On the Development of an Automated Design Procedure to Design Optimal Robots

The objective in this work is to build a rapid and automated numerical design method that makes optimal design of robots possible. In this work, two classes of optimal robot design problems were specifically addressed: (1) When the objective is to optimize a pre-designed robot, and (2) when the goal is to design an optimal robot from scratch. In the first case, to reach the optimum design some of the critical dimensions or specific measures to optimize (design parameters) are varied within an established range. Then the stress is calculated as a function of the design parameter(s), the design parameter(s) that optimizes a pre-determined performance index provides the optimum design. In the second case, this work focuses on the development of an automated procedure for the optimal design of robotic systems. For this purpose, Pro/Engineer© and MatLab© software packages are integrated to draw the robot parts, optimize them, and then re-draw the optimal system parts.

Experimental and Theoretical Models to Probe Mechanisms of Biological Charge Flow

Nature is challenged to move charge efficiently over many length scales. From sub-nm to μm distances, electron-transfer proteins orchestrate energy conversion, storage, and release both inside and outside the cell. Uncovering the detailed mechanisms of biological electron-transfer reactions, which are often coupled to bond-breaking and bond-making events, is essential to designing durable, artificial energy conversion systems that mimic the specificity and efficiency of their natural counterparts. Here, we use theoretical modeling of long-distance charge hopping (Chapter 3), synthetic donor-bridge-acceptor molecules (Chapters 4, 5, and 6), and de novo protein design (Chapters 5 and 6) to investigate general principles that govern light-driven and electrochemically driven electron-transfer reactions in biology. We show that fast, μm-distance charge hopping along bacterial nanowires requires closely packed charge carriers with low reorganization energies (Chapter 3); singlet excited-state electronic polarization of supermolecular electron donors can attenuate intersystem crossing yields to lower-energy, oppositely polarized, donor triplet states (Chapter 4); the effective static dielectric constant of a small (~100 residue) de novo designed 4-helical protein bundle can change upon phototriggering an electron transfer event in the protein interior, providing a means to slow the charge-recombination reaction (Chapter 5); and a tightly-packed de novo designed 4-helix protein bundle can drastically alter charge-transfer driving forces of photo-induced amino acid radical formation in the bundle interior, effectively turning off a light-driven oxidation reaction that occurs in organic solvent (Chapter 6). This work leverages unique insights gleaned from proteins designed from scratch that bind synthetic donor-bridge-acceptor molecules that can also be studied in organic solvents, opening new avenues of exploration into the factors critical for protein control of charge flow in biology.

Konzeption, Implementierung und Anwendung eines automatisierten aerothermodynamischen Vorentwurfsprozesses für Axialturbinen

In dieser Arbeit wird ein Prozess für den frühen aerothermodynamischen Entwurf von Axialturbinen konzipiert und durch Kopplung einzelner Computerprogramme im DLR Göttingen realisiert. Speziell für die Erstauslegung von Geometrien und die Vorhersage von globalen Leistungsdaten beliebiger Axialturbinen wurde ein neues Programm erzeugt. Dessen effiziente Anwendung wird mit einer zu diesem Zweck konzipierten grafischen Entwurfsumgebung ausgeführt. Kennzeichnend für den Vorentwurfsprozess in dieser Arbeit ist die Anwendung von ein- und zweidimensionaler Strömungssimulation sowie der hohe Grad an Verknüpfung der verwendeten Programme sowohl auf prozesstechnischer wie auch auf datentechnischer Ebene. Dabei soll dem sehr frühen Entwurf eine deutlich stärkere Rolle zukommen als bisher üblich und im Gegenzug die Entwurfszeit mit höher auflösenden Vorentwurfsprogrammen reduziert werden. Die Anwendung der einzelnen Programme im Rahmen von Subprozessen wird anhand von exemplarischen Turbinenkonfigurationen in der Arbeit ebenso dargestellt, wie die Validierung des gesamten Entwurfsprozesses anhand der Auslegung einer folgend realisierten und erfolgreich operierenden Axialturbine eines Triebwerkssimulators für Flugzeug-Windkanalmodelle (TPS). Neben der Erleichterung von manueller Entwurfstätigkeit durch grafische Benutzerinteraktion kommt in einzelnen Subprozessen eine automatisierte Mehrziel-Optimierung zum Einsatz.

Shift Scheduling in Services When Employees Have Limited Availability: An L. P. Approach

This paper compares two linear programming (LP) models for shift scheduling in services where homogeneously-skilled employees are available at limited times. Although both models are based on set covering approaches, one explicitly matches employees to shifts, while the other imposes this matching implicitly. Each model is used in three forms—one with complete, another with very limited meal break placement flexibility, and a third without meal breaks—to provide initial schedules to a completion/improvement heuristic. The term completion/improvement heuristic is used to describe a construction/ improvement heuristic operating on a starting schedule. On 80 test problems varying widely in scheduling flexibility, employee staffing requirements, and employee availability characteristics, all six LP-based procedures generated lower cost schedules than a comparison from-scratch construction/improvement heuristic. This heuristic, which perpetually maintains an explicit matching of employees to shifts, consists of three phases which add, drop, and modify shifts. In terms of schedule cost, schedule generation time, and model size, the procedures based on the implicit model performed better, as a group, than those based on the explicit model. The LP model with complete break placement flexibility and implicitly matching employees to shifts generated schedules costing 6.7% less than those developed by the from-scratch heuristic.

