A Clusterização de dados em grupos oferece uma maneira de entender e extrair informações relevantes de grandes conjuntos de dados. A abordagem em relação a aspectos como a representação dos dados e medida de similaridade entre clusters, e a necessidade de ajuste de parâmetros iniciais são as principais diferenças entre os algoritmos de clusterização, influenciando na qualidade da divisão dos clusters. O uso cada vez mais comum de grandes conjuntos de dados aliado à possibilidade de melhoria das técnicas já existentes tornam a clusterização de dados uma área de pesquisa que permite inovações em diferentes campos. Nesse trabalho é feita uma revisão dos métodos de clusterização já existentes, e é descrito um novo método de clusterização de dados baseado na identificação de comunidades em redes complexas e modelos computacionais inspirados biologicamente. A técnica de clusterização proposta é composta por duas etapas: formação da rede usando os dados de entrada; e particionamento dessa rede para obtenção dos clusters. Nessa última etapa, a técnica de otimização por nuvens de partículas é utilizada a fim de identificar os clusters na rede, resultando em um algoritmo de clusterização hierárquico divisivo. Resultados experimentais revelaram como características do método proposto a capacidade de detecção de clusters de formas arbitrárias e a representação de clusters com diferentes níveis de refinamento. / DAta clustering is an important technique to understand and to extract relevant information in large datasets. Data representation and similarity measure adopted, and the need to adjust initial parameters, are the main differences among clustering algorithms, interfering on clusters quality. The crescent use of large datasets and the possibility to improve existing techniques make data clustering a research area that allows innovation in different fields. In this work is made a review of existing data clustering methods, and it is proposed a new data clustering technique based on community dectection on complex networks and bioinspired models. The proposed technique is composed by two steps: network formation to represent input data; and network partitioning to identify clusters. In the last step, particle swarm optimization technique is used to detect clusters, resulting in an hierarchical clustering algorithm. Experimental results reveal two main features of the algorithm: the ability to detect clusters in arbitrary shapes and the ability to generate clusters with different refinement degrees

Continuum manipulators which are robot limbs inspired by trunks, snakes, and tentacles, represent a promising field in robotic manipulation research. They are well known for their compliance, as they can conform to the shape of objects they interact with. Furthermore, they also benefit from improved dexterity and reduced weight compared to traditional rigid manipulators. The current state of the art continuum robots typically consists of a bulky pneumatic or tendon-driven actuation system at the base, hindering their scalability. Additionally, they tend to sag due to their own weight and are weak in the torsional direction, limiting their performance under external load. This work presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, light-weight, and is inherently safe for human-robot interaction. This dissertation includes contributions in the design of the modular and torsionally strong continuum robot, the motion planning and control of the system, and finally the embedded sensing to close the loop providing robust feedback.

