Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

Evolutionary methods for self-organizing cooperation in peer-to-peer networks

The Peer-to-Peer network paradigm is drawing the attention of both final users and researchers for its features. P2P networks shift from the classic client-server approach to a high level of decentralization where there is no central control and all the nodes should be able not only to require services, but to provide them to other peers as well. While on one hand such high level of decentralization might lead to interesting properties like scalability and fault tolerance, on the other hand it implies many new problems to deal with. A key feature of many P2P systems is openness, meaning that everybody is potentially able to join a network with no need for subscription or payment systems. The combination of openness and lack of central control makes it feasible for a user to free-ride, that is to increase its own benefit by using services without allocating resources to satisfy other peers’ requests. One of the main goals when designing a P2P system is therefore to achieve cooperation between users. Given the nature of P2P systems based on simple local interactions of many peers having partial knowledge of the whole system, an interesting way to achieve desired properties on a system scale might consist in obtaining them as emergent properties of the many interactions occurring at local node level. Two methods are typically used to face the problem of cooperation in P2P networks: 1) engineering emergent properties when designing the protocol; 2) study the system as a game and apply Game Theory techniques, especially to find Nash Equilibria in the game and to reach them making the system stable against possible deviant behaviors. In this work we present an evolutionary framework to enforce cooperative behaviour in P2P networks that is alternative to both the methods mentioned above. Our approach is based on an evolutionary algorithm inspired by computational sociology and evolutionary game theory, consisting in having each peer periodically trying to copy another peer which is performing better. The proposed algorithms, called SLAC and SLACER, draw inspiration from tag systems originated in computational sociology, the main idea behind the algorithm consists in having low performance nodes copying high performance ones. The algorithm is run locally by every node and leads to an evolution of the network both from the topology and from the nodes’ strategy point of view. Initial tests with a simple Prisoners’ Dilemma application show how SLAC is able to bring the network to a state of high cooperation independently from the initial network conditions. Interesting results are obtained when studying the effect of cheating nodes on SLAC algorithm. In fact in some cases selfish nodes rationally exploiting the system for their own benefit can actually improve system performance from the cooperation formation point of view. The final step is to apply our results to more realistic scenarios. We put our efforts in studying and improving the BitTorrent protocol. BitTorrent was chosen not only for its popularity but because it has many points in common with SLAC and SLACER algorithms, ranging from the game theoretical inspiration (tit-for-tat-like mechanism) to the swarms topology. We discovered fairness, meant as ratio between uploaded and downloaded data, to be a weakness of the original BitTorrent protocol and we drew inspiration from the knowledge of cooperation formation and maintenance mechanism derived from the development and analysis of SLAC and SLACER, to improve fairness and tackle freeriding and cheating in BitTorrent. We produced an extension of BitTorrent called BitFair that has been evaluated through simulation and has shown the abilities of enforcing fairness and tackling free-riding and cheating nodes.

Computational tool for three-point hitch geometry optimization

The weight-transfer effect, consisting of the change in dynamic load distribution between the front and the rear tractor axles, is one of the most impairing phenomena for the performance, comfort, and safety of agricultural operations. Excessive weight transfer from the front to the rear tractor axle can occur during operation or maneuvering of implements connected to the tractor through the three-point hitch (TPH). In this respect, an optimal design of the TPH can ensure better dynamic load distribution and ultimately improve operational performance, comfort, and safety. In this study, a computational design tool (The Optimizer) for the determination of a TPH geometry that minimizes the weight-transfer effect is developed. The Optimizer is based on a constrained minimization algorithm. The objective function to be minimized is related to the tractor front-to-rear axle load transfer during a simulated reference maneuver performed with a reference implement on a reference soil. Simulations are based on a 3-degrees-of-freedom (DOF) dynamic model of the tractor-TPH-implement aggregate. The inertial, elastic, and viscous parameters of the dynamic model were successfully determined through a parameter identification algorithm. The geometry determined by the Optimizer complies with the ISO-730 Standard functional requirements and other design requirements. The interaction between the soil and the implement during the simulated reference maneuver was successfully validated against experimental data. Simulation results show that the adopted reference maneuver is effective in triggering the weight-transfer effect, with the front axle load exhibiting a peak-to-peak value of 27.1 kN during the maneuver. A benchmark test was conducted starting from four geometries of a commercially available TPH. As result, all the configurations were optimized by above 10%. The Optimizer, after 36 iterations, was able to find an optimized TPH geometry which allows to reduce the weight-transfer effect by 14.9%.

