A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Models and Simulations of Diamond-like Carbon for large-area high voltage power diodes

Silicon-based discrete high-power devices need to be designed with optimal performance up to several thousand volts and amperes to reach power ratings ranging from few kWs to beyond the 1 GW mark. To this purpose, a key element is the improvement of the junction termination (JT) since it allows to drastically reduce surface electric field peaks which may lead to an earlier device failure. This thesis will be mostly focused on the negative bevel termination which from several years constitutes a standard processing step in bipolar production lines. A simple methodology to realize its counterpart, a planar JT with variation of the lateral doping concentration (VLD) will be also described. On the JT a thin layer of a semi insulating material is usually deposited, which acts as passivation layer reducing the interface defects and contributing to increase the device reliability. A thorough understanding of how the passivation layer properties affect the breakdown voltage and the leakage current of a fast-recovery diode is fundamental to preserve the ideal termination effect and provide a stable blocking capability. More recently, amorphous carbon, also called diamond-like carbon (DLC), has been used as a robust surface passivation material. By using a commercial TCAD tool, a detailed physical explanation of DLC electrostatic and transport properties has been provided. The proposed approach is able to predict the breakdown voltage and the leakage current of a negative beveled power diode passivated with DLC as confirmed by the successfully validation against the available experiments. In addition, the VLD JT proposed to overcome the limitation of the negative bevel architecture has been simulated showing a breakdown voltage very close to the ideal one with a much smaller area consumption. Finally, the effect of a low junction depth on the formation of current filaments has been analyzed by performing reverse-recovery simulations.

Planning and flexible operation of storage systems in power grids: from transmission to distribution networks

The first part of the thesis has been devoted to the transmission planning with high penetration of renewable energy sources. Both stationary and transportable battery energy storage (BES, BEST) systems have been considered in the planning model, so to obtain the optimal set of BES, BEST and transmission lines that minimizes the total cost in a power network. First, a coordinated expansion planning model with fixed transportation cost for BEST devices has been presented; then, the model has been extended to a planning formulation with a distance-dependent transportation cost for the BEST units, and its tractability has been proved through a case study based on a 190-bus test system. The second part of this thesis is then devoted to the analysis of planning and management of renewable energy communities (RECs). Initially, the planning of photovoltaic and BES systems in a REC with an incentive-based remuneration scheme according to the Italian regulatory framework has been analysed, and two planning models, according to a single-stage, or a multi-stage approach, have been proposed in order to provide the optimal set of BES and PV systems allowing to achieve the minimum energy procurement cost in a given REC. Further, the second part of this thesis is devoted to the study of the day-ahead scheduling of resources in renewable energy communities, by considering two types of REC. The first one, which we will refer to as “cooperative community”, allows direct energy transactions between members of the REC; the second type of REC considered, which we shall refer to as “incentive-based”, does not allow direct transactions between members but includes economic revenues for the community shared energy, according to the Italian regulation framework. Moreover, dispatchable renewable energy generation has been considered by including producers equipped with biogas power plants in the community.

A multidisciplinary approach to the study of protein dynamics and signal transmission

Allostery is a phenomenon of fundamental importance in biology, allowing regulation of function and dynamic adaptability of enzymes and proteins. Despite the allosteric effect was first observed more than a century ago allostery remains a biophysical enigma, defined as the “second secret of life”. The challenge is mainly associated to the rather complex nature of the allosteric mechanisms, which manifests itself as the alteration of the biological function of a protein/enzyme (e.g. ligand/substrate binding at the active site) by binding of “other object” (“allos stereos” in Greek) at a site distant (> 1 nanometer) from the active site, namely the effector site. Thus, at the heart of allostery there is signal propagation from the effector to the active site through a dense protein matrix, with a fundamental challenge being represented by the elucidation of the physico-chemical interactions between amino acid residues allowing communicatio n between the two binding sites, i.e. the “allosteric pathways”. Here, we propose a multidisciplinary approach based on a combination of computational chemistry, involving molecular dynamics simulations of protein motions, (bio)physical analysis of allosteric systems, including multiple sequence alignments of known allosteric systems, and mathematical tools based on graph theory and machine learning that can greatly help understanding the complexity of dynamical interactions involved in the different allosteric systems. The project aims at developing robust and fast tools to identify unknown allosteric pathways. The characterization and predictions of such allosteric spots could elucidate and fully exploit the power of allosteric modulation in enzymes and DNA-protein complexes, with great potential applications in enzyme engineering and drug discovery.

