DICAR - Artículoshttps://hdl.handle.net/10481/137062024-03-28T10:51:54Z2024-03-28T10:51:54ZInteractive simulations as a tool for logistics and maintenance of IFMIF-DONESBenito Fuentes, Andreahttps://hdl.handle.net/10481/899982024-03-15T07:47:17ZInteractive simulations as a tool for logistics and maintenance of IFMIF-DONES
Benito Fuentes, Andrea
Pre-configured virtual reality (VR) simulations of the logistics and maintenance processes have proven to be useful for identifying potential design issues as well as planning operations during an early design phase of facility. But VR simulations can also be used to deeply explore the feasibility of these procedures in a more interactive manner, so that we can identify potential risks and difficulty levels from early stages and study different maintenance strategies to assist the maintenance worker during these procedures. This article presents a framework to design and validate logistics and maintenance procedures in complex facilities, such as the International Fusion Materials Irradiation Facility DEMO Oriented Neutron Source (IFMIF-DONES). Our framework begins with a preparatory phase where essential information about the procedures and Computer-Aided Design (CAD) models is compiled into a comprehensive Virtualization Task Document (VTD). Differently from previous work, this VTD allows representation of parallel tasks. We implement the interactive version of the virtual environment, where the different maintenance and logistics equipment, as well as a virtual maintenance worker is controlled by the user (the person executing the interactive simulation). We have validated this interactive framework with two simulations for the installation process of the Superconducting Radio Frequency Linear accelerator (SRF Linac) modules in IFMIF-DONES. In one simulation (the automatic one), the procedures are reproduced as they are planned, while in the second simulation (the interactive one) the user freely controls the movements of the moving parts of the crane, grab and release plant equipment, move platforms, etc. Based on our simulations, the interactive version allows easier detection of potential points of collisions as well as more precise assessment of the difficulty of the tasks to be performed.
Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students’ Attention and the Estimation of Academic Performance in Secondary SchoolFuentes-Martínez, Víctor JuanRomero García, Samuel FranciscoLópez Gordo, Miguel ÁngelMinguillón Campos, JesúsRodríguez Álvarez, Manuelhttps://hdl.handle.net/10481/888152024-02-09T09:32:58ZLow-Cost EEG Multi-Subject Recording Platform for the Assessment of Students’ Attention and the Estimation of Academic Performance in Secondary School
Fuentes-Martínez, Víctor Juan; Romero García, Samuel Francisco; López Gordo, Miguel Ángel; Minguillón Campos, Jesús; Rodríguez Álvarez, Manuel
The level of student attention in class greatly affects their academic performance. Teachers
typically rely on visual inspection to react to students’ attention in time, but this subjective method
leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off
cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost
EEG-based platform for assessing students’ attention and estimating their academic performance. In a
study involving 34 secondary school students (aged 14 to 16), participants watched an academic video
and answered evaluation questions while their EEG activity was recorded using a commercial headset.
The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral
density (PSD) of the EEG beta band (12–30 Hz) and students’ academic performance. Additionally,
there was a notable difference in PSD-beta between high and low academic performers. These
findings encourage the use of PSD-beta for the immediate and objective assessment of both the
student attention and the subsequent academic performance. The platform offers valuable and
objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching
and learning environments.
Intra-family links in the analysis of marital networksMerelo Guervos, Juan JuliánMolinari, M. Cristinahttps://hdl.handle.net/10481/873412024-01-26T09:34:12ZIntra-family links in the analysis of marital networks
Merelo Guervos, Juan Julián; Molinari, M. Cristina
Marriage networks, which represent the matrimonial connections between different families in a given historical and geographical milieu, rarely take into account one aspect of internal family dynamics, namely the existence of intra-family marriages. The inclusion of such marriages, represented in the graph by self-loops, is essential in order to compute more accurate measures of centrality. In this paper, we discuss various procedures for incorporating these links into the analysis, with the requirement that they be compatible with the use of already available social network analysis software. We then apply them to two historical marriage networks, one from the Republic of Venice and the other from Taiwan. By comparing centrality measures for the baseline and modified networks, we found that the most satisfactory of the proposed methods is the one that duplicates nodes of families with intra-family marriages and adds new edges that link these duplicated nodes to all the families to which the original node was connected. This procedure is computationally simple and conceptually sound, making it a useful tool for analysing marital networks.
Estimation of COVID-19 dynamics in the different states of the United States using Time-Series ClusteringRojas Ruiz, Fernando JoséValenzuela Cansino, OlgaRojas Ruiz, Ignaciohttps://hdl.handle.net/10481/872792024-01-25T11:02:45ZEstimation of COVID-19 dynamics in the different states of the United States using Time-Series Clustering
Rojas Ruiz, Fernando José; Valenzuela Cansino, Olga; Rojas Ruiz, Ignacio
Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together in a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows to compare time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR model was computed. With these mathematical model it is possible to have a more precise knowledge of the evolution and forecast of the pandemic.
StarTroper, a film trope rating optimizer using Deep Learning and Evolutionary AlgorithmsGarcía-Ortega, Rubén HéctorGarcía Sánchez, PabloMerelo Guervos, Juan Juliánhttps://hdl.handle.net/10481/871272024-01-23T08:50:04ZStarTroper, a film trope rating optimizer using Deep Learning and Evolutionary Algorithms
García-Ortega, Rubén Héctor; García Sánchez, Pablo; Merelo Guervos, Juan Julián
Designing a story is widely considered a crafty yet critical task that requires deep specific human
knowledge in order to reach a minimum quality and originality. This includes designing at a high
level different elements of the film; these high-level elements are called tropes when they become
patterns. The present paper proposes and evaluates a methodology to automatically synthesise
sets of tropes in a way that they maximize the potential rating of a film that conforms to them.
We use deep learning to create a surrogate model mapping film ratings from tropes, trained
with the data extracted and processed from huge film databases in Internet, and then we use a
Genetic Algorithm that uses that surrogate model as evaluator to optimize the combination of
tropes in a film. In order to evaluate the methodology, we analyse the nature of the tropes and
their distributions in existing films, the performance of the models and the quality of the sets of
tropes synthesised. The results of this proof of concept show that the methodology works and is
able to build sets of tropes that maximize the rating and that these sets are genuine. The work
has revealed that the methodology and tools developed are directly suitable for assisting in the
plots generation as an authoring tool and, ultimately, for supporting the automatic generation of
stories, for example, in massively populated videogames.