Dynamics of undeactuated cable-driven parallel robots

This thesis focuses on the dynamics of underactuated cable-driven parallel robots (UACDPRs), including various aspects of robotic theory and practice, such as workspace computation, parameter identification, and trajectory planning. After a brief introduction to CDPRs, UACDPR kinematic and dynamic models are analyzed, under the relevant assumption of inextensible cables. The free oscillatory motion of the end-effector (EE), which is a unique feature of underactuated mechanisms, is studied in detail, from both a kinematic and a dynamic perspective. The free (small) oscillations of the EE around equilibria are proved to be harmonic and the corresponding natural oscillation frequencies are analytically computed. UACDPR workspace computation and analysis are then performed. A new performance index is proposed for the analysis of the influence of actuator errors on cable tensions around equilibrium configurations, and a new type of workspace, called tension-error-insensitive, is defined as the set of poses that a UACDPR EE can statically attain even in presence of actuation errors, while preserving tensions between assigned (positive) bounds. EE free oscillations are then employed to conceive a novel procedure aimed at identifying the EE inertial parameters. This approach does not require the use of force or torque measurements. Moreover, a self-calibration procedure for the experimental determination of UACDPR initial cable lengths is developed, which consequently enables the robot to automatically infer the EE initial pose at machine start-up. Lastly, trajectory planning of UACDPRs is investigated. Two alternative methods are proposed, which aim at (i) reducing EE oscillations even when model parameters are uncertain or (ii) eliminate EE oscillations in case model parameters are perfectly known. EE oscillations are reduced in real-time by dynamically scaling a nominal trajectory and filtering it with an input shaper, whereas they can be eliminated if an off-line trajectory is computed that accounts for the system internal dynamics.

Probabilistic approaches in civil engineering: generation of random fields and structural identification with genetic algorithms

The inherent stochastic character of most of the physical quantities involved in engineering models has led to an always increasing interest for probabilistic analysis. Many approaches to stochastic analysis have been proposed. However, it is widely acknowledged that the only universal method available to solve accurately any kind of stochastic mechanics problem is Monte Carlo Simulation. One of the key parts in the implementation of this technique is the accurate and efficient generation of samples of the random processes and fields involved in the problem at hand. In the present thesis an original method for the simulation of homogeneous, multi-dimensional, multi-variate, non-Gaussian random fields is proposed. The algorithm has proved to be very accurate in matching both the target spectrum and the marginal probability. The computational efficiency and robustness are very good too, even when dealing with strongly non-Gaussian distributions. What is more, the resulting samples posses all the relevant, welldefined and desired properties of “translation fields”, including crossing rates and distributions of extremes. The topic of the second part of the thesis lies in the field of non-destructive parametric structural identification. Its objective is to evaluate the mechanical characteristics of constituent bars in existing truss structures, using static loads and strain measurements. In the cases of missing data and of damages that interest only a small portion of the bar, Genetic Algorithm have proved to be an effective tool to solve the problem.

Identification and genetic diversity in phytoplasmas associated with diseases of cassava and other agronomic relevant crops in south-east Asia and Latin America