Construction is a complex activity that requires the cooperation of multiple workers. Every year, construction activities cause injuries and casualties. To make construction safer, new solutions could be provided by robotics. Robots could be employed not only to replace human workers, but also to make construction in harsh environments safe and cost-effective, paving the way for enhanced underwater infrastructure, deeper underground mining, and planetary colonization. In this thesis, we focus on the topic of collective construction, which involves the cooperation of multiple robots, by presenting a collective robot construction method of our own. Collective construction can be a more viable option than employing individual, complex robots, by potentially allowing the effective realization of large structures, while offering resilience through redundancy, analogous to insect colonies. Our approach offers a novel solution in the design trade-off between choosing the number of robots involved vs. the complexity of the robots involved. On the one hand, capable and complex robots are expensive, limiting the cost effectiveness of realizing large swarms which provide redundancy and increase the system’s resilience to faults. On the other hand, simple and inexpensive robots can be manufactured in large numbers and offer high redundancy, at the cost of limited individual capa bilities and lower performance. We use two types of robots: intelligent scaffolding and worker robots. The intelligent scaffolding acts as regular scaffolding, allowing the worker robots to navigate the structure they assemble, while also guiding and monitoring the construction of the structure. The worker robots move and connect scaffolding and building material while only knowing the local commands necessary to complete their task. This approach is loosely inspired by termite mounds, in which termites use the process of stigmergy in which they mark construction pellets with pheromones to affect the progress of construction, while navigating the struc ture that they build. Thanks to intelligent scaffolding, construction robots have a simple design that allows minimalist onboard computation and communication equipment. In this thesis, we produced a minimum viable prototype demonstrating this concept. Intelligent scaffolding is realized through smart blocks that can be laid and connected to each other. The smart blocks are capable of simple computation and communication once laid. The construction robot uses local navigation methods by line-following across the scaffolding and building blocks of the system. The blocks and construction robot both have a modular design, simplifying the process of manufacturing and repairs while maintaining a low cost. The robot and blocks use magnets to increase the margin of error during block manipulation and allow for the assembly and removal of scaffolding as well as its reuse between build sites. To communicate with the robot, the intelligent scaffolding blocks send local IR signals, similar to TV remote signals, when the robot is on top of them, minimizing the risk of global interference and keeping the system portable. To monitor the connectivity of the system throughout the life cycle of the structure, electrical connections run through each of the blocks, which indicate the status of the structure and can be used to diagnose the location of breaks in the structure for maintenance.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23505号 / 工博第4917号 / 新制||工||1768(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 泉田 啓, 教授 小森 雅晴 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Les jonctions tunnel magnétiques sont des candidats prometteurs for le calcul. Mais quand elles sont réduites à des dimensions nanométriques, conserver leur stabilité devient difficile. Les jonctions tunnel magnétiques instables subissent des renversements aléatoires de leur aimantation et se comportent comme des oscillateurs stochastiques. Pourtant, la nature stochastique de ces jonctions tunnel superparamagnétiques n’est pas une faille mais un atout qui peut être utilisé pour le calcul bio-inspiré. En effet, notre cerveau a évolué de sorte qu’il puisse fonctionner dans un environnement bruité et avec des composants instables. Dans cette thèse, nous montrons plusieurs applications possibles des jonctions tunnel superparamagnétiques.Nous démontrons qu’une junction tunnel superparamagnétique peut être synchronisée en fréquence et en phase à une faible tension oscillante. De manière contre intuitive, notre expérience montre que cela peut être fait grâce à l’injection de bruit dans le système. Nous développons un modèle théorique pour comprendre ce phénomène et prédire qu’il permet un gain énergétique d’un facteur cent par rapport à la synchronisation d’oscillateurs à transfert de spin traditionnels. De plus, nous utilisons notre modèle pour étudier la synchronisation de plusieurs jonctions couplées. De nombreuses méthodes théoriques réalisant des tâches cognitives telles que la reconnaissance de motifs et la classification grâce à la synchronisation d’oscillateurs ont été proposés. Utiliser la synchronisation induite par le bruit de jonctions tunnel superparamagnétiques permettrait de réaliser ces tâches à basse énergie.Nous faisons une analogie entre les jonctions tunnel superparamagnétiques et les neurones sensoriels qui émettent des pics de tension séparés par des intervalles aléatoires. En poursuivant cette analogie, nous démontrons que des populations de jonctions tunnel superparamagnétiques peuvent représenter des distributions de probabilité et réaliser de l’inférence Bayésienne. De plus, nous démontrons que des populations interconnectées peuvent faire du calcul, notamment de l’apprentissage, des transformations de coordonnées et de la fusion sensorielles. Un tel système est faisable de manière réaliste et pourrait permettre de fabriquer des capteurs intelligents à bas coût énergétique. / Magnetic tunnel junctions are promising candidates for computing applications. But when they are reduced to nanoscale dimensions, maintaining their stability becomes an issue. Unstable magnetic tunnel junctions undergo random switches of the magnetization between their two stable states and thus behave as stochastic oscillators. However, the stochastic nature of these superparamagnetic tunnel junctions is not a liability but an asset which can be used for the implementation of bio-inspired computing schemes. Indeed, our brain has evolved to function in a noisy environment and with unstable components. In this thesis, we show several possible applications of superparamagnetic tunnel junctions.We demonstrate how a superparamagnetic tunnel junction can be frequency and phase-locked to a weak oscillating voltage. Counterintuitively, our experiment shows that this is achieved by injecting noise in the system. We develop a theoretical model to understand this phenomenon and predict that it allows a hundred-fold energy gain over the synchronization of traditional dc-driven spin torque oscillators. Furthermore, we leverage our model to study the synchronization of several coupled junctions. Many theoretical schemes using the synchronization of oscillators to perform cognitive tasks such as pattern recognition and classification have been proposed. Using the noise-induced synchronization of superparamagnetic tunnel junctions would allow implementing these tasks at low energy.We draw an analogy between superparamagnetic tunnel junctions and sensory neurons which fire voltage pulses with random time intervals. Pushing this analogy, we demonstrate that populations of junctions can represent probability distributions and perform Bayesian inference. Furthermore, we demonstrate that interconnected populations can perform computing tasks such as learning, coordinate transformations and sensory fusion. Such a system is realistically implementable and could allow for intelligent sensory processing at low energy cost.

