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Parameters Estimation of Hydraulic Circuit Head Losses for Virtual Sensor Design
No full textThis paper presents the procedure used to design a so-called virtual sensor (VS) that is able to estimate fluid flow rates (FRs) in the elements of a complex hydraulic closed network, only knowing the total FR imposed by the circulator pump and the open/closed state of the valves that section off each subnet. The VS is based on a mathematical model representing the relationship between FR and head loss (HL). An identification procedure is developed to estimate the numerical values of the parameters of the FR/HL relationship. Once the mathematical model is completely known, in terms of its topological representation and the numerical values of all the model parameters, a flow-solver algorithm is used to estimate the FR in each branch of the hydraulic circuit. The importance of such a mathematical device is evidenced by the necessity to compute the heating power, directly depending on the hydraulic FR, that each single heating body releases, in order to give a new solution going beyond the current technologies to many problems, such as, for example, the optimization of the central heat generator efficiency or the fair cost allocation in the management of old centralized heating plants. The good results obtained from different tests, carried on a reference mock-up, are presented to prove the reliability and the efficiency of the proposed approach
Artificial neural network for detecting incorrectly fixed phase ambiguities for L1 mass-market receivers
No full textOne of the main challenges in global navigation satellite systems (GNSS) network real-time kinematic positioning is phase ambiguity estimation. We describe methods that predict false fixing (FF) of phase ambiguities in mass-market receivers. In this work, FF is defined to occur when the differences between 3D coordinates estimated in real-time differ by more than 20 cm with respect to the reference coordinates. Phase ambiguity FF events occur for many reasons, such as wrong estimation of phase ambiguities by the network software, noise in the corrections, and the environment of the rover. Moreover, one of the main reasons for phase ambiguity FF is the high level of noise and the low redundancy of observation by receivers that track L1 frequencies only. We develop and analyze a specific tool utilizing an artificial neural network that, when trained, tested, and refined specifically for GNSS mass-market receivers, can predict and detect FF. This tool comprises three inputs for all epochs, the Horizontal Dilution of Precision index, the latency of the differential correction, and the number of satellites with fixed phase ambiguities seen by the rover. It provides as output an index consisting of values 0 or 1, i.e., 0 for no FF and 1 for FF. A description of the training and validating phases is provided. The results of tests show that the algorithm has a 99.7% probability of detecting phase ambiguity FF in these cases
Tensor decomposition techniques for analysing time-varying networks
No full textThe aim of this Ph.D thesis is the study of time-varying networks via theoretical and data-driven approaches. Networks are natural objects to represent a vast variety of systems in nature, e.g., communication networks (phone calls and e-mails), online social networks (Facebook, Twitter), infrastructural networks, etc. Considering the temporal dimension of networks helps to better understand and predict complex phenomena, by taking into account both the fact that links in the network are not continuously active over time and the potential relation between multiple dimensions, such as space and time. A fundamental challenge in this area is the definition of mathematical models and tools able to capture topological and dynamical aspects and to reproduce properties observed on the real dynamics of networks. Thus, the purpose of this thesis is threefold: 1) we will focus on the analysis of the complex mesoscale patterns, as community like structures and their evolution in time, that characterize time-varying networks; 2) we will study how these patterns impact dynamical processes that occur over the network; 3) we will sketch a generative model to study the interplay between topological and temporal patterns of time-varying networks and dynamical processes occurring over the network, e.g., disease spreading. To tackle these problems, we adopt and extend an approach at the intersection between multi-linear algebra and machine learning: the decomposition of time-varying networks represented as tensors (multi-dimensional arrays). In particular, we focus on the study of Non-negative Tensor Factorization (NTF) techniques to detect complex topological and temporal patterns in the network. We first extend the NTF framework to tackle the problem of detecting anomalies in time-varying networks. Then, we propose a technique to approximate and reconstruct time-varying networks affected by missing information, to both recover the missing values and to reproduce dynamical processes on top of the network. Finally, we focus on the analysis of the interplay between the discovered patterns and dynamical processes. To this aim, we use the NTF as an hint to devise a generative model of time-varying networks, in which we can control both the topological and temporal patterns, to identify which of them has a major impact on the dynamics
A method to solve the efficiency-accuracy trade-off of multi-harmonic balance calculation of structures with friction contacts
No full textTransition Towards a Post Carbon City - Does Resilience Matter?
