The AOE Accumulated Ocean Energy Inc. (AOE) wave energy converter (WEC) is a cascaded pneumatic system, in which air is successively compressed through three point absorber devices on the way to shore; this air is then used to drive an electricity generator. To better quantify the performance of this device, this thesis presents a dynamically coupled model architecture of the AOE WEC, which was developed using the finite element solver ProteusDS and MATLAB/Simulink. This model is subsequently applied for the development and implementation of control in the AOE WEC. At each control stage, comprehensive power matrix data is generated to assess power production as a function of control complexity. The nature of the AOE WEC presented a series of novel challenges, centered on the significant residency time of air within the power take-off (PTO). As a result, control implementation was broken into two stages: passive and active control. The first stage, passive control, was realized as an optimization of eight critical PTO parameters with the objective of maximizing exergy output. After only 15 generations, the genetic algorithm optimization led to an increase of 330.4% over an initial, informed estimate of the optimal design, such that the annually-averaged power output was 29.37 kW. However, a disparity in power production between low and moderate energy sea-states was identified, which informed the development of an active control strategy for the increase of power production in low energy sea-states. To this aim, a recirculation-based control strategy was developed, in which three accumulator tanks were used to selectively pressurize and de-pressurize the piston at opportune times, thereby increasing the continuity of air throughput. Under the influence of active control, sea-states with significant wave heights between 0.75 m – 1.75 m, which on average encompass 55.93% of the year at the Amphitrite Bank deployment location, saw a 16.3% increase in energy production. / Graduate / 2018-08-18