High power density electrical machines with hairpin windings

Nowadays, electrical machines are seeing an ever-increasing development and extensive research is currently being dedicated to the improvement of their efficiency and torque/power density. Compared to conventional random windings, hairpin winding inherently features lower DC resistance, higher fill factor, better thermal performance, improved reliability, and an automated manufacturing process. However, several challenges need to be addressed, including electromagnetic, thermal, and manufacturing aspects. Of these, the high ohmic losses at high-frequency operations due to skin and proximity effects are the most severe, resulting in low efficiency or high-temperature values. In this work, the hairpin winding challenges were highlighted at high-frequency operations and at showing the limits of applicability of these standard approaches. Afterward, a multi-objective design optimization is proposed aiming to enhance the exploitation of the hairpin technology in electrical machines. Efficiency and volume power density are considered as main design objectives. Subsequently, a changing paradigm is made for the design of electric motors equipped with hairpin windings, where it is proven that a temperature-oriented approach would be beneficial when designing this type of pre-formed winding. Furthermore, the effect of the rotor topology on AC losses is also considered. After providing design recommendations and FE electromagnetic and thermal evaluations, experimental tests are also performed for validation purposes on a motorette wound with pre-formed conductors. The results show that operating the machine at higher temperatures could be beneficial to efficiency, particularly in high-frequency operations where AC losses are higher at low operating temperatures. The last part of the thesis focuses on comparing the main electromagnetic performance metrics for a conventional hairpin winding, wound onto a benchmark stator with a semi-closed slot opening design, and a continuous hairpin winding, in which the slot opening is open. Lastly, the adoption of semi-magnetic slot wedges is investigated to improve the overall performance of the motor.

Physical modeling and numerical simulations of degradation mechanisms in devices and insulators for power applications

In this thesis, a TCAD approach for the investigation of charge transport in amorphous silicon dioxide is presented for the first time. The proposed approach is used to investigate high-voltage silicon oxide thick TEOS capacitors embedded in the back-end inter-level dielectric layers for galvanic insulation applications. In the first part of this thesis, a detailed review of the main physical and chemical properties of silicon dioxide and the main physical models for the description of charge transport in insulators are presented. In the second part, the characterization of high-voltage MIM structures at different high-field stress conditions up to the breakdown is presented. The main physical mechanisms responsible of the observed results are then discussed in details. The third part is dedicated to the implementation of a TCAD approach capable of describing charge transport in silicon dioxide layers in order to gain insight into the microscopic physical mechanisms responsible of the leakage current in MIM structures. In particular, I investigated and modeled the role of charge injection at contacts and charge build-up due to trapping and de-trapping mechanisms in the oxide layer to the purpose of understanding its behavior under DC and AC stress conditions. In addition, oxide breakdown due to impact-ionization of carriers has been taken into account in order to have a complete representation of the oxide behavior at very high fields. Numerical simulations have been compared against experiments to quantitatively validate the proposed approach. In the last part of the thesis, the proposed approach has been applied to simulate the breakdown in realistic structures under different stress conditions. The TCAD tool has been used to carry out a detailed analysis of the most relevant physical quantities, in order to gain a detailed understanding on the main mechanisms responsible for breakdown and guide design optimization.

Design of a wearable platform for PPG analysis with multicore low-power processor

Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).

