14 resultados para Control flow graphs

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


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This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).

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This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

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Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

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The study of optic flow on postural control may explain how self-motion perception contributes to postural stability in young males and females and how such function changes in the old falls risk population. Study I: The aim was to examine the optic flow effect on postural control in young people (n=24), using stabilometry and surface-electromyography. Subjects viewed expansion and contraction optic flow stimuli which were presented full field, in the foveral or in the peripheral visual field. Results showed that optic flow stimulation causes an asymmetry in postural balance and a different lateralization of postural control in men and women. Gender differences evoked by optic flow were found both in the muscle activity and in the prevalent direction of oscillation. The COP spatial variability was reduced during the view of peripheral stimuli which evoked a clustered prevalent direction of oscillation, while foveal and random stimuli induced non-distributed directions. Study II was aimed at investigating the age-related mechanisms of postural stability during the view of optic flow stimuli in young (n=17) and old (n=19) people, using stabilometry and kinematic. Results showed that old people showed a greater effort to maintain posture during the view of optic flow stimuli than the young. Elderly seems to use the head stabilization on trunk strategy. Visual stimuli evoke an excitatory input on postural muscles, but the stimulus structure produces different postural effects. Peripheral optic flow stabilizes postural sway, while random and foveal stimuli provoke larger sway variability similar to those evoked in baseline. Postural control uses different mechanisms within each leg to produce the appropriate postural response to interact with extrapersonal environment. Ageing reduce the effortlessness to stabilize posture during optic flow, suggesting a neuronal processing decline associated with difficulty integrating multi-sensory information of self-motion perception and increasing risk of falls.

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The velocity and mixing field of two turbulent jets configurations have been experimentally characterized by means of cold- and hot-wire anemometry in order to investigate the effects of the initial conditions on the flow development. In particular, experiments have been focused on the effect of the separation wall between the two streams on the flow field. The results of the experiments have pointed out that the wake behind a thick wall separating wall has a strong influence on the flow field evolution. For instance, for nearly unitary velocity ratios, a clear vortex shedding from the wall is observable. This phenomenon enhances the mixing between the inner and outer shear layer. This enhancement in the fluctuating activity is a consequence of a local absolute instability of the flow which, for a small range of velocity ratios, behaves as an hydrodynamic oscillator with no sensibility to external perturbations. It has been suggested indeed that this absolute instability can be used as a passive method to control the flow evolution. Finally, acoustic excitation has been applied to the near field in order to verify whether or not the observed vortex shedding behind the separating wall is due to a global oscillating mode as predicted by the theory. A new scaling relationship has been also proposed to determine the preferred frequency for nearly unitary velocity ratios. The proposed law takes into account both the Reynolds number and the velocity ratio dependence of this frequency and, therefore, improves all the previously proposed relationships.

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The Gaia space mission is a major project for the European astronomical community. As challenging as it is, the processing and analysis of the huge data-flow incoming from Gaia is the subject of thorough study and preparatory work by the DPAC (Data Processing and Analysis Consortium), in charge of all aspects of the Gaia data reduction. This PhD Thesis was carried out in the framework of the DPAC, within the team based in Bologna. The task of the Bologna team is to define the calibration model and to build a grid of spectro-photometric standard stars (SPSS) suitable for the absolute flux calibration of the Gaia G-band photometry and the BP/RP spectrophotometry. Such a flux calibration can be performed by repeatedly observing each SPSS during the life-time of the Gaia mission and by comparing the observed Gaia spectra to the spectra obtained by our ground-based observations. Due to both the different observing sites involved and the huge amount of frames expected (≃100000), it is essential to maintain the maximum homogeneity in data quality, acquisition and treatment, and a particular care has to be used to test the capabilities of each telescope/instrument combination (through the “instrument familiarization plan”), to devise methods to keep under control, and eventually to correct for, the typical instrumental effects that can affect the high precision required for the Gaia SPSS grid (a few % with respect to Vega). I contributed to the ground-based survey of Gaia SPSS in many respects: with the observations, the instrument familiarization plan, the data reduction and analysis activities (both photometry and spectroscopy), and to the maintenance of the data archives. However, the field I was personally responsible for was photometry and in particular relative photometry for the production of short-term light curves. In this context I defined and tested a semi-automated pipeline which allows for the pre-reduction of imaging SPSS data and the production of aperture photometry catalogues ready to be used for further analysis. A series of semi-automated quality control criteria are included in the pipeline at various levels, from pre-reduction, to aperture photometry, to light curves production and analysis.

