688 research outputs found
Literature review on the smart city resources analysis with big data methodologies
This article provides a systematic literature review on applying different algorithms to municipal data processing, aiming
to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select
feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification,
correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types,
ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long
Short-Term Memory (LSTM) for timeseries prediction and classification.Open access funding provided by FCT|FCCN (b-on).info:eu-repo/semantics/publishedVersio
Fuzzy Natural Logic in IFSA-EUSFLAT 2021
The present book contains five papers accepted and published in the Special Issue, âFuzzy Natural Logic in IFSA-EUSFLAT 2021â, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference âThe 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferencesâ, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IFâTHEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications
"Le present est plein de lâavenir, et chargĂ© du passĂ©" : VortrĂ€ge des XI. Internationalen Leibniz-Kongresses, 31. Juli â 4. August 2023, Leibniz UniversitĂ€t Hannover, Deutschland. Band 3
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersĂ€chsisches Ministerium fĂŒr Wissenschaft und Kultur (MWK
The community structure of the geometric soft configuration model
Treballs Finals de MĂ ster en FĂsica dels Sistemes Complexos i BiofĂsica, Facultat de FĂsica, Universitat de Barcelona. Curs: 2022-2023. Tutora: M.Ăngeles Serrano MoralNetwork models serve as an approach to explain the properties of real networks. The geometric soft configuration model, also known as the S1/H2 model, can be used to generate synthetic networks that replicate many features of real complex networks âsparsity, a heterogeneous degree distribution, the small world property, a high level of clustering, and moreâ while randomizing others. In this work, a range of parameters of the S1/H2 model has been explored, satisfactorily manipulating the level of heterogeneity of the degree distribution with the parameter Îł and the level of clustering with the parameter ÎČ, in order to probe the level of control that is possible to attain in the generation of random networks. Recent theoretical evidence supports that hyperbolic networks like this one possess topological community structure, up to being maximally modular in the thermodynamic limit, even if the model is not purposefully equipped with geometric communities. The community structure of the S1/H2 model was put under scrutiny using computational simulations, revealing
that synthetic networks generated according to it could be consistently partitioned with a high modularity. The modularity of equally sized angular partitions of the generated random networks was evaluated, confirming that this model tends to maximal modularity in the limit of large network size and in a regime of high clustering. The Louvain method for community detection in the topology of complex networks using modularity maximization was employed as well, giving rise to no significantly better results in comparison with the initial approach. With the S1/H2 model, it was also explored how much of the community structure of real networks can be attributed to the effect of clustering in combination with their heterogeneous degree distribution ânetworks with these two features are called hierarchicalâ. The results suggest that the communities detected in some real networks are, in part or totally, a byproduct of their hierarchicity
Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion
According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems
Euler Characteristic Tools For Topological Data Analysis
In this article, we study Euler characteristic techniques in topological data
analysis. Pointwise computing the Euler characteristic of a family of
simplicial complexes built from data gives rise to the so-called Euler
characteristic profile. We show that this simple descriptor achieve
state-of-the-art performance in supervised tasks at a very low computational
cost. Inspired by signal analysis, we compute hybrid transforms of Euler
characteristic profiles. These integral transforms mix Euler characteristic
techniques with Lebesgue integration to provide highly efficient compressors of
topological signals. As a consequence, they show remarkable performances in
unsupervised settings. On the qualitative side, we provide numerous heuristics
on the topological and geometric information captured by Euler profiles and
their hybrid transforms. Finally, we prove stability results for these
descriptors as well as asymptotic guarantees in random settings.Comment: 39 page
Opinion formation on evolving network. The DPA method applied to a nonlocal cross-diffusion PDE-ODE system
We study a system of nonlocal aggregation cross-diffusion PDEs that describe
the evolution of opinion densities on a network. The PDEs are coupled with a
system of ODEs that describe the time evolution of the agents on the network.
Firstly, we apply the Deterministic Particle Approximation (DPA) method to the
aforementioned system in order to prove the existence of solutions under
suitable assumptions on the interactions between agents. Later on, we present
an explicit model for opinion formation on an evolving network. The opinions
evolve based on both the distance between the agents on the network and the
'attitude areas,' which depend on the distance between the agents' opinions.
The position of the agents on the network evolves based on the distance between
the agents' opinions. The goal is to study radicalization, polarization, and
fragmentation of the population while changing its open-mindedness and the
radius of interaction
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services
This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book
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