Dans cette thèse nous nous intéressons à la problématique de la construction de l'esthétiquechez les humains. Nous proposons d'utiliser un robot comme modèle pour étudier les briquesde bases qui participent au développement des préférences esthétiques. Nous utilisons le termed'esthétique artificielle (E.A ) pour désigner les préférences du robot.Plusieurs travaux de recherche tentent d'établir des théories de l'esthétique que nous séparons icien deux approches. D'une part, les approches empiriques qui étudientles préférences esthétiques d'un point de vue expérimental. Nous nous intéressons notamment àune branche plus radicale des approches empiriques, nommée la neuroesthétique. Celle-ci postulel'existence de structures cérébrales dédiées à l'appréciation des scènes visuelles en général et de l'art en particulier.D'autre part, les approches sociales qui avancent que les préférences esthétiques se transmettent de générationen génération et se construisent selon l'historique de l'individu et de ses interactions avec les autres.Le contextualisme historique est une branchedes approches sociales qui établit un lien entre le contexte dans lequel une œuvre est observée et son appréciation.Sans remettre en cause l'approche neuroscientifique, nous avons choisi de nous positionner dans une approche sociale et développementaleen utilisant des méthodes expérimentales telles que celles utilisées en esthétique empirique.Nous étudions l'émergence du sens esthétique dans le cadre de la référenciation sociale.On appelle référenciation sociale la capacité à attribuer des valences émotionnelles à des objets a priori neutre.Nous testons nos hypothèses sur robot mobile dans un cadre d'interaction triadique : homme-robot objet.Ceci dans un milieu naturel centré sur des humains non initiés à la robotique.Les humains jouent le rôle d'enseignants (professeur) du robot. Ils ont la tâche de suivre le robot dans son développementet de lui enseigner leurs préférences pour lui permettre de développer son propre "goût".Nous avons choisi de mener nos expériences dans des milieux dominés par l'esthétique comme les musées ou les galeries d'art.Toutefois, ces expériences peuvent être menées en tout lieu où des humains et des objets seraient disponibles.Notre robot, nommé Berenson en référence à un célèbre historien de l'art du 19ème siècle, est un outilpour comprendre d'une part comment s'installent des interactions sociales et comment les humainsprêtent des intentions aux machines, et d'autres part il permet d'étudier les briques minimalesd'intelligence artificielle à mettre en place pour construire une esthétique artificielle. / In this thesis we propose a robot as tool to study minimal bricks that helps human develop their aesthetic preferences. We refer to the robot preference using the term Artificial Esthetics (A.E).Several research work tries to establish a unified theory of esthetics. We divide them into two approaches. In one side, the empirical approaches which study esthetic preferences in an experimental manner. We mainly discuss the more radical branch of those approaches named "Neuroesthetic". Neuroesthetic advocates the existence of neural structures dedicated to visual scene preference and particularly to art appreciation. In another side, the social approaches which advocate that esthetic preferences are transmitted generation after generation, and they are built according to the individual historic and his interaction with others. Historical contextualism is a branch of the social approaches of art that draws a link between the appreciation of an artwork and the context where the artwork is observed.Without rejecting the neuroscientific approach, we choose a social and developmental way to study artificial esthetic using experimental methods from the empirical esthetic. We study the esthetic preferences development in the social referencing framework. Social referencing is the ability to attribute emotional values to à priori neutral objects. We test our hypothesis on a mobile robot in a triadic interaction : human-robot-object. This in a natural human centered environment. Humans play the role of the teachers. They have to fololow the robot in his development and teach it their preferences in order to help it develop its own "taste".We chose to conduct our experiment in places dominated by art and esthetics like museums and art galleries, however, this kind of experiment can take place anyway where human and objects are present.We named our robot Berenson in reference to a famous art historian of the 19th century. Berenson is a tool to understand how human project intentions into machines in one hand, and in the other hand the robot helps scientist build and understand minimal artificial intelligence bricks to build an artificial esthetic.