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This thesis deals with distributed control strategies for cooperative control of multi-robot systems. Specifically, distributed coordination strategies are presented for groups of mobile robots. The formation control problem is initially solved exploiting artificial potential fields. The purpose of the presented formation control algorithm is to drive a group of mobile robots to create a completely arbitrarily shaped formation. Robots are initially controlled to create a regular polygon formation. A bijective coordinate transformation is then exploited to extend the scope of this strategy, to obtain arbitrarily shaped formations. For this purpose, artificial potential fields are specifically designed, and robots are driven to follow their negative gradient. Artificial potential fields are then subsequently exploited to solve the coordinated path tracking problem, thus making the robots autonomously spread along predefined paths, and move along them in a coordinated way. Formation control problem is then solved exploiting a consensus based approach. Specifically, weighted graphs are used both to define the desired formation, and to implement collision avoidance. As expected for consensus based algorithms, this control strategy is experimentally shown to be robust to the presence of communication delays. The global connectivity maintenance issue is then considered. Specifically, an estimation procedure is introduced to allow each agent to compute its own estimate of the algebraic connectivity of the communication graph, in a distributed manner. This estimate is then exploited to develop a gradient based control strategy that ensures that the communication graph remains connected, as the system evolves. The proposed control strategy is developed initially for single-integrator kinematic agents, and is then extended to Lagrangian dynamical systems.

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This thesis deals with the study of optimal control problems for the incompressible Magnetohydrodynamics (MHD) equations. Particular attention to these problems arises from several applications in science and engineering, such as fission nuclear reactors with liquid metal coolant and aluminum casting in metallurgy. In such applications it is of great interest to achieve the control on the fluid state variables through the action of the magnetic Lorentz force. In this thesis we investigate a class of boundary optimal control problems, in which the flow is controlled through the boundary conditions of the magnetic field. Due to their complexity, these problems present various challenges in the definition of an adequate solution approach, both from a theoretical and from a computational point of view. In this thesis we propose a new boundary control approach, based on lifting functions of the boundary conditions, which yields both theoretical and numerical advantages. With the introduction of lifting functions, boundary control problems can be formulated as extended distributed problems. We consider a systematic mathematical formulation of these problems in terms of the minimization of a cost functional constrained by the MHD equations. The existence of a solution to the flow equations and to the optimal control problem are shown. The Lagrange multiplier technique is used to derive an optimality system from which candidate solutions for the control problem can be obtained. In order to achieve the numerical solution of this system, a finite element approximation is considered for the discretization together with an appropriate gradient-type algorithm. A finite element object-oriented library has been developed to obtain a parallel and multigrid computational implementation of the optimality system based on a multiphysics approach. Numerical results of two- and three-dimensional computations show that a possible minimum for the control problem can be computed in a robust and accurate manner.

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Nanotechnologies are rapidly expanding because of the opportunities that the new materials offer in many areas such as the manufacturing industry, food production, processing and preservation, and in the pharmaceutical and cosmetic industry. Size distribution of the nanoparticles determines their properties and is a fundamental parameter that needs to be monitored from the small-scale synthesis up to the bulk production and quality control of nanotech products on the market. A consequence of the increasing number of applications of nanomaterial is that the EU regulatory authorities are introducing the obligation for companies that make use of nanomaterials to acquire analytical platforms for the assessment of the size parameters of the nanomaterials. In this work, Asymmetrical Flow Field-Flow Fractionation (AF4) and Hollow Fiber F4 (HF5), hyphenated with Multiangle Light Scattering (MALS) are presented as tools for a deep functional characterization of nanoparticles. In particular, it is demonstrated the applicability of AF4-MALS for the characterization of liposomes in a wide series of mediums. Afterwards the technique is used to explore the functional features of a liposomal drug vector in terms of its biological and physical interaction with blood serum components: a comprehensive approach to understand the behavior of lipid vesicles in terms of drug release and fusion/interaction with other biological species is described, together with weaknesses and strength of the method. Afterwards the size characterization, size stability, and conjugation of azidothymidine drug molecules with a new generation of metastable drug vectors, the Metal Organic Frameworks, is discussed. Lastly, it is shown the applicability of HF5-ICP-MS for the rapid screening of samples of relevant nanorisk: rather than a deep and comprehensive characterization it this time shown a quick and smart methodology that within few steps provides qualitative information on the content of metallic nanoparticles in tattoo ink samples.