Embedding Video Object Detection Capabilities on Low-Power Nano-Drones

Our objective for this thesis work was the deployment of a Neural Network based approach for video object detection on board a nano-drone. Furthermore, we have studied some possible extensions to exploit the temporal nature of videos to improve the detection capabilities of our algorithm. For our project, we have utilized the Mobilenetv2/v3SSDLite due to their limited computational and memory requirements. We have trained our networks on the IMAGENET VID 2015 dataset and to deploy it onto the nano-drone we have used the NNtool and Autotiler tools by GreenWaves. To exploit the temporal nature of video data we have tried different approaches: the introduction of an LSTM based convolutional layer in our architecture, the introduction of a Kalman filter based tracker as a postprocessing step to augment the results of our base architecture. We have obtain a total improvement in our performances of about 2.5 mAP with the Kalman filter based method(BYTE). Our detector run on a microcontroller class processor on board the nano-drone at 1.63 fps.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.

Phosphate Regulated Proteins Of Xanthomonas Citri Subsp. Citri: A Proteomic Approach.

Xanthomonas citri subsp. citri (X. citri) is the causative agent of the citrus canker, a disease that affects several citrus plants in Brazil and across the world. Although many studies have demonstrated the importance of genes for infection and pathogenesis in this bacterium, there are no data related to phosphate uptake and assimilation pathways. To identify the proteins that are involved in the phosphate response, we performed a proteomic analysis of X. citri extracts after growth in three culture media with different phosphate concentrations. Using mass spectrometry and bioinformatics analysis, we showed that X. citri conserved orthologous genes from Pho regulon in Escherichia coli, including the two-component system PhoR/PhoB, ATP binding cassette (ABC transporter) Pst for phosphate uptake, and the alkaline phosphatase PhoA. Analysis performed under phosphate starvation provided evidence of the relevance of the Pst system for phosphate uptake, as well as both periplasmic binding proteins, PhoX and PstS, which were formed in high abundance. The results from this study are the first evidence of the Pho regulon activation in X. citri and bring new insights for studies related to the bacterial metabolism and physiology. Biological significance Using proteomics and bioinformatics analysis we showed for the first time that the phytopathogenic bacterium X. citri conserves a set of proteins that belong to the Pho regulon, which are induced during phosphate starvation. The most relevant in terms of conservation and up-regulation were the periplasmic-binding proteins PstS and PhoX from the ABC transporter PstSBAC for phosphate, the two-component system composed by PhoR/PhoB and the alkaline phosphatase PhoA.

A New Extraction Approach To Correct The Effect Of Apparent Increase In The Secoiridoid Content After Filtration Of Virgin Olive Oil.

In the current study, a new approach has been developed for correcting the effect that moisture reduction after virgin olive oil (VOO) filtration exerts on the apparent increase of the secoiridoid content by using an internal standard during extraction. Firstly, two main Spanish varieties (Picual and Hojiblanca) were submitted to industrial filtration of VOOs. Afterwards, the moisture content was determined in unfiltered and filtered VOOs, and liquid-liquid extraction of phenolic compounds was performed using different internal standards. The resulting extracts were analyzed by HPLC-ESI-TOF/MS, in order to gain maximum information concerning the phenolic profiles of the samples under study. The reduction effect of filtration on the moisture content, phenolic alcohols, and flavones was confirmed at the industrial scale. Oleuropein was chosen as internal standard and, for the first time, the apparent increase of secoiridoids in filtered VOO was corrected, using a correction coefficient (Cc) calculated from the variation of internal standard area in filtered and unfiltered VOO during extraction. This approach gave the real concentration of secoiridoids in filtered VOO, and clarified the effect of the filtration step on the phenolic fraction. This finding is of great importance for future studies that seek to quantify phenolic compounds in VOOs.

The Crystal-t Algorithm: A New Approach To Calculate The Sle Of Lipidic Mixtures Presenting Solid Solutions.

Lipidic mixtures present a particular phase change profile highly affected by their unique crystalline structure. However, classical solid-liquid equilibrium (SLE) thermodynamic modeling approaches, which assume the solid phase to be a pure component, sometimes fail in the correct description of the phase behavior. In addition, their inability increases with the complexity of the system. To overcome some of these problems, this study describes a new procedure to depict the SLE of fatty binary mixtures presenting solid solutions, namely the Crystal-T algorithm. Considering the non-ideality of both liquid and solid phases, this algorithm is aimed at the determination of the temperature in which the first and last crystal of the mixture melts. The evaluation is focused on experimental data measured and reported in this work for systems composed of triacylglycerols and fatty alcohols. The liquidus and solidus lines of the SLE phase diagrams were described by using excess Gibbs energy based equations, and the group contribution UNIFAC model for the calculation of the activity coefficients of both liquid and solid phases. Very low deviations of theoretical and experimental data evidenced the strength of the algorithm, contributing to the enlargement of the scope of the SLE modeling.