Identification and genetic diversity of phytoplasmas infecting tropical plant species, selected among those most agronomically relevant in South-east Asia and Latin America were studied. Correlation between evolutionary divergence of relevant phytoplasma strains and their geographic distribution by comparison on homologous genes of phytoplasma strains detected in the same or related plant species in other geographical areas worldwide was achieved. Molecular diversity was studied on genes coding ribosomal proteins, groEL, tuf and amp besides phytoplasma 16S rRNA. Selected samples infected by phytoplasmas belonging to diverse ribosomal groups were also studied by in silico RFLP followed by phylogenetic analyses. Moreover a partial genome annotation of a ‘Ca. P. brasiliense’ strain was done towards future application for epidemiological studies. Phytoplasma presence in cassava showing frog skin (CFSD) and witches’ broom (CWB) diseases in Costa Rica - Paraguay and in Vietnam – Thailand, respectively, was evaluated. In both cases, the diseases were associated with phytoplasmas related to aster yellows, apple proliferation and “stolbur” groups, while only phytoplasma related to X-disease group in CFSD, and to hibiscus witches’ broom, elm yellows and clover proliferation groups in CWB. Variability was found among strains belonging to the same ribosomal group but having different geographic origin and associated with different disease. Additionally, a dodder transmission assay to elucidate the role of phytoplasmas in CWB disease was carried out, and resulted in typical phytoplasma symptoms in periwinkle plants associated with the presence of aster yellows-related strains. Lethal wilt disease, a severe disease of oil palm in Colombia that is spreading throughout South America was also studied. Phytoplasmas were detected in symptomatic oil palm and identified as ‘Ca. P. asteris’, ribosomal subgroup 16SrI-B, and were distinguished from other aster yellows phytoplasmas used as reference strains; in particular, from an aster yellows strain infecting corn in the same country.

TNFRSF13B Genetic variability an anthropological - evolutionary approach to Biomedical Research

In the recent years TNFRSF13B coding variants have been implicated by clinical genetics studies in Common Variable Immunodeficiency (CVID), the most common clinically relevant primary immunodeficiency in individuals of European ancestry, but their functional effects in relation to the development of the disease have not been entirely established. To examine the potential contribution of such variants to CVID, the more comprehensive perspective of an evolutionary approach was applied in this study, underling the belief that evolutionary genetics methods can play a role in dissecting the origin, causes and diffusion of human diseases, representing a powerful tool also in human health research. For this purpose, TNFRSF13B coding region was sequenced in 451 healthy individuals belonging to 26 worldwide populations, in addition to 96 control, 77 CVID and 38 Selective IgA Deficiency (IgAD) individuals from Italy, leading to the first achievement of a global picture of TNFRSF13B nucleotide diversity and haplotype structure and making suggestion of its evolutionary history possible. A slow rate of evolution, within our species and when compared to the chimpanzee, low levels of genetic diversity geographical structure and the absence of recent population specific selective pressures were observed for the examined genomic region, suggesting that geographical distribution of its variability is more plausibly related to its involvement also in innate immunity rather than in adaptive immunity only. This, together with the extremely subtle disease/healthy samples differences observed, suggests that CVID might be more likely related to still unknown environmental and genetic factors, rather than to the nature of TNFRSF13B variants only.

Genetic characterization, population history and evolutionary medicine perspective in two native south american populations: Yanesha and Wichi

Two Amerindian populations from the Peruvian Amazon (Yanesha) and from rural lowlands of the Argentinean Gran Chaco (Wichi) were analyzed. They represent two case study of the South American genetic variability. The Yanesha represent a model of population isolated for long-time in the Amazon rainforest, characterized by environmental and altitudinal stratifications. The Wichi represent a model of population living in an area recently colonized by European populations (the Criollos are the population of the admixed descendents), whose aim is to depict the native ancestral gene pool and the degree of admixture, in relation to the very high prevalence of Chagas disease. The methods used for the genotyping are common, concerning the Y chromosome markers (male lineage) and the mitochondrial markers (maternal lineage). The determination of the phylogeographic diagnostic polymorphisms was carried out by the classical techniques of PCR, restriction enzymes, sequencing and specific mini-sequencing. New method for the detection of the protozoa Trypanosoma cruzi was developed by means of the nested PCR. The main results show patterns of genetic stratification in Yanesha forest communities, referable to different migrations at different times, estimated by Bayesian analyses. In particular Yanesha were considered as a population of transition between the Amazon basin and the Andean Cordillera, evaluating the potential migration routes and the separation of clusters of community in relation to different genetic bio-ancestry. As the Wichi, the gene pool analyzed appears clearly differentiated by the admixed sympatric Criollos, due to strict social practices (deeply analyzed with the support of cultural anthropological tools) that have preserved the native identity at a diachronic level. A pattern of distribution of the seropositivity in relation to the different phylogenetic lineages (the adaptation in evolutionary terms) does not appear, neither Amerindian nor European, but in relation to environmental and living conditions of the two distinct subpopulations.