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The purpose of the air traffic management system is to ensure the safe and efficient flow of air traffic. Therefore, while augmenting efficiency, throughput and capacity in airport operations, attention has rightly been placed on doing it in a safe manner. In the control tower, many advances in operational safety have come in the form of visualization tools for tower controllers. However, there is a paradox in developing such systems to increase controllers' situational awareness: by creating additional computer displays, the controller's vision is pulled away from the outside view and the time spent looking down at the monitors is increased. This reduces their situational awareness by forcing them to mentally and physically switch between the head-down equipment and the outside view. This research is based on the idea that augmented reality may be able to address this issue. The augmented reality concept has become increasingly popular over the past decade and is being proficiently used in many fields, such as entertainment, cultural heritage, aviation, military & defense. This know-how could be transferred to air traffic control with a relatively low effort and substantial benefits for controllers’ situation awareness. Research on this topic is consistent with SESAR objectives of increasing air traffic controllers’ situation awareness and enable up to 10 % of additional flights at congested airports while still increasing safety and efficiency. During the Ph.D., a research framework for prototyping augmented reality tools was set up. This framework consists of methodological tools for designing the augmented reality overlays, as well as of hardware and software equipment to test them. Several overlays have been designed and implemented in a simulated tower environment, which is a virtual reconstruction of Bologna airport control tower. The positive impact of such tools was preliminary assessed by means of the proposed methodology.

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Marek's disease (MD) is a contagious, lymphoproliferative and neuropathic disease of poultry caused by a ubiquitous lymphotropic and oncogenic virus, Gallid alphaherpesvirus 2 (GaHV-2). MD has been reported in all poultry-rearing countries and is among the viral diseases with the highest economic impact in the poultry industry worldwide, including Italy. MD has been also recognized as one of the leading causes of mortality in backyard poultry. The present doctoral thesis aimed at exploring Marek's disease virus molecular epidemiology in Italian commercial and backyard chicken flocks and, for the first time, in commercial turkeys affected by clinical MD. Molecular biology techniques targeting the full-length meq gene, the major GaHV-2 oncogene, were used to detect and characterize the circulating GaHV-2 strains searching for genetic markers of virulence. A final study focused on the development of rapid, sensitive, and species-specific loop-mediated isothermal amplification assays coupled with a lateral flow device readout for the detection of conventional and recombinant HVT-based vaccines is included in the thesis. HVT vaccines, currently used to protect chickens from MD, are referred to as "leaky", as they do not impede the infection, replication, and shedding of field GaHV-2: vaccinal and field viruses can coexist in the vaccinated host and molecular tests able to discriminate between GaHV-2 and HVT are required. These new simple, fast, and accurate tests for the monitoring of MD vaccination success in the field could be greatly beneficial for field veterinarians, small laboratories, and more broadly for resource-limited settings.

