Data-Driven and Hybrid Methods for Naval Applications

The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones

Marine safety and data analytics : vessel crash stop maneuvering performance prediction

Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents

Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system

A new series on diagnostic echographic cases and living brief reviews: a potentially useful tool for clinicians edited by FADOI

Sonography – similar to what happened almost two centuries ago with the introduction of stethoscopes – has completely changed patients’ clinical management in Internal Medicine. The availability of performant, sometimes even small-sized and cost-effective machines, has allowed doctors in Internal-Medicine units to perform bedside-ultrasound examinations alongside regular clinical ones. [...

Whole mitochondrial DNA sequencing in Alpine populations and the genetic history of the Neolithic Tyrolean Iceman

The Tyrolean Iceman is an extraordinarily well-preserved natural mummy that lived south of the Alpine ridge ~5,200 years before present (ybp), during the Copper Age. Despite studies that have investigated his genetic profile, the relation of the Iceman´s maternal lineage with present-day mitochondrial variation remains elusive. Studies of the Iceman have shown that his mitochondrial DNA (mtDNA) belongs to a novel lineage of haplogroup K1 (K1f) not found in extant populations. We analyzed the complete mtDNA sequences of 42 haplogroup K bearing individuals from populations of the Eastern Italian Alps – putatively in genetic continuity with the Tyrolean Iceman—and compared his mitogenome with a large dataset of worldwide K1 sequences. Our results allow a re-definition of the K1 phylogeny and indicate that the K1f haplogroup is absent or rare in present-day populations. We suggest that mtDNA Iceman´s lineage could have disappeared during demographic events starting in Europe from ~5,000 ybp. Based on the comparison of our results with published data, we propose a scenario that could explain the apparent contrast between the phylogeographic features of maternal and paternal lineages of the Tyrolean Iceman within the context of the demographic dynamics happening in Europe from 8,000 ybp.This study was financed by the Provincia Autonoma di Bolzano – Alto Adige, Ripartizione Diritto allo studio, università e ricerca scientifica, funds to VCS

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel

May Measure Month 2022 in Italy: A Focus on Fixed-dose Combination, Therapeutic Adherence, and Medical Inertia in a Nationwide Survey

Introduction Hypertension is the main risk factor for cardiovascular diseases (CVD). Notably, only about half of hypertensive patients manage to achieve the recommended blood pressure (BP) control. Main reasons for the persistence of uncontrolled BP during treatment are lack of compliance on the patients' side, and therapeutic inertia on physicians' side.Methods During the global BP screening campaign "May Measure Month" (MMM) (May 1st to July 31st, 2022), a nationwide, cross-sectional, opportunistic study endorsed by the Italian Society of Hypertension was conducted on volunteer adults >= 18 years to raise awareness of the health issues surrounding high BP. A questionnaire on demographic/clinical features and questions on the use of fixed-dose single-pills for the treatment of hypertension was administered. BP was measured with standard procedures.Results A total of 1612 participants (mean age 60.0 +/- 15.41 years; 44.7% women) were enrolled. Their mean BP was 128.5 +/- 18.1/77.1 +/- 10.4 mmHg. About half of participants were sedentary, or overweight/obese, or hypertensive. 55.5% individuals with complete BP assessment had uncontrolled hypertension. Most were not on a fixed-dose combination of antihypertensive drugs and did not regularly measure BP at home. Self-reported adherence to BP medications was similar between individuals with controlled and uncontrolled BP (95% vs 95.5%).Conclusions This survey identified a remarkable degree of therapeutic inertia and poor patients' involvement in the therapeutic process and its monitoring in the examined population, underlining the importance of prevention campaigns to identify areas of unsatisfactory management of hypertension, to increase risk factors' awareness in the population with the final purpose of reducing cardiovascular risk

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Conduction mechanisms in a planar nanocomposite resistive switching device based on cluster-assembled Au/ZrO_x films

Nanostructured zirconia and gold films (ns-Au/ZrOx) have been demonstrated as devices characterized by non-linear and hysteretic electrical behavior, with short-term memory and potentiation/depression activity. Here we investigate the conduction mechanisms regulating the non-linear behavior of the nanostructured bilayer Au/ZrOx films. In particular, we investigated the hysteretic I-V curves following the Chua’s approach to memristive systems and separately modelling ion migration and electron transport in the films. The conduction mechanisms exhibited by the bilayered nanostructured system are strongly influenced by the nanogranular morphology that dynamically changes because of electrical stimuli; structural rearrangements are particularly promoted by intense local electric fields and high mobility along bottlenecks and edges in the microstructure. Electron transport is accounted for the Schottky barrier at the electrode interfaces and Poole-Frenkel effect in the bulk nanogranular material, according to a dynamic reorganization of the cluster-assembled network. A model for Poole-Frenkel effect is here discussed to include saturation of the Coulombic traps in the high applied field regime; the proposed model has been validated with experimental voltage ramps with varying sweep-velocity and at different temperatures (from 300 to 200 K), as also by a power exponent parameter analysis.</p