Identification of novel genetic alterations in pediatric cytogenetically normal acute myeloid leukemia by next-generation sequencing

Pediatric acute myeloid leukemia (AML) is a molecularly heterogeneous disease that arises from genetic alterations in pathways that regulate self-renewal and myeloid differentiation. While the majority of patients carry recurrent chromosomal translocations, almost 20% of childhood AML do not show any recognizable cytogenetic alteration and are defined as cytogenetically normal (CN)-AML. CN-AML patients have always showed a great variability in response to therapy and overall outcome, underlining the presence of unknown genetic changes, not detectable by conventional analyses, but relevant for pathogenesis, and outcome of AML. The development of novel genome-wide techniques such as next-generation sequencing, have tremendously improved our ability to interrogate the cancer genome. Based on this background, the aim of this research study was to investigate the mutational landscape of pediatric CN-AML patients negative for all the currently known somatic mutations reported in AML through whole-transcriptome sequencing (RNA-seq). RNA-seq performed on diagnostic leukemic blasts from 19 pediatric CN-AML cases revealed a considerable incidence of cryptic chromosomal rearrangements, with the identification of 21 putative fusion genes. Several of the fusion genes that were identified in this study are recurrent and might have a prognostic and/or therapeutic relevance. A paradigm of that is the CBFA2T3-GLIS2 fusion, which has been demonstrated to be a common alteration in pediatric CN-AML, predicting poor outcome. Important findings have been also obtained in the identification of novel therapeutic targets. On one side, the identification of NUP98-JARID1A fusion suggests the use of disulfiram; on the other, here we describe alteration-activating tyrosine kinases, providing functional data supporting the use of tyrosine kinase inhibitors to specifically inhibit leukemia cells. This study provides new insights in the knowledge of genetic alterations underlying pediatric AML, defines novel prognostic markers and putative therapeutic targets, and prospectively ensures a correct risk stratification and risk-adapted therapy also for the “all-neg” AML subgroup.

A Trans-dimensional inversion algorithm to model deformation sources with unconstrained shape in finite element domains

Ground deformation provides valuable insights on subsurface processes with pattens reflecting the characteristics of the source at depth. In active volcanic sites displacements can be observed in unrest phases; therefore, a correct interpretation is essential to assess the hazard potential. Inverse modeling is employed to obtain quantitative estimates of parameters describing the source. However, despite the robustness of the available approaches, a realistic imaging of these reservoirs is still challenging. While analytical models return quick but simplistic results, assuming an isotropic and elastic crust, more sophisticated numerical models, accounting for the effects of topographic loads, crust inelasticity and structural discontinuities, require much higher computational effort and information about the crust rheology may be challenging to infer. All these approaches are based on a-priori source shape constraints, influencing the solution reliability. In this thesis, we present a new approach aimed at overcoming the aforementioned limitations, modeling sources free of a-priori shape constraints with the advantages of FEM simulations, but with a cost-efficient procedure. The source is represented as an assembly of elementary units, consisting in cubic elements of a regular FE mesh loaded with a unitary stress tensors. The surface response due to each of the six stress tensor components is computed and linearly combined to obtain the total displacement field. In this way, the source can assume potentially any shape. Our tests prove the equivalence of the deformation fields due to our assembly and that of corresponding cavities with uniform boundary pressure. Our ability to simulate pressurized cavities in a continuum domain permits to pre-compute surface responses, avoiding remeshing. A Bayesian trans-dimensional inversion algorithm implementing this strategy is developed. 3D Voronoi cells are used to sample the model domain, selecting the elementary units contributing to the source solution and those remaining inactive as part of the crust.