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The main purpose of this work is to develop a numerical platform for the turbulence modeling and optimal control of liquid metal flows. Thanks to their interesting thermal properties, liquid metals are widely studied as coolants for heat transfer applications in the nuclear context. However, due to their low Prandtl numbers, the standard turbulence models commonly used for coolants as air or water are inadequate. Advanced turbulence models able to capture the anisotropy in the flow and heat transfer are then necessary. In this thesis, a new anisotropic four-parameter turbulence model is presented and validated. The proposed model is based on explicit algebraic models and solves four additional transport equations for dynamical and thermal turbulent variables. For the validation of the model, several flow configurations are considered for different Reynolds and Prandtl numbers, namely fully developed flows in a plane channel and cylindrical pipe, and forced and mixed convection in a backward-facing step geometry. Since buoyancy effects cannot be neglected in liquid metals-cooled fast reactors, the second aim of this work is to provide mathematical and numerical tools for the simulation and optimization of liquid metals in mixed and natural convection. Optimal control problems for turbulent buoyant flows are studied and analyzed with the Lagrange multipliers method. Numerical algorithms for optimal control problems are integrated into the numerical platform and several simulations are performed to show the robustness, consistency, and feasibility of the method.

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Atrial fibrillation (AF) is a widespread arrhythmia, associated with higher risk of stroke, sleep disorders and dementia. In some conditions, electrical cardioversion (ECV) represents the best choice for rhythm control. Nowadays, there is a growing interest in developing new devices for screening and monitoring of AF patients. We aimed to improve acute efficacy of ECV procedure and to explore the feasibility of the use of new wearable devices for monitoring in candidates to AF ECV. We compared antero-apical pads vs antero-posterior patches approach for AF ECV, and we elaborated a decision algorithm to improve acute efficacy. After, we evaluated the feasibility of the use of new wearable devices for monitoring of candidates to AF ECV. In particular, we analysed the effect of AF ECV on heart rate variability and vascular age parameters derived from PPG signals registered with Empatica (CE 1876/MDD 93/42/EEC), and on EEG pattern registered with Neurosteer (Israel). From December 2005 to September 2019, 492 patients were enrolled. We evaluated acute efficacy of the two approaches for AF ECV and we elaborated a decision algorithm based on body surface area, weight, and height. The decision algorithm improved first shock efficacy (93.2% vs. 87.2%, p=0.025). From 1st November 2021 to 1st April 2022, 24 patients were enrolled in PPEEG-AF pilot study. Considering vascular age parameters, a significant reduction in TPR and a wave was observed (p<0.001). Considering sleep patterns, a tendency to higher coherence was observed in registrations acquired during AF, or considering signals registered for each patient independently from AF. The new decision algorithm improved acute efficacy and reduced costs associated with adhesive patches. Significant modifications were observed on vascular age parameters measured before and after ECV, and a possible AF effect on sleep pattern was noticed. More data are necessary to confirm these preliminary results.

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The aim of this research is to improve the understanding of the factors that control the formation of karst porosity in hypogene settings and its associated patterns of void-conduit networks. Subsurface voids created by hypogene dissolution may span from few microns to decametric tubes providing interconnected conduit systems and forming highly anisotropic permeability domains in many reservoirs. Characterizing the spatial-morphological organization of hypogene karst is a challenging task that has dramatic implications for the applied industry, given that only partial data can be acquired from the subsurface by indirect techniques. Therefore, two outcropping cave analogues are examined: the Cavallone-Bove Cave in the Majella Massif (Italy), and the karst systems of the Salitre Formation (Brazil). In the latter, a peculiar example of hypogene speleogenesis associated with silicification has been studied, providing an analogue of many karstified reservoirs hosted in cherts or cherty-carbonates within mixed sedimentary sequences. The first part of the thesis is focused on the relationships between fracture patterns and flow pathways in deformed units in: 1) a fold-and-thrust setting (Majella Massif); 2) a cratonic block (Brazil). These settings represent potential playgrounds for the migration and accumulation of geofluids, where hypogene conduits may affect flow pathways, fluid storage, and reservoir properties. The results indicate that localized deformation producing cross-formational fracture zones associated with anticline hinges or fault damage zones is critical for hypogene fluid migration and karstification. The second part of the thesis deals with the multidisciplinary study of hydrothermal silicification and hypogene dissolution in Calixto Cave (Brazil). Petrophysical analyses and a geochemical characterization of silica deposits are used to unravel the spatial-morphological organization of the conduit system and its speleogenesis. The novel results obtained from this cave shed new light on the relationship between hydrothermal silicification, hypogene dissolution and the development of multistorey cave systems in layered carbonate-siliciclastic sequences.