Um conto, um canto, um encanto: a história, o mapa, a música em Belo Horizonte

O presente trabalho pretende ser uma contribuição no campo da etnomusicologia, e resulta de um estudo e pesquisa de campo realizada na cidade de Belo Horizonte, capital do estado de Minas Gerais, Brasil. Belo Horizonte é uma cidade “inventada”, assim como Brasília, a capital federal. Enquanto cidade, Belo Horizonte foi desenhada, projetada, nasceu de uma prancheta e foi oficialmente inaugurada em 12 de dezembro de 1897. Nesta tese tem como objetivo compreender e demonstrar como as transformações sociais e espaciais foram provocadas pelo acontecimento musical. Defendo a posição que o entendimento do que denomino Acontecimento Musical permite perceber o potencial da música de reorganizar normas, inicialmente defendidas pelos idealizadores da cidade, da utilização e ocupação dos lugares e espaços, e construindo, pela prática musical, uma nova cartografia da cidade, que se impõe a cartografia oficialmente idealizada, resultando em uma Cartografia Musical de Belo Horizonte.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

EasyInterface: a toolkit for the rapid development of GUIs for research prototype tools

During the lifetime of a research project, different partners develop several research prototype tools that share many common aspects. This is equally true for researchers as individuals and as groups: during a period of time they often develop several related tools to pursue a specific research line. Making research prototype tools easily accessible to the community is of utmost importance to promote the corresponding research, get feedback, and increase the tools’ lifetime beyond the duration of a specific project. One way to achieve this is to build graphical user interfaces (GUIs) that facilitate trying tools; in particular, with web-interfaces one avoids the overhead of downloading and installing the tools. Building GUIs from scratch is a tedious task, in particular for web-interfaces, and thus it typically gets low priority when developing a research prototype. Often we opt for copying the GUI of one tool and modifying it to fit the needs of a new related tool. Apart from code duplication, these tools will “live” separately, even though we might benefit from having them all in a common environment since they are related. This work aims at simplifying the process of building GUIs for research prototypes tools. In particular, we present EasyInterface, a toolkit that is based on novel methodology that provides an easy way to make research prototype tools available via common different environments such as a web-interface, within Eclipse, etc. It includes a novel text-based output language that allows to present results graphically without requiring any knowledge in GUI/Web programming. For example, an output of a tool could be (a structured version of) “highlight line number 10 of file ex.c” and “when the user clicks on line 10, open a dialog box with the text ...”. The environment will interpret this output and converts it to corresponding visual e_ects. The advantage of using this approach is that it will be interpreted equally by all environments of EasyInterface, e.g., the web-interface, the Eclipse plugin, etc. EasyInterface has been developed in the context of the Envisage [5] project, and has been evaluated on tools developed in this project, which include static analyzers, test-case generators, compilers, simulators, etc. EasyInterface is open source and available at GitHub2.

Synthetic Circuits for Feedback and Detection in Bacteria

Synthetic biology, by co-opting molecular machinery from existing organisms, can be used as a tool for building new genetic systems from scratch, for understanding natural networks through perturbation, or for hybrid circuits that piggy-back on existing cellular infrastructure. Although the toolbox for genetic circuits has greatly expanded in recent years, it is still difficult to separate the circuit function from its specific molecular implementation. In this thesis, we discuss the function-driven design of two synthetic circuit modules, and use mathematical models to understand the fundamental limits of circuit topology versus operating regimes as determined by the specific molecular implementation. First, we describe a protein concentration tracker circuit that sets the concentration of an output protein relative to the concentration of a reference protein. The functionality of this circuit relies on a single negative feedback loop that is implemented via small programmable protein scaffold domains. We build a mass-action model to understand the relevant timescales of the tracking behavior and how the input/output ratios and circuit gain might be tuned with circuit components. Second, we design an event detector circuit with permanent genetic memory that can record order and timing between two chemical events. This circuit was implemented using bacteriophage integrases that recombine specific segments of DNA in response to chemical inputs. We simulate expected population-level outcomes using a stochastic Markov-chain model, and investigate how inferences on past events can be made from differences between single-cell and population-level responses. Additionally, we present some preliminary investigations on spatial patterning using the event detector circuit as well as the design of stationary phase promoters for growth-phase dependent activation. These results advance our understanding of synthetic gene circuits, and contribute towards the use of circuit modules as building blocks for larger and more complex synthetic networks.

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.

Studio e Sperimentazione di Algoritmi di Reinforcement Learning Applicati a Video Game

Nella prima parte del mio lavoro viene presentato uno studio di una prima soluzione "from scratch" sviluppata da Andrew Karpathy. Seguono due miei miglioramenti: il primo modificando direttamente il codice della precedente soluzione e introducendo, come obbiettivo aggiuntivo per la rete nelle prime fasi di gioco, l'intercettazione della pallina da parte della racchetta, migliorando l'addestramento iniziale; il secondo é una mia personale implementazione utilizzando algoritmi più complessi, che sono allo stato dell'arte su giochi dell'Atari, e che portano un addestramento molto più veloce della rete.

