265 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Energy crisis in Europe: the European Union’s objectives and countries’ policy trends—new transition paths?

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    Amidst the ongoing European energy crisis, the EU has proposed a legislative package to enhance gas independence from Russia, diversify energy supplies, and increase renewable energy targets. However, the urgency for energy security has led some countries to prioritise gas independence over decarbonisation, potentially sacrificing or delaying EU targets. Considering this framework, this article contributes to the body of knowledge by examining the electricity mix of the six most significant EU countries in terms of generation capacity, considers their alignment with 2025 energy transition goals, and analyses the latest legislative trends to evaluate their compatibility with EU objectives. The findings from these analyses indicate that EU members are currently prioritising gas independence, which has led to re-starting or extending the lifespan of coal-fired power plants and an increasing interest in nuclear energy as a low-carbon alternative. These findings have significant implications as they reveal how countries are being steered away from their pre-crisis energy transition paths, resulting in the formation of new perspectives for both the short and long term.This research has been funded by the European Social Fund and the Secretariat of Universities and Research of Catalonia.Peer ReviewedPostprint (published version

    Ideologia della “propaganda” e propaganda “delle ideologie”. Critica del paradigma psico-sociologico e proposte semiotiche per lo studio della “comunicazione politica”

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    La tesi si compone di tre capitoli: nel primo si svolge una critica storico-concettuale al paradigma dominante tramite cui è pensato il rapporto tra comunicazione, politica e democrazia, mentre nel secondo e nel terzo si inquadrano questi stessi processi a partire dalla riflessione semiotica di matrice linguistico-strutturale. In questo contesto si mette a punto l'idea di "ideologia" come "codice semiotico" e si discute la possibilità di applicarlo allo studio empirico sistematico della comunicazione politica

    Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

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    Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results

    Unlocking the Pragmatics of Emoji: Evaluation of the Integration of Pragmatic Markers for Sarcasm Detection

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    Emojis have become an integral element of online communications, serving as a powerful, under-utilised resource for enhancing pragmatic understanding in NLP. Previous works have highlighted their potential for improvement of more complex tasks such as the identification of figurative literary devices including sarcasm due to their role in conveying tone within text. However present state-of-the-art does not include the consideration of emoji or adequately address sarcastic markers such as sentiment incongruence. This work aims to integrate these concepts to generate more robust solutions for sarcasm detection leveraging enhanced pragmatic features from both emoji and text tokens. This was achieved by establishing methodologies for sentiment feature extraction from emojis and a depth statistical evaluation of the features which characterise sarcastic text on Twitter. Current convention for generation of training data which implements weak-labelling using hashtags or keywords was evaluated against a human-annotated baseline; postulated validity concerns were verified where statistical evaluation found the content features deviated significantly from the baseline, highlighting potential validity concerns for many prominent works on the topic to date. Organic labelled sarcastic tweets containing emojis were crowd sourced by means of a survey to ensure valid outcomes for the sarcasm detection model. Given an established importance of both semantic and sentiment information, a novel sentiment-aware attention mechanism was constructed to enhance pattern recognition, balancing core features of sarcastic text: sentiment incongruence and context. This work establishes a framework for emoji feature extraction; a key roadblock cited in literature for their use in NLP tasks. The proposed sarcasm detection pipeline successfully facilitates the task using a GRU neural network with sentiment-aware attention, at an accuracy of 73% and promising indications regarding model robustness as part of a framework which is easily scalable for the inclusion of any future emojis released. Both enhanced sentiment information to supplement context in addition to consideration of the emoji were found to improve outcomes for the task

    Quantifying non-exhaust emissions and the impact of hybrid and electric vehicles using combined measurement and modelling approaches

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    Road traffic is a significant emission source of urban particulate matter (PM). Due to the implementation of exhaust regulatory standards in the UK, PM emissions which arise from the wear of brakes, tyres and the road surface, together with the resuspension of road dust are now predicted to exceed tailpipe emissions. While a growing body of academic literature has developed in recent years, non-exhaust emissions (NEE) remain unregulated and largely understudied, and the impact of powertrain electrification on the vehicle fleet has not been quantified. Thus, the aim of this thesis is to improve our understanding of these important emission sources and to determine the impact of NEE on urban air pollution - both now, and in the future. A series of highly time-resolved atmospheric measurement campaigns has been undertaken at roadside and background locations to determine roadside traffic increments. These measurements provide a comprehensive dataset of traffic emissions in London, Birmingham and Manchester, incorporating locations with different vehicle mix and speed, during summer and winter periods. PM mass and elemental tracers have been used to estimate the contribution of NEE concentrations using a scaling factor approach. A novel CO2 dilution approach has been undertaken to determine average fleet emission factors (EFs), whilst the impact of electric vehicle regenerative braking has also been simulated. The results indicate that NEE concentrations and EFs are highly dependent upon meteorological conditions, traffic speed, traffic volume and vehicle class. Brake wear is the dominant source of road traffic PM emissions in congested environments, whilst for each emission source, heavy duty vehicles (HDVs) contribute an order of magnitude greater than light duty vehicles (LDVs). On the other hand, despite the predicted increase in mass, the regenerative braking simulations suggest that passenger vehicles under electric powertrains will reduce brake wear emissions by 65 – 95%. This reduction depends on the assessed drive cycle and vehicle class, highlighting the importance of driving style on future brake wear emissions. The EFs developed in this thesis have been combined with traffic forecasts to project total national emissions in the UK up to 2035 – and can be used to validate the national atmospheric emission inventory. To conclude, a number of recommendations have been made with respect to air quality measurement strategies and emission policies which are needed to further our understanding of NEE, and to reduce these traffic-related emissions. It is proposed that a multi-disciplinary study should be undertaken encompassing laboratory dynamometer testing, on-vehicle measurements and environmental atmospheric measurements.Open Acces

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    Modelling as Research Methodology

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    Modelling as Research Methodology is written for the scientist and student researching the (expected) functioning of systems under specified conditions. As such, it represents an introduction to the use of modelling in natural, human and economical sciences. The book is divided into two sections. The first section illustrates the universal nature of modelling as aid to the researcher. In the second section, several typical examples of modelling are described
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