Cette thèse est consacrée à la conception et à la mise en œuvre d’un réseau d’antenne multifonction.  Basé sur le concept du Time Modulated Array (TMA), réseau modulé dans le temps et grâce à des switches cette étude montre la possibilité de réaliser un réseau multifonctions. Deux fonctions ont été étudiés, une fonction radar (fonction principale) et une fonction communication (fonction secondaire). Une des innovations apportées par ce principe est la bidirectionnalité (chaque fonction est réalisée dans une direction différente) et l’aspect simultané des fonctions exécutées. . La technique conventionnelle du TMA présente aussi des inconvénients pour être utiliser dans des applications radar.  En effet, les variations de directivité, l'angle d'ouverture ainsi qu’une grande sensibilité aux interférences font que le TMA n’est pas compatible avec des applications radar. En effet, une variation de directivité provoque une variation de puissances à l'émission donc les signaux réfléchis souffriront également de cette variation qui peut ainsi créer des erreurs de détections. Des variations de l'angle d'ouverture crée une variation de la résolution angulaire du radar dans le temps ce qui perturbe la capacité de discrimination du radar. De plus, le rejet des interférences est aussi nécessaire afin d'éviter d'être aveuglé par un brouilleur ou par les échos parasites pendant notre détection. Pour résoudre ces inconvénients une méthode spécifique appelée Adapted Radar TMA a ainsi été développée. Grâce à une méthode d'optimisation (algorithme génétique) avec des contraintes définis, avec comme variables principale la loi d’excitation des antennes, plusieurs compromis ont été proposés afin de mutualiser et maximiser les performances de chaque partie (radar et communication). Ainsi 3 méthodes de loi d'excitation des antennes (ou pondération) ont été pensés. Par le biais de ces méthodes, la directivité et l'angle d'ouverture ont été contrôlés. Le rejet des interférences est désormais possible dans une direction donné. De plus, le réseau multifonction est aussi capable de fournir une partie communication ajouté à la partie radar déjà existante. L'optimisation exploite le comportement instantané d'ARTMA. Ainsi, en utilisant la variation des lobes secondaires dus aux changements des poids dans le temps, plusieurs modulations peuvent être adressées, à savoir une modulation ASK ou QAM. Un prototype de ce réseau multifonction comportant 16 antennes a été conçu. Les résultats des mesures ont fourni de bons résultats et ont validé le concept d'une communication en utilisant une modulation d'amplitude et de phase en faisant varier les lobes secondaires dans le temps grâce à des switchs en amont des antennes. / This thesis is devoted to the design and implementation of a multifunctional antenna array. Based on the concept of Time Modulated Array (TMA), array modulated in time with switches this study shows the possibility of a multifunction array. Two functions were studied, a radar function (main function) and a communication function (secondary function). One of the innovations of this principle is the bidirectional (each function is performed in a different direction) and the simultaneous appearance of the functions performed. The conventional technique of TMA also has drawbacks to be used in radar applications. Indeed, variations of directivity, beamwidth and a sensitivity to interference make the TMA no compatible with radar applications. Indeed, a directivity variation causes variations in the power transmission therefore the reflected signals also suffer from these variations, which can thus create errors detections. Variations in the beamwidth creates a change in the angular resolution of the radar in time thereby interfering with the discrimination ability of the radar. In addition, the interference rejection is also needed to avoid being blinded by a jammer or clutter during our detection. To overcome these drawbacks a specific method called Adapted Radar TMA has been developed. Through an optimization method (genetic algorithm) with defined constraints using as main variable the excitation law of the antennas, several compromises were proposed in order to make matched and maximize the performance of each part (radar and communication). Thus methods 3 excitation law of the antennas (weighting coefficients) were thought. Through these methods, the directivity and the beamwidth have been controlled. The interference rejection is now possible in a given direction. In addition, the multifunction array is also capable of providing a communication part added to the existing part radar. Optimization operates with ARTMA instant behavior. Thus, using the variation of the sidelobes due to changes in weighting coefficients over the time, several modulations may be addressed, namely ASK or QAM. A prototype of this multifunction network with 16 antennas was designed. Measurement results have provided good results and have validated the concept of communication using an amplitude and phase modulation by varying the side lobes in time through the switches before of the antennas.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / David H. Ben-Arieh / In present competitive market, there is growing concern for ascertaining and fulfilling the individual customer’s wants and needs. Therefore, the focus of manufacturing has been shifting from mass production to mass customization, which requires the manufacturers to introduce an increasing number of products with shorter life span and at a lower cost. Also, another challenge is to manage the variety of products in an environment where demands are stochastic and the lead times to fulfill those demands are short. The focus of this thesis is to develop and investigate platform based production strategies, as opposed to producing each product independently, which would ensure the economic production of the broader specialized products with small final assembly time and under demand uncertainty. The thesis proposes three different platform based production models. The first model considers the economic production of products based on a single platform and with forecasted demands of the products. The model is formulated as a general optimization problem that considers the minimization of total production costs. The second model is the extension of the first model and considers the production of products based on multiple platforms and considers the minimization of total production costs and the setup costs of having multiple platforms. The third model is also an extension of the first model and considers the demands of the products to be stochastic in nature. The model considers the minimization of total production costs and shortage costs of lost demands and holding cost of surplus platforms under demand uncertainties. The problem is modeled as a two stage stochastic programming with recourse. As only the small instances of the models could be solved exactly in a reasonable time, various heuristics are developed by combining the genetic evolutionary search approaches and some operations research techniques to solve the realistic size problems. The various production models are validated and the performances of the various heuristics tailored for the applications are investigated by applying these solution approaches on a case of cordless drills.

In this work, the potential of intelligent algorithms for optimizing the real power loss and enhancing the grid connection power factor in a real hydrocarbon facility electrical system is assessed. Namely, genetic algorithm (GA), improve strength Pareto evolutionary algorithm (SPEA2) and differential evolutionary algorithm (DEA) are developed and implemented. The economic impact associated with these objectives optimization is highlighted. The optimization of the subject objectives is addressed as single and multi-objective constrained nonlinear problems. Different generation modes and system injected reactive power cases are evaluated. The studied electrical system constraints and parameters are all real values. The uniqueness of this thesis is that none of the previous literature studies addressed the technical and economic impacts of optimizing the aforementioned objectives for real hydrocarbon facility electrical system. All the economic analyses in this thesis are performed based on real subsidized cost of energy for the kingdom of Saudi Arabia. The obtained results demonstrate the high potential of optimizing the studied system objectives and enhancing the economics of the utilized generation fuel via the application of intelligent algorithms.