One of the approaches to building artificial intelligence (AI) is to decipher the princi- ples of the brain function and to employ similar mechanisms for solving cognitive tasks, such as visual perception or natural language understanding, using machines. The recent breakthrough, named deep learning, demonstrated that large multi-layer networks of arti- ficial neural-like computing units attain remarkable performance on some of these tasks. Nevertheless, such artificial networks remain to be very loosely inspired by the brain, which rich structures and mechanisms may further suggest new algorithms or even new paradigms of computation. In this thesis, we explore brain-inspired probabilistic mechanisms, such as neural and synaptic stochasticity, in the context of generative models. The two questions we ask here are: (i) what kind of models can describe a neural learning system built of stochastic components? and (ii) how can we implement such systems e ̆ciently? To give specific answers, we consider two well known models and the corresponding neural architectures: the Naive Bayes model implemented with a winner-take-all spiking neural network and the Boltzmann machine implemented in a spiking or non-spiking fashion. We propose and analyze an e ̆cient neuromorphic implementation of the stochastic neu- ral firing mechanism and study the e ̄ects of synaptic unreliability on learning generative energy-based models implemented with neural networks.

Continuum manipulators which are robot limbs inspired by trunks, snakes, and tentacles, represent a promising field in robotic manipulation research. They are well known for their compliance, as they can conform to the shape of objects they interact with. Furthermore, they also benefit from improved dexterity and reduced weight compared to traditional rigid manipulators. The current state of the art continuum robots typically consists of a bulky pneumatic or tendon-driven actuation system at the base, hindering their scalability. Additionally, they tend to sag due to their own weight and are weak in the torsional direction, limiting their performance under external load. This work presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, light-weight, and is inherently safe for human-robot interaction. This dissertation includes contributions in the design of the modular and torsionally strong continuum robot, the motion planning and control of the system, and finally the embedded sensing to close the loop providing robust feedback.

Origami has been extensively studied by engineers for its unique motions and ability to collapse to small volumes. Techniques have been studied for replicating origami-like folding motion in thick materials, but limited practical applications of these techniques have been demonstrated. Developable mechanisms are a new mechanism type that has a similar ability to collapse to a low profile. The cylindrical developable mechanism has the ability to emerge from and conform to a cylindrical surface. In this work, a few practical applications of devices with novel expanding motions are presented. The design and testing of an origami-inspired deployable ballistic barrier, which was designed by combining and modifying existing thickness accommodation techniques, is discussed. The properties of cylindrical developable mechanisms are examined and two devices designed for use with minimally invasive surgical tooling are presented. Various hinge options for small-scale cylindrical developable mechanisms are then reviewed and discussed. A planar modeling assumption for curved lamina emergent torsional joints in thin-walled cylinders is then analytically and empirically validated. Conclusions are drawn and recommendations for future work are given.

The foreign exchange (Forex) market has over 5 trillion USD turnover per day. In addition, it is one of the most volatile and dynamic markets in the world. Market conditions continue to change every second. Algorithmic trading in Financial markets have received a lot of attention in recent years. However, only few literature have explored the applicability and performance of various dynamic multi-objective algorithms (DMOAs) in the Forex market. This dissertation proposes a dynamic multi-swarm multi-objective particle swarm optimization (DMS-MOPSO) to solve dynamic MOPs (DMOPs). In order to explore the performance and applicability of DMS-MOPSO, the algorithm is adapted for the Forex market. This dissertation also explores the performance of di erent variants of dynamic particle swarm optimization (PSO), namely the charge PSO (cPSO) and quantum PSO (qPSO), for the Forex market. However, since the Forex market is not only dynamic but have di erent con icting objectives, a single-objective optimization algorithm (SOA) might not yield pro t over time. For this reason, the Forex market was de ned as a multi-objective optimization problem (MOP). Moreover, maximizing pro t in a nancial time series, like Forex, with computational intelligence (CI) techniques is very challenging. It is even more challenging to make a decision from the solutions of a MOP, like automated Forex trading. This dissertation also explores the e ects of ve decision models (DMs) on DMS-MOPSO and other three state-of-the-art DMOAs, namely the dynamic vector-evaluated particle swarm optimization (DVEPSO) algorithm, the multi-objective particle swarm optimization algorithm with crowded distance (MOPSOCD) and dynamic non-dominated sorting genetic algorithm II (DNSGA-II). The e ects of constraints handling and the, knowledge sharing approach amongst sub-swarms were explored for DMS-MOPSO. DMS-MOPSO is compared against other state-of-the-art multi-objective algorithms (MOAs) and dynamic SOAs. A sliding window mechanism is employed over di erent types of currency pairs. The focus of this dissertation is to optimized technical indicators to maximized the pro t and minimize the transaction cost. The obtained results showed that both dynamic single-objective optimization (SOO) algorithms and dynamic multi-objective optimization (MOO) algorithms performed better than static algorithms on dynamic poroblems. Moreover, the results also showed that a multi-swarm approach for MOO can solve dynamic MOPs. / Dissertation (MEng)--University of Pretoria, 2019. / Computer Science / MSc / Unrestricted