Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Diseño de un observador Backstepping para un robot con articulación rotaria flexible

Tesis (Maestro en Ciencias de la Ingeniería Eléctrica con Especialidad en Control) UANL, 2001.

Estrategia didáctica para el desarrollo de competencias en solución de problemas mediante la programación digital del robot Karel.

Tesis (Maestría en Docencia con Orientación en Educación Media Superior) UANL, 2012.

Optimization and validation of a new 3D-US imaging robot to detect, localize and quantify lower limb arterial stenoses

L’athérosclérose est une maladie qui cause, par l’accumulation de plaques lipidiques, le durcissement de la paroi des artères et le rétrécissement de la lumière. Ces lésions sont généralement localisées sur les segments artériels coronariens, carotidiens, aortiques, rénaux, digestifs et périphériques. En ce qui concerne l’atteinte périphérique, celle des membres inférieurs est particulièrement fréquente. En effet, la sévérité de ces lésions artérielles est souvent évaluée par le degré d’une sténose (réduction >50 % du diamètre de la lumière) en angiographie, imagerie par résonnance magnétique (IRM), tomodensitométrie ou échographie. Cependant, pour planifier une intervention chirurgicale, une représentation géométrique artérielle 3D est notamment préférable. Les méthodes d’imagerie par coupe (IRM et tomodensitométrie) sont très performantes pour générer une imagerie tridimensionnelle de bonne qualité mais leurs utilisations sont dispendieuses et invasives pour les patients. L’échographie 3D peut constituer une avenue très prometteuse en imagerie pour la localisation et la quantification des sténoses. Cette modalité d’imagerie offre des avantages distincts tels la commodité, des coûts peu élevés pour un diagnostic non invasif (sans irradiation ni agent de contraste néphrotoxique) et aussi l’option d’analyse en Doppler pour quantifier le flux sanguin. Étant donné que les robots médicaux ont déjà été utilisés avec succès en chirurgie et en orthopédie, notre équipe a conçu un nouveau système robotique d’échographie 3D pour détecter et quantifier les sténoses des membres inférieurs. Avec cette nouvelle technologie, un radiologue fait l’apprentissage manuel au robot d’un balayage échographique du vaisseau concerné. Par la suite, le robot répète à très haute précision la trajectoire apprise, contrôle simultanément le processus d’acquisition d’images échographiques à un pas d’échantillonnage constant et conserve de façon sécuritaire la force appliquée par la sonde sur la peau du patient. Par conséquent, la reconstruction d’une géométrie artérielle 3D des membres inférieurs à partir de ce système pourrait permettre une localisation et une quantification des sténoses à très grande fiabilité. L’objectif de ce projet de recherche consistait donc à valider et optimiser ce système robotisé d’imagerie échographique 3D. La fiabilité d’une géométrie reconstruite en 3D à partir d’un système référentiel robotique dépend beaucoup de la précision du positionnement et de la procédure de calibration. De ce fait, la précision pour le positionnement du bras robotique fut évaluée à travers son espace de travail avec un fantôme spécialement conçu pour simuler la configuration des artères des membres inférieurs (article 1 - chapitre 3). De plus, un fantôme de fils croisés en forme de Z a été conçu pour assurer une calibration précise du système robotique (article 2 - chapitre 4). Ces méthodes optimales ont été utilisées pour valider le système pour l’application clinique et trouver la transformation qui convertit les coordonnées de l’image échographique 2D dans le référentiel cartésien du bras robotisé. À partir de ces résultats, tout objet balayé par le système robotique peut être caractérisé pour une reconstruction 3D adéquate. Des fantômes vasculaires compatibles avec plusieurs modalités d’imagerie ont été utilisés pour simuler différentes représentations artérielles des membres inférieurs (article 2 - chapitre 4, article 3 - chapitre 5). La validation des géométries reconstruites a été effectuée à l`aide d`analyses comparatives. La précision pour localiser et quantifier les sténoses avec ce système robotisé d’imagerie échographique 3D a aussi été déterminée. Ces évaluations ont été réalisées in vivo pour percevoir le potentiel de l’utilisation d’un tel système en clinique (article 3- chapitre 5).

Imagination et individuation dans la philosophie de Spinoza : La science intuitive comme imagination libérée

La philosophie de Spinoza cherche à concilier et réunir trois horizons philosophiques fondamentaux : l’émanation néo-platonicienne (l’expression), le mécanisme cartésien (cause efficiente), et les catégories aristotéliciennes (Substance, attribut, mode). Ce premier point est pris pour acquis. Nous expliquerons que cette tentative sera rendue possible grâce à la conception nouvelle, au 17e siècle, de l’actualité de l’infini. Nous examinerons ensuite les conséquences de cette nouvelle interprétation, qui permet de rendre l’individu transparent à lui-même sur un plan d’immanence, expressif par rapport à une éminence qui le diffuse, mais déterminé dans une substantialité fictive entre objets finis. En proposant le pouvoir de l’imagination et des prophètes comme point de départ et principe actif du conatus, nous montrerons que la distinction, chez Spinoza, demeure toujours une fiction. Pour conclure, nous serons en mesure de signaler en quoi le Zarathoustra de Nietzsche relève d’une volonté de poursuivre le travail entrepris par Spinoza.