NIALM (Non-Intrusive Load Monitoring) est une technologie innovante qui permet de suivre la consommation individuelle en énergie des différents appareils électriques dans un réseau électrique grâce à un seul point de mesure. Ainsi, l’installation et la maintenance du système est très simple. Cependant, le logiciel NIALM nécessite le développement d’algorithmes sophistiqués pour identifier la consommation de chaque appareil avec une bonne précision. Par conséquent, ces algorithmes complexes nécessitent une plate-forme d’exécution puissante et coûteuse. En réponse à ce problème, cette thèse propose un système NIALM innovant fonctionnant en temps réel et à faible coût. Ce système permet de dépasser certaines limites actuelles du NIALM grâce à une extraction d’informations supplémentaires sur les signatures électriques, une détection des transitions lentes et des appareils à multi-états grâce à deux nouvelles fonctions : un algorithme de détection d'événements CUSUM et une ventilation des sommes cumulées en se basant sur un algorithme génétique. La deuxième contribution importante est de proposer une méthodologie utilisant le modèle RPN (Reactive Process Network) pour développer le système NIALM dans un SoC (System on Chip) avec une accélération matérielle de type FPGA. Ce SoC permet ainsi l'exécution en parallèle dans le FPGA de processus de traitement de données avec des algorithmes complexes tout en satisfaisant les contraintes de temps. Les avantages de notre méthode sont : la capacité de développer une spécification exécutable, d’effectuer une exploration d'architecture, et d’obtenir rapidement un prototype du système NIALM à partir d’un même modèle applicatif. / In comparison to conventional smart meters, NIALM (Non-Intrusive Load Monitoring) is an innovative technology because it can monitor power usage on individual appliances in an electrical network using only one sensing node. Thus, setting up and maintaining the system is very simple because of the few of hardware elements. In contrast, the software of NIALM is often very complex and there is still the need in developing more complex algorithms to classify appliances more accurately. These complex algorithms of NIALM require to run on a powerful and expensive hardware platform. In order to overcome this problem, the first contribution of this thesis is to propose a low cost real-time innovative NIALM system to solve some limits of NIALM design by extracting more electrical signatures, detecting slow transition and multi-state appliances, and energy disaggregation in real-time. This is possible by using two new algorithms: CUSUM event detection algorithm and disaggregation based on Genetic Algorithm. Similar to complex DSP systems, a NIALM system contains both event control processes and data streaming processes. The second important contribution of this research is to propose a methodology based on RPN model (Reactive Process Network) to develop a complex NIALM system in SoC with FPGA acceleration. Such SoC allows running data streaming processes with complex algorithms and hard timing constraints in parallel in FPGA while other processes can run in processors. The advantages of our methodology are the ability to develop an executable specification to proceed to architecture exploration, and prototype the NIALM system quickly using the same application model.

No description available.

Non-rigid shapes are generally known as objects whose three dimensional geometry may deform by internal and/or external forces. Deformable shapes are all around us, ranging from protein molecules, to natural objects such as the trees in the forest or the fruits in our gardens, and even human bodies. Two deformable shapes may be related by isometry, which means their intrinsic geometries are preserved, even though their extrinsic geometries are dissimilar. An important problem in the analysis of the deformable shapes is to identify the three-dimensional correspondence between two isometric shapes, given that the two shapes may be deviated from isometry by intrinsic distortions. A major challenge is that non-rigid shapes have large degrees of freedom on how to deform. Nevertheless, irrespective of how they are deformed, they may be aligned such that the geodesic distance between two arbitrary points on two shapes are nearly equal. Such alignment may be expressed by a permutation matrix (a matrix with binary entries) that corresponds to every paired geodesic distance in between the two shapes. The alignment involves searching the space over all possible mappings (that is all the permutations) to locate the one that minimizes the amount of deviation from isometry. A brute-force search to locate the correspondence is not computationally feasible. This thesis introduces a novel approach created to locate such correspondences, in spite of the large solution space that encompasses all possible mappings and the presence of intrinsic distortion. In order to find correspondences between two shapes, the first step is to create a suitable descriptor to accurately describe the deformable shapes. To this end, we developed deformation-invariant metric descriptors. A descriptor constitutes pair-wise geodesic distances among arbitrary number of discrete points that represent the topology of the non-rigid shape. Our descriptor provides isometric-invariant representation of the shape irrespective of its circumstantial deformation. Two isometric-invariant descriptors, representing two candidate deformable shapes, are the input parameters to our optimization algorithm. We then proceed to locate the permutation matrix that aligns the two descriptors, that minimizes the deviation from isometry. Once we have developed such a descriptor, we turn our attention to finding correspondences between non deformable shapes. In this study, we investigate the use of both classical and quantum particle swarm optimization (PSO) algorithms for this task. To explore the merits of variants of PSO, integer optimization involving test functions with large dimensions were performed, and the results and the analysis suggest that quantum PSO is more effective optimization method than its classical PSO counterpart. Further, a scheme is proposed to structure the solution space, composed of permutation matrices, in lexicographic ordering. The search in the solution space is accordingly simplified to integer optimization to find the integer rank of the targeted permutation matrix. Empirical results suggest that this scheme improves the scalability of quantum PSO across large solution spaces. Yet, quantum PSO's global search capability requires assistance in order to more effectively manoeuvre through the local extrema prevalent in the large solution spaces. A mutation based genetic algorithm (GA) is employed to augment the search diversity of quantum PSO when/if the swarm stagnates among the local extrema. The mutation based GA instantly disengages the optimization engine from the local extrema in order to reorient the optimization energy to the trajectories that steer to the global extrema, or the targeted permutation matrix. Our resultant optimization algorithm combines quantum Particle Swarm Optimization (PSO) and mutation based Genetic Algorithm (GA). Empirical results show that the optimization method presented is scalable and efficient on standard hardware across different solution space sizes. The performance of the optimization method, in simulations and on various near-isometric shapes, is discussed. In all cases investigated, the method could successfully identify the correspondence among the non-rigid deformable shapes that were related by isometry.