HBIM methodology for structural preservation of historical buildings: the semantic modeling of the San Felice sul Panaro fortress

Building Information Modelling is changing the design and construction field ever since it entered the market. It took just some time to show its capabilities, it takes some time to be mastered before it could be used expressing all its best features. Since it was conceived to be adopted from the earliest stage of design to get the maximum from the decisional project, it still struggles to adapt to existing buildings. In fact, there is a branch of this methodology that is dedicated to what has been already made that is called Historic BIM or HBIM. This study aims to make clear what are BIM and HBIM, both from a theoretical point of view and in practice, applying from scratch the state of the art to a case study. It had been chosen the fortress of San Felice sul Panaro, a marvellous building with a thousand years of history in its bricks, that suffered violent earthquakes, but it is still standing. By means of this example, it will be shown which are the limits that could be encountered when applying BIM methodology to existing heritage, moreover will be pointed out all the new features that a simple 2D design could not achieve.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.

Power converters in WBG device technology for automotive applications and characterization setups for GaN power transistors

This PhD dissertation envisages the design of innovative power converters exploiting WBG devices to get state-of-the-art performance in products intended for industrial applications of automotive field. The collaborations with different specialized companies, provided the opportunity to access commercially-available state-of-the-art SiC and GaN technologies and the possibility to realize innovative converter prototypes. Concerning SiC technology, the complete design of a $350kW$ Battery Emulator instrument in collaboration with a company leader in the automotive testing sector, was carried out from scratch exploiting state-of-the-art SiC power-modules, planar magnetics and top-notch MCU technologies. Discrete high-voltage GaN switches were exploited in the Power Supplies design for automotive charger application to target improved performances compared to the market state-of-the-art. Specifically, two high-efficiency prototypes, an AC/DC converter and a DC/DC converter of $7.5kW$, have been realized for this purpose. To further investigate the characteristics of state-of-the-art GaN power devices two measurement set-ups have been designed. In particular, the trapping phenomenon causing the collapse of drain current during ON-state with a consequent degradation of ON-resistance has been analyzed.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.

Paradigm Shift in European Intellectual Property Law? From Microsoft to Linux

"L’auteure Lucie Guibault aborde la question des ""logiciels libres"" dans le cadre des droits d’auteurs et des brevets. Ces logiciels sont des programmes informatiques qui sont gratuitement distribués au public, dont la modification et la redistribution sont fortement encouragées, mais dont la vente ou la commercialisation est découragée ou même carrément prohibée. Ces caractéristiques particulières distinguent les logiciels libres des ""programmes propriétés"" traditionnels, qui se basent sur le principe que le créateur d’un programme en possède les droits de propriété et qu’il est le seul autorisé à le modifier ou le vendre, sous réserve de sa capacité à faire cession de ces droits. Les logiciels libres sont fondés sur une idéologie de coopération, qui promeut la propagation des idées et des connaissances et qui favorise ainsi la création de meilleurs logiciels. L’auteure présente les grandes caractéristiques des trois principales licences de logiciels libres, soit la ""General Public License"", la licence ""Berkeley Software Distribution"" et la ""Mozilla Public License"". Elle soutient que ces logiciels libres et les régimes normatifs qui les encadrent sont à l’origine d’un changement de paradigme au sein des régimes européens et hollandais de protection des droits d’auteurs et des brevets. La première partie de l’article analyse les régimes des droits d’auteur des trois licences de logiciels libres. L’auteure souligne que ces régimes ont été établis en se basant sur la prémisse qu’il n’y a pas de distinction significative entre les créateurs et les utilisateurs de logiciels libres. Les régimes normatifs reflètent cette situation en prévoyant un ensemble de droits et d’obligations pour les utilisateurs dans le cadre de l’utilisation, de la reproduction, de la modification et de la redistribution gratuite des logiciels libres. L’auteur explique comment ces régimes normatifs s’intègrent au sein de la législation européenne et hollandaise, entre autre au niveau du droit de propriété, du droit commercial, du droit des communications et du droit des obligations. L’auteur démontre que, de façon générale, ces régimes normatifs législatifs semblent s’être adéquatement adaptés aux nouvelles réalités posées par les règles de droits d’auteurs des logiciels libres. La seconde partie aborde la problématique du droit des brevets, tel que défini par la législation européenne et hollandaise. La plupart des utilisateurs et créateurs de logiciels libres s’opposent aux régimes de brevets traditionnels, qui limitent l’innovation et les possibilités de développement techniques. L’auteur décrit les différents régimes alternatifs de brevets offerts par les trois licences de logiciels libres. De plus, l’auteur présente l’encadrement légal pour ces nouveaux brevets, tel que défini par les législations européennes et hollandaises. Elle soutient que cet encadrement légal est inadéquat et qu’il n’est pas adapté aux besoins des utilisateurs de logiciels libres."