Strategy-proofness makes the difference: deferred-acceptance with responsive priorities

In college admissions and student placements at public schools, the admission decision can be thought of as assigning indivisible objects with capacity constraints to a set of students such that each student receives at most one object and monetary compensations are not allowed. In these important market design problems, the agent-proposing deferred-acceptance (DA-)mechanism with responsive strict priorities performs well and economists have successfully implemented DA-mechanisms or slight variants thereof. We show that almost all real-life mechanisms used in such environments - including the large classes of priority mechanisms and linear programming mechanisms - satisfy a set of simple and intuitive properties. Once we add strategy-proofness to these properties, DA-mechanisms are the only ones surviving. In market design problems that are based on weak priorities (like school choice), generally multiple tie-breaking (MTB)procedures are used and then a mechanism is implemented with the obtained strict priorities. By adding stability with respect to the weak priorities, we establish the first normative foundation for MTB-DA-mechanisms that are used in NYC.

Evaluation of mitochondrial function in a model of developmental programming of hypertension associated with transient neonatal oxygen exposure

UNE EXPOSITION NÉONATALE À L’OXYGÈNE MÈNE À DES MODIFICATIONS DE LA FONCTION MITOCHONDRIALE CHEZ LE RAT ADULTE Introduction: L’exposition à l’oxygène (O2) des ratons nouveau-nés a des conséquences à l’âge adulte dont une hypertension artérielle (HTA), une dysfonction vasculaire, une néphropénie et des indices de stress oxydant. En considérant que les reins sont encore en développement actif lors des premiers jours après la naissance chez les rats, jouent un rôle clé dans le développement de l’hypertension et qu’une dysfonction mitochondriale est associé à une augmentation du stress oxydant, nous postulons que les conditions délétères néonatales peuvent avoir un impact significatif au niveau rénal sur la modulation de l’expression de protéines clés du fonctionnement mitochondrial et une production mitochondriale excessive d’espèces réactives de l’ O2. Méthodes: Des ratons Sprague-Dawley sont exposés à 80% d’O2 (H) ou 21% O2 (Ctrl) du 3e au 10e jr de vie. En considérant que plusieurs organes des rats sont encore en développement actif à la naissance, ces rongeurs sont un modèle reconnu pour étudier les complications d’une hyperoxie néonatale, comme celles liées à une naissance prématurée chez l’homme. À 4 et à 16 semaines, les reins sont prélevés et les mitochondries sont extraites suivant une méthode d’extraction standard, avec un tampon contenant du sucrose 0.32 M et différentes centrifugations. L’expression des protéines mitochondriales a été mesurée par Western blot, tandis que la production d’ H202 et les activités des enzymes clés du cycle de Krebs ont été évaluées par spectrophotométrie. Les résultats sont exprimés par la moyenne ± SD. Résultats: Les rats mâles H de 16 semaines (n=6) présentent une activité de citrate synthase (considéré standard interne de l’expression protéique et de l’abondance mitochondriales) augmentée (12.4 ± 8.4 vs 4.1 ± 0.5 μmole/mL/min), une diminution de l’activité d’aconitase (enzyme sensible au redox mitochondrial) (0.11 ± 0.05 vs 0.20 ± 0.04 μmoles/min/mg mitochondrie), ainsi qu’une augmentation dans la production de H202 (7.0 ± 1.3 vs 5.4 ± 0.8 ρmoles/mg protéines mitochondriales) comparativement au groupe Ctrl (n=6 mâles et 4 femelles). Le groupe H (vs Ctrl) présente également une diminution dans l’expression de peroxiredoxin-3 (Prx3) (H 0.61±0.06 vs. Ctrl 0.78±0.02 unité relative, -23%; p<0.05), une protéine impliquée dans l’élimination d’ H202, de l’expression du cytochrome C oxidase (Complexe IV) (H 1.02±0.04 vs. Ctrl 1.20±0.02 unité relative, -15%; p<0.05), une protéine de la chaine de respiration mitochondriale, tandis que l’expression de la protéine de découplage (uncoupling protein)-2 (UCP2), impliquée dans la dispersion du gradient proton, est significativement augmentée (H 1.05±0.02 vs. Ctrl 0.90±0.03 unité relative, +17%; p<0.05). Les femelles H (n=6) (vs Ctrl, n=6) de 16 semaines démontrent une augmentation significative de l’activité de l’aconitase (0.33±0.03 vs 0.17±0.02 μmoles/min/mg mitochondrie), de l’expression de l’ATP synthase sous unité β (H 0.73±0.02 vs. Ctrl 0.59±0.02 unité relative, +25%; p<0.05) et de l’expression de MnSOD (H 0.89±0.02 vs. Ctrl 0.74±0.03 unité relative, +20%; p<0.05) (superoxide dismutase mitochondriale, important antioxidant), tandis que l’expression de Prx3 est significativement réduite (H 1.1±0.07 vs. Ctrl 0.85±0.01 unité relative, -24%; p<0.05). À 4 semaines, les mâles H (vs Ctrl) présentent une augmentation significative de l’expression de Prx3 (H 0.72±0.03 vs. Ctrl 0.56±0.04 unité relative, +31%; p<0.05) et les femelles présentent une augmentation significative de l’expression d’UCP2 (H 1.22±0.05 vs. Ctrl 1.03±0.04 unité relative, +18%; p<0.05) et de l’expression de MnSOD (H 1.36±0.01 vs. 1.19±0.06 unité relative, +14%; p<0.05). Conclusions: Une exposition néonatale à l’O2 chez le rat adulte mène à des indices de dysfonction mitochondriale dans les reins adultes, associée à une augmentation dans la production d’espèces réactives de l’oxygène, suggérant que ces modifications mitochondriales pourraient jouer un rôle dans l’hypertension artérielle et d’un stress oxydant, et par conséquent, être un facteur possible dans la progression vers des maladies cardiovasculaires. Mots-clés: Mitochondries, Reins, Hypertension, Oxygène, Stress Oxydant, Programmation