In this thesis a Proactive Risk-Aware Robotic Sensor Network (RSN) is proposed for the application of Critical Infrastructure Protection (CIP). Each robotic member of the RSN is granted a perception of risk by means of a Risk Management Framework (RMF). A fuzzy-risk model is used to extract distress-based risk features and potential intrusion-based risk features for CIP. Detected high-risk events invoke a fuzzy-auction Multi-Robot Task Allocation (MRTA) algorithm to create a response group for each detected risk. Through Evolutionary Multi-Objective (EMO) optimization, a Pareto set of optimal robot configurations for a response group will be generated using the Non-Dominating Sorting Genetic Algorithm II (NSGA-II). The optimization objectives are to maximize sensor coverage of essential spatial regions and minimize the amount of energy exerted by the response group. A set of non-dominated solutions are produced from EMO optimization for a decision maker to select a single response. The RSN response group will re-organize based on the specifications of the selected response.

The objective of this thesis is to apply alternative multi-objective optimization techniques to the portfolio selection problem. Theoretical part starts with detailed analysis of the classical Markowitz model and its assumptions. Following that, introduction of multi-criterion optimization techniques available for finding non-dominated portfolios is given. One of these techniques, the genetic algorithm, is presented in great detail. Some of the basic methods useful for predicting stock prices and its risks are presented at the end of the theoretical part. Practical part presents an application of the theory to the problem of constructing efficient portfolios of 11 selected stocks traded on Prague Stock Exchange. Results achieved by different approaches are compared and interpreted.

To mitigate high voltages in transmission systems with low demands, traditional solutions often consider the installation of reactive power compensators. The deployment and tuning of numbers of VAr compensators at various locations may not be cost-effective. This thesis presents an alternative method that utilises existing parallel transformers in distribution networks to provide reactive power supports for transmission systems under low demands. The operation of parallel transformers in small different tap positions, i.e. with staggered taps, can provide a means of absorbing reactive power. The aggregated reactive power absorption from many pairs of parallel transformers could be sufficient to provide voltage support to the upstream transmission network. Network capability studies have been carried out to investigate the reactive power absorption capability through the use of tap stagger. The studies are based on a real UK High Voltage distribution network, and the tap staggering technique has been applied to primary substation transformers. The results confirm that the tap staggering method has the potential to increase the reactive power demand drawn from the transmission grid. This thesis also presents an optimal control method for tap stagger to minimise the introduced network loss as well as the number of tap switching operations involved. A genetic algorithm (GA) based procedure has been developed to solve the optimisation problem. The GA method has been compared with two alternative solution approaches, i.e. the rule-based control scheme and the branch-and-bound algorithm. The results indicate that the GA method is superior to the other two approaches. The economic and technical impacts of the tap staggering technique on the transmission system has been studied. In the economic analysis, the associated costs of applying the tap staggering method have been investigated from the perspective of transmission system operator. The IEEE Reliability Test System has been used to carry out the studies, and the results have been compared with the installation of shunt reactors. In the technical studies, the dynamic impacts of tap staggering or reactor switching on transmission system voltages have been analysed. From the results, the tap staggering technique has more economic advantages than reactors and can reduce voltage damping as well as overshoots during the transient states.