No full textthe chapter focuses on the relations between cities and climate change and introduces the concept of post carbon city. Section 4.3 discusses the concept of sustainable development and current approaches to achieve it in cities. It argues that resilient thinking approaches are more appropriate than current efficiency approaches for dealing with transitions toward post carbon city. Finally, it presents a number of case studies derived from an analysis developed by MILESECURE‐2050 (2012). Section 4.4 provides final remarks and further develop-ments of this stud
Intra-speaker and inter-speaker variability in speech sound pressure level across repeated readings
No full textThe intra- and inter-speaker variability of speech sound pressure level (SPL) has been investigated under repeatability conditions in this work. In a semi-anechoic chamber, speech from 17 individuals was recorded with a sound level meter, a headworn microphone, and a vocal monitoring device. The subjects were asked to read twice and in sequence two phonetically balanced passages. The speech variability has been investigated for mean, equivalent, and mode SPL from each reading and device. The intra-speaker variability has been evaluated by means of the average among individual standard deviations in the four readings and it reached the maximum of 2 dB for mode SPL. For the inter-speaker variability, the experimental standard deviation of individual averaged SPL parameters among the four repeated measures has been calculated, obtaining the highest value of 5.3 dB for mode SPL. Changes in SPL variability have been evaluated with different logging intervals for each device. The influence of speech material has been investigated by the Wilcoxon test on paired lists of descriptive statistics for SPL distribution and equivalent SPL in the repeated readings. The data reported in this study may be considered as a preliminary reference for the investigation of changes in speech SPL over subjects
A Participatory Design Approach for Energy-Aware Mobile App for Smart Home Monitoring
No full textIt is generally recognized that our behaviours affect the environment. However, it is difficult to correlate behaviour of an individual person to large-scale problems. This is usually due to insufficient ergonomy of available tools. The main cause is that most of user-awareness tools available are technology-centered instead of user-centered. In this paper, we present a participatory design approach we followed to design and develop an energy-aware mobile application for user-awareness on energy consumption for Smart Home monitoring. To engage end-users from the early design stages, we conduct two on-line surveys and a focus group involving about 630 people. Results allowed on identifying functional requirements and guidelines for mobile app design. The purpose of this research is to increase user-awareness on energy consumption using tools and methods required by users themselves. Furthermore in this paper, we present the technological choices that drove our implementation of an energy-aware application based on prosumers' requirements
Analysis of a multi-population kinetic model for traffic flow
No full textIn this work we extend a recent kinetic traffic model [G. Puppo, M. Semplice, A. Tosin, and G. Visconti, Kinet. Relat. Models, in press, 2016] to the case of more than one class of vehicles, each of which is characterized by few different microscopic features. We consider a Boltzmann-like framework with only binary interactions, which take place among vehicles belonging to the various classes. Our approach differs from the multi-population kinetic model proposed in [G. Puppo, M. Semplice, A. Tosin, and G. Visconti, Commun. Math. Sci., 14:643-669, 2016] because here we assume continuous velocity spaces and we introduce a parameter describing the physical velocity jump performed by a vehicle that increases its speed after an interaction. The model is discretized in order to investigate numerically the structure of the resulting fundamental diagrams and the system of equations is analyzed by studying well posedness. Moreover, we compute the equilibria of the discretized model and we show that the exact asymptotic kinetic distributions can be obtained with a small number of velocities in the grid. Finally, we introduce a new probability law in order to attenuate the sharp capacity drop occurring in the diagrams of traffic