Browsing by Keyword "Physics and Astronomy (miscellaneous)"
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Item Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest(2019-03-01) Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Picón, Artzai; Alonso, Erik; Owens, Pamela; Idris, Ahamed; COMPUTER_VISIONThe automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.Item Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States(2022-10-25) Liu, Ruilin; V. Romero, Sebastián; Oregi, Izaskun; Osaba, Eneko; Villar-Rodriguez, Esther; Ban, Yue; Tecnalia Research & Innovation; QuantumCoherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources—such as numbers of quantum gates, layers and iterations—are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes.Item Effect of the Metal Transfer Mode on the Symmetry of Bead Geometry in WAAM Aluminum(2021-07-10) Veiga, Fernando; Suárez, Alfredo; Aldalur, Eider; Bhujangrao, Trunal; Tecnalia Research & Innovation; FABRIC_INTELThe symmetrical nature in the case of wall fabrication by wire arc additive manufacturing (WAAM) has been observed in the literature, but it has not been studied as a source of knowledge. This paper focuses on the comparative study of three drop transfer methods employing Gas Metal Arc Welding (GMAW) technology, one of the most reported for the manufacture of aluminum alloys. The transfer modes studied are the well-known pulsed GMAW, cold arc, and the newer pulsed AC. The novelty of the last transfer mode is the reversal of the polarity during the preparation phase of the substance for droplet deposition. This study compares the symmetry of zero beads to determine the best parameters and transfer modes for wire arc additive manufacturing of 5 series aluminum. The pulsed transfer modes show values of 0.6 for symmetry ratio, which makes them more interesting strategies than cold arc with a symmetry ratio of 0.5. Furthermore, the methodology proposed in this study can be extrapolated to other materials manufactured with this technology.Item Three-Dimensional Finite Element Modelling of Sheet Metal Forming for the Manufacture of Pipe Components: Symmetry Considerations: Symmetry Considerations(2022-01-25) Bhujangrao, Trunal; Veiga, Fernando; Penalva, Mariluz; Costas, Adriana; Ruiz, Cristina; Tecnalia Research & Innovation; FABRIC_INTELThe manufacture of parts by metal forming is a widespread technique in sectors such as oil and gas and automotives. It is therefore important to make a research effort to know the correct set of parameters that allow the manufacture of correct parts. This paper presents a process analysis by means of the finite element model. The use case presented in this paper is that of a 3-m diameter pipe component with a thickness of 22 mm. In this type of application, poor selection of process conditions can result in parts that are out of tolerance, both in dimensions and shape. A 3D finite element model is made, and the symmetry of the tube section generated in 2D is analysed. As a novelty, an analysis of the process correction as a function of the symmetrical deformation of the material in this case in the form of a pipe is carried out. The results show a correct fitting of the model and give guidelines for manufacturing.