Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Genetic structure and connectivity between populations of two common Mediterranean sessile invertebrates

Population genetic and phylogeography of two common mediterranean species were studied in 10 localities located on the coasts of Toscana, Puglia and Calabria. The aim of the study was to verify the extent of genetic breaks, in areas recognized as boundaries between Mediterranean biogeographic sectors. From about 100 sequences obtained from the mitochondrial Cytochrome Oxidase subunit I (COI) gene of Halocynthia papillosa and Hexaplex trunculus genetic diversity, genetic structure at small and large distances and demographic history of both specieswere analyzed. No evidences of genetic breaks were found for the two species in Toscana and Puglia. The genetic structure of H. trunculus evidences the extent of a barrier to gene flow localized in Calabria, which could be represented by the Siculo-Tunisian Strait and the Strait of Messina. The observed patterns showed similar level of gene flow at small distances in both species, although the two species have different larval ecology. These results suggest that other factors, such as currents, local dynamics and seasonal temperatures, influence the connectivity along the Italian peninsula. The geographic distribution of the haplotypes shows that H. papillosacould represent a single genetic pool in expansion, whereas H. trunculus has two distinct genetic pools in expansion. The demographic pattern of the two species suggests that Pleistocene sea level oscillations, in particular of the LGM, may have played a key role in shaping genetic structure of the two species. This knowledge provides basic information, useful for the definition of management plans, or for the design of a network of marine protected areas along the Italian peninsula.

Laser shock peening treatment to control and moderate fatigue crack growth in aircraft structure based on residual stress engineering approach

Laser Shock Peening (LSP) is a surface enhancement treatment which induces a significant layer of beneficial compressive residual stresses of up to several mm underneath the surface of metal components in order to improve the detrimental effects of the crack growth behavior rate in it. The aim of this thesis is to predict the crack growth behavior in metallic specimens with one or more stripes which define the compressive residual stress area induced by the Laser Shock Peening treatment. The process was applied as crack retardation stripes perpendicular to the crack propagation direction with the object of slowing down the crack when approaching the peened stripes. The finite element method has been applied to simulate the redistribution of stresses in a cracked model when it is subjected to a tension load and to a compressive residual stress field, and to evaluate the Stress Intensity Factor (SIF) in this condition. Finally, the Afgrow software is used to predict the crack growth behavior of the component following the Laser Shock Peening treatment and to detect the improvement in the fatigue life comparing it to the baseline specimen. An educational internship at the “Research & Technologies Germany – Hamburg” department of AIRBUS helped to achieve knowledge and experience to write this thesis. The main tasks of the thesis are the following: •To up to date Literature Survey related to “Laser Shock Peening in Metallic Structures” •To validate the FE model developed against experimental measurements at coupon level •To develop design of crack growth slowdown in Centered Cracked Tension specimens based on residual stress engineering approach using laser peened strip transversal to the crack path •To evaluate the Stress Intensity Factor values for Centered Cracked Tension specimens after the Laser Shock Peening treatment via Finite Element Analysis •To predict the crack growth behavior in Centered Cracked Tension specimens using as input the SIF values evaluated with the FE simulations •To validate the results by means of experimental tests

Correlation between structure and electrochemistry of LiMO2 cathode materials (M = Ni, Co)

Technical diversity and various knowledge is required for the understanding of undoubtedly complex system such as a Lithium-ion battery. The peculiarity is to combine different techniques that allow a complete investigation while the battery is working. Nowadays, research on Li-ion batteries (LIBs) is experiencing an exponential growth in the development of new cathode materials. Accordingly, Li-rich and Ni-rich NMCs, which have similar layered structure of LiMO2 oxides, have been recently proposed. Despite the promising performance on them, still a lot of issues have to be resolved and the materials need a more in depth characterisation for further commercial applications. In this study LiMO2 material, in particular M = Co and Ni, will be presented. We have focused on the synthesis of pure LiCoO2 and LiNiO2 at first, followed by the mixed LiNi0.5Co0.5O2. Different ways of synthesis were investigated for LCO but the sol-gel-water method showed the best performances. An accurate and systematic structural characterization followed by the appropriate electrochemical tests were done. Moreover, the in situ techniques (in-situ XRD and in situ OEMS) allowed a deep investigation in the structural change and gas evolution upon the electrochemically driven processes.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.