costs are not fixed, nor known in advance. This uncertainty and uncontrollability can lead to unacceptable losses or even bankruptcy. A way of dealing with these factors is robustness modelling. A robust facility location model aims to locate a facility that stays within predefined limits for all expectable circumstances as good as possible. The deviation robustness concept is used as basis to develop a new competitive deviation robustness model. The competition is modelled with a Huff based model, which calculates the market share of the new facility. Robustness in this model is defined as the ability of a facility location to capture a minimum market share, despite variations in demand. A test case is developed by which algorithms can be tested on their ability to solve robust facility location models. Four stochastic optimization algorithms are considered from which Simulated Annealing turned out to be the most appropriate. The test case is slightly modified for a competitive market situation. With the Simulated Annealing algorithm, the developed competitive deviation model is solved, for three considered norms of deviation. At the end, also a grid search is performed to illustrate the landscape of the objective function of the competitive deviation model. The model appears to be multimodal and seems to be challenging for further research.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Wireless Sensor Network Deployment

Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work.

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

Optimal pose selection for the calibration of an overconstrained Cable-Driven Parallel Robot

In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.

Optimización en el cómputos de algoritmos de cuadratura para funciones especiales. Optimization of quadrature algorithms for special functions.

En nuestro proyecto anterior aproximamos el cálculo de una integral definida con integrandos de grandes variaciones funcionales. Nuestra aproximación paraleliza el algoritmo de cómputo de un método adaptivo de cuadratura, basado en reglas de Newton-Cote. Los primeros resultados obtenidos fueron comunicados en distintos congresos nacionales e internacionales; ellos nos permintieron comenzar con una tipificación de las reglas de cuadratura existentes y una clasificación de algunas funciones utilizadas como funciones de prueba. Estas tareas de clasificación y tipificación no las hemos finalizado, por lo que pretendemos darle continuidad a fin de poder informar sobre la conveniencia o no de utilizar nuestra técnica. Para llevar adelante esta tarea se buscará una base de funciones de prueba y se ampliará el espectro de reglas de cuadraturas a utilizar. Además, nos proponemos re-estructurar el cálculo de algunas rutinas que intervienen en el cómputo de la mínima energía de una molécula. Este programa ya existe en su versión secuencial y está modelizado utilizando la aproximación LCAO. El mismo obtiene resultados exitosos en cuanto a precisión, comparado con otras publicaciones internacionales similares, pero requiere de un tiempo de cálculo significativamente alto. Nuestra propuesta es paralelizar el algoritmo mencionado abordándolo al menos en dos niveles: 1- decidir si conviene distribuir el cálculo de una integral entre varios procesadores o si será mejor distribuir distintas integrales entre diferentes procesadores. Debemos recordar que en los entornos de arquitecturas paralelas basadas en redes (típicamente redes de área local, LAN) el tiempo que ocupa el envío de mensajes entre los procesadores es muy significativo medido en cantidad de operaciones de cálculo que un procesador puede completar. 2- de ser necesario, paralelizar el cálculo de integrales dobles y/o triples. Para el desarrollo de nuestra propuesta se desarrollarán heurísticas para verificar y construir modelos en los casos mencionados tendientes a mejorar las rutinas de cálculo ya conocidas. A la vez que se testearán los algoritmos con casos de prueba. La metodología a utilizar es la habitual en Cálculo Numérico. Con cada propuesta se requiere: a) Implementar un algoritmo de cálculo tratando de lograr versiones superadoras de las ya existentes. b) Realizar los ejercicios de comparación con las rutinas existentes para confirmar o desechar una mejor perfomance numérica. c) Realizar estudios teóricos de error vinculados al método y a la implementación. Se conformó un equipo interdisciplinario integrado por investigadores tanto de Ciencias de la Computación como de Matemática. Metas a alcanzar Se espera obtener una caracterización de las reglas de cuadratura según su efectividad, con funciones de comportamiento oscilatorio y con decaimiento exponencial, y desarrollar implementaciones computacionales adecuadas, optimizadas y basadas en arquitecturas paralelas.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.