1,721,334 research outputs found
Future Trends in Optimal Design in Electromagnetics
No full textIn computational electromagnetics, there are manyfold advantages when using machine learning methods because no mathematical formulation is required to solve the direct problem for given input geometry. Moreover, due to the inherent bidirectionality of a convolutional neural network, it can be trained to identify the geometry giving rise to the prescribed output field. All this puts the ground for neural meta-modeling of fields, despite different levels of cost and accuracy. For the sake of an example, a surrogate model of the field in a small device is shown. In particular, a concept of multi-fidelity model makes it possible to control both prediction accuracy and computational cost. Moreover, TEAM Problem 35 is solved and it is shown how a generative adversarial network can help multiobjective optimal design
The Electrification of Italian Railway Network Across the Photographic Archive of TIBB
No full textTransition from steam to electric power, and the search for the best of several possible solutions, was a challenge for the countries that undertook it. What made this transition possible was the commitment of the engineers of the time, the companies they worked for and the governments who believed, although among many difficulties, in this idea. An important photographic archive masterfully illustrates this evolution
Deformable MEMS with Fringing Field: Models, Uniqueness Conditions and Membrane Profile Recovering
No full textA particular 1D II-order differential semi-linear elliptic model for electrostatic membrane MEMS devices, which is well-known in the literature, considers the amplitude of the electric field locally proportional to the membrane’s geometric curvature, which contains a term involving the fringing field according to Pelesko and Driscoll’s theory. Thus, in this paper, we will begin from this elliptical model, of which the uniqueness condition for the solution does not depend on the electromechanical properties of the membrane’s constituent material. In particular, after analyzing the model’s advantages and disadvantages, we present a new uniqueness condition for the solution depending on the properties listed above, which appears to be more important than the existence condition of the solution that is well-known in literature. Therefore, once the fringing field’s mode of action on the electrostatic force acting on the membrane is evaluated, suitable numerical techniques are used and compared to recover the membrane profile without ghost solutions and to propose an innovative criterion for selecting the membrane material, which depends on the electrical operative parameters and vice-versa. Finally, the possible industrial uses of the studied device are evaluated
Linear antenna array modeling with deep neural networks
No full textIn modern wireless telecommunication systems, antenna arrays are widely used as elements of multiple – input multiple – output technology. In the fifth-generation systems, arrays are utilized to realize beamforming that forms the radiation pattern of the base station in the direction of the mobile user. This requires the utilization of many-element antenna arrays that are precisely controlled to achieve the required radiation properties. In this paper we apply the concept of deep neural network to model antenna array radiation properties. In this proof-of-concept research we aim at investigating to what extent it is possible to use deep neural networks for modeling antenna arrays. We consider a full-wave model of linear array with a reflector, which was controlled by the phase and amplitude of the signals feeding the elementary radiators. The applied method made it possible to solve the direct and inverse problems. The results that we obtained show that deep neural networks are able to model antenna array properties
Physics-informed Neural Networks for the Resolution of Analysis Problems in Electromagnetics
No full textLearning from examples is the golden rule in the construction of behavioral models using neural networks (NN). When NN are trained to simulate physical equations, the tight enforcement of such laws is not guaranteed by the training process. In addition, there can be situations in which providing enough examples for a reliable training can be difficult, if not impossible. To alleviate these drawbacks of NN, recently a class of NN incorporating physical behavior has been proposed. Such NN are called “physics-informed neural networks” (PINN). In this contribution, their application to direct electromagnetic (EM) problems will be presented, and a formulation able to minimize an integral error will be introduced
Magnetic Field Synthesis in the Design of Inductors for Magnetic Fluid Hyperthermia
No full textA magnetic fluid hyperthermia (MFH) inductor design using multiobjective evolution strategy techniques is proposed. Uniformity of the magnetic field and solution sensitivity are the objective functions chosen for the selection of the inductor geometry. After a 3-D finite-element analysis (FEA) of the thermal field, the coupled-field response of the synthesized inductor has been assessed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Hierarchical paradigm for automated optimal design of dual-band wearable antenna with simplified human body models
No full textCurvature Dependent Electrostatic Field in the Deformable MEMS Device: Stability and Optimal Control
No full textThe recovery of the membrane profile of an electrostatic micro-electro-mechanical system (MEMS) device is an important issue because, when applying an external voltage, the membrane deforms with the consequent risk of touching the upper plate of the device (a condition that should be avoided). Then, during the deformation of the membrane, it is useful to know if this movement admits stable equilibrium configurations. In such a context, our present work analyze the behavior of an electrostatic 1D membrane MEMS device when an external electric voltage is applied. In particular, starting from a well-known second-order elliptical semi-linear di erential model, obtained considering the electrostatic field inside the device proportional to the curvature of the membrane, the only possible equilibrium position is obtained, and its stability is analyzed. Moreover, considering that the membrane has an inertia in moving and taking into account that it must not touch the upper plate of the device, the range of possible values of the applied external voltage is obtained, which accounted for these two particular operating conditions. Finally, some calculations about the variation of potential energy have identified optimal control conditions
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