Effects Of Therapeutic Approach On The Neonatal Evolution Of Very Low Birth Weight Infants With Patent Ductus Arteriosus.

To analyze the effects of treatment approach on the outcomes of newborns (birth weight [BW] < 1,000 g) with patent ductus arteriosus (PDA), from the Brazilian Neonatal Research Network (BNRN) on: death, bronchopulmonary dysplasia (BPD), severe intraventricular hemorrhage (IVH III/IV), retinopathy of prematurity requiring surgical (ROPsur), necrotizing enterocolitis requiring surgery (NECsur), and death/BPD. This was a multicentric, cohort study, retrospective data collection, including newborns (BW < 1000 g) with gestational age (GA) < 33 weeks and echocardiographic diagnosis of PDA, from 16 neonatal units of the BNRN from January 1, 2010 to Dec 31, 2011. Newborns who died or were transferred until the third day of life, and those with presence of congenital malformation or infection were excluded. Groups: G1 - conservative approach (without treatment), G2 - pharmacologic (indomethacin or ibuprofen), G3 - surgical ligation (independent of previous treatment). Factors analyzed: antenatal corticosteroid, cesarean section, BW, GA, 5 min. Apgar score < 4, male gender, Score for Neonatal Acute Physiology Perinatal Extension (SNAPPE II), respiratory distress syndrome (RDS), late sepsis (LS), mechanical ventilation (MV), surfactant (< 2 h of life), and time of MV. death, O2 dependence at 36 weeks (BPD36wks), IVH III/IV, ROPsur, NECsur, and death/BPD36wks. Student's t-test, chi-squared test, or Fisher's exact test; Odds ratio (95% CI); logistic binary regression and backward stepwise multiple regression. Software: MedCalc (Medical Calculator) software, version 12.1.4.0. p-values < 0.05 were considered statistically significant. 1,097 newborns were selected and 494 newborns were included: G1 - 187 (37.8%), G2 - 205 (41.5%), and G3 - 102 (20.6%). The highest mortality was observed in G1 (51.3%) and the lowest in G3 (14.7%). The highest frequencies of BPD36wks (70.6%) and ROPsur were observed in G3 (23.5%). The lowest occurrence of death/BPD36wks occurred in G2 (58.0%). Pharmacological (OR 0.29; 95% CI: 0.14-0.62) and conservative (OR 0.34; 95% CI: 0.14-0.79) treatments were protective for the outcome death/BPD36wks. The conservative approach of PDA was associated to high mortality, the surgical approach to the occurrence of BPD36wks and ROPsur, and the pharmacological treatment was protective for the outcome death/BPD36wks.

Applying The Maternal Near Miss Approach For The Evaluation Of Quality Of Obstetric Care: A Worked Example From A Multicenter Surveillance Study.

To assess quality of care of women with severe maternal morbidity and to identify associated factors. This is a national multicenter cross-sectional study performing surveillance for severe maternal morbidity, using the World Health Organization criteria. The expected number of maternal deaths was calculated with the maternal severity index (MSI) based on the severity of complication, and the standardized mortality ratio (SMR) for each center was estimated. Analyses on the adequacy of care were performed. 17 hospitals were classified as providing adequate and 10 as nonadequate care. Besides almost twofold increase in maternal mortality ratio, the main factors associated with nonadequate performance were geographic difficulty in accessing health services (P < 0.001), delays related to quality of medical care (P = 0.012), absence of blood derivatives (P = 0.013), difficulties of communication between health services (P = 0.004), and any delay during the whole process (P = 0.039). This is an example of how evaluation of the performance of health services is possible, using a benchmarking tool specific to Obstetrics. In this study the MSI was a useful tool for identifying differences in maternal mortality ratios and factors associated with nonadequate performance of care.