Showing papers in "International Journal of Numerical Modelling-electronic Networks Devices and Fields in 2020"
TL;DR: A comparative study on the application of the ANNs for modeling the scattering parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions is presented.
62 citations
40 citations
37 citations
TL;DR: An overview of recent advances in neural network‐based inverse modeling techniques for microwave applications and the recently proposed activation function and three‐stage deep learning algorithm for training the hybrid deep neural network are reviewed.
31 citations
TL;DR: The proposed M2LP model is a fast, accurate, and reliable regression model for design optimization of microwave antennas that had been used in a design optimization process and the obtained optimal antenna had been prototyped using 3D printing technology for justification and results.
30 citations
27 citations
24 citations
TL;DR: This paper presents a novel, unique‐integrated compact, smart IoT module that aims at reducing costs associated with commercial data loggers and sensing modules, requiring control and data storage that need proprietary software.
24 citations
22 citations
21 citations
TL;DR: The greedy routing scheme in the Floodlight controller is proposed and results confirm the effectiveness of the proposed scheme in comparison with other baseline routing protocols such as random, shortest path (SP), and adaptive flow routing (AFR).
TL;DR: It can be concluded that the proposed models have sufficient accuracy that can be used in a computationally efficient design optimization process of a large‐scale RA design.
TL;DR: This work investigates how different conditions affect the robustness of the derived surrogate models, that is, how much the approximation accuracy varies given different experimental designs, and proposes two criteria for a good accuracy-robustness trade-off.
Abstract: We present an algorithm for computing sparse, least squares-based polynomial chaos expansions, incorporating both adaptive polynomial bases and sequential experimental designs. The algorithm is employed to approximate stochastic high-frequency electromagnetic models in a black-box way, in particular, given only a dataset of random parameter realizations and the corresponding observations regarding a quantity of interest, typically a scattering parameter. The construction of the polynomial basis is based on a greedy, adaptive, sensitivity-related method. The sequential expansion of the experimental design employs different optimality criteria, with respect to the algebraic form of the least squares problem. We investigate how different conditions affect the robustness of the derived surrogate models, that is, how much the approximation accuracy varies given different experimental designs. It is found that relatively optimistic criteria perform on average better than stricter ones, yielding superior approximation accuracies for equal dataset sizes. However, the results of strict criteria are significantly more robust, as reduced variations regarding the approximation accuracy are obtained, over a range of experimental designs. Two criteria are proposed for a good accuracy-robustness trade-off.
TL;DR: This paper proposes the hybrid approach by combining the SFLA algorithm with a pattern search algorithm, which improves the original technique named as the hybrid shuffled frog‐leaping and pattern search algorithms (hSFLA‐PS), which superiority of the proposed hybrid algorithm over the original S FLA in terms of implementation time and solution quality is compared.
TL;DR: Simulation results demonstrate that compared with other methods, the proposed PSOGP has better overall performance in joint sidelobe suppression and nulls control of large‐scale linear antenna arrays.
TL;DR: This work proposes the employment of a recently introduced concept of constrained modelling, where the surrogate domain is confined only to contain the essential subsets of the parameter space, and demonstrated using a miniaturized microstrip rat‐race coupler with its yield optimized at the cost of just a few dozen of EM simulations of the circuit.
TL;DR: An overview of recent advances in parametric modeling of microwave components using combined neural network and transfer function (neuro‐TF) and the sensitivity analysis‐based neuro‐TF modeling technique is provided.
Abstract: Parametric modeling of electromagnetic (EM) behaviors has become important for EM design optimizations of microwave components. This paper provides an overview of recent advances in parametric modeling of microwave components using combined neural network and transfer function (neuro-TF). Transfer functions are used to represent the EM responses of passive components vs frequency. With the help of the transfer function, the nonlinearity of the neural network structure can be significantly decreased. We first introduce the neuro-TF modeling approach in rational format. We also review the pole-residue-based neuro-TF modeling technique. The orders of the pole-residue transfer functions may vary over different regions of geometrical parameters. A pole-residue tracking technique can be used to solve this order-changing problem. As a further advancement, we discuss the sensitivity analysis-based neuro-TF modeling technique. The purpose is to increase the model accuracy by utilizing EM sensitivity information and to speed up the model development process by reducing the number of training data required for developing the model. After the modeling process, the trained model can be used to provide accurate and fast prediction of the EM responses w.r.t. the geometrical variables and can be subsequently used in the high-level circuit and system design.
TL;DR: A multipath routing algorithm based on the optimization approach that is designed by considering the several factors, such as energy, QoS and trust, and has better performance than current multipath routes.
TL;DR: The upgraded optimization technique is the joint execution of moth flame optimization with the integration of local random search (LRS) called MFLRS and random decision forest (RDF), and hence it is named as M FLRS‐RDF technique.
TL;DR: The structural design of a reflectarray antenna with nonuniform reflector height operating in X band has been fabricated for the experimental measurement of reflectarray performance using 3D printer technology.
TL;DR: A hybrid model of wind speed forecasting is developed that is compared with the conventional recurrent neural network (RNN) prediction structure and the obtained results provide the effectiveness of the proposed model in terms of mean absolute error and the rate of convergence parameter.