Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning

For the optimal design of electromagnetic devices, it is the most time consuming to obtain the training samples from full wave electromagnetic simulation software, including HFSS, CST, and IE3D. Traditional machine learning methods usually use only labeled samples or unlabeled samples, but in practical problems, labeled samples and unlabeled samples coexist, and the acquisition cost of labeled samples is relatively high. This paper proposes a semisupervised learning Gaussian Process (GP), which combines unlabeled samples to improve the accuracy of the GP model and reduce the number of labeled training samples required. The proposed GP model consists two parts: initial training and self-training. In the process of initial training, a small number of labeled samples obtained by full wave electromagnetic simulation are used for training the initial GP model. Afterwards, the trained GP model is copied to another GP model in the process of self-training, and then the two GP models will update after crosstraining with different unlabeled samples. Using the same test samples for testing and updating, a model with a smaller error will replace another. Repeat the self-training process until a predefined stopping criterion is met. Four different benchmark functions and resonant frequency modeling problems of three different microstrip antennas are used to evaluate the effectiveness of the GP model. The results show that the proposed GP model has a good fitting effectiveness on benchmark functions. For microstrip antennas resonant frequency modeling problems, in the case of using the same labeled samples, its predictive ability is better than that of the traditional supervised GP model.

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Simulation on Probe-Fed Rectangular Patch Antenna with the Minkowski Fractal Structure

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.1148 ◽

2014 ◽

Vol 971-973 ◽

pp. 1148-1151

Author(s):

Ruo Yan Han ◽

Wei He ◽

Ping Lu ◽

He Ling Cai ◽

Jiao Hong

Keyword(s):

Resonant Frequency ◽

Patch Antenna ◽

High Frequency ◽

Fractal Structure ◽

Simulation Software ◽

Electromagnetic Simulation ◽

Rectangular Patch ◽

Simulation Results ◽

Application Requirements ◽

Minkowski Fractal

The probe-fed rectangular patch antenna with the Minkowski fractal structure which for 2.45GHz was simulated by using high frequency electromagnetic simulation software HFSS V11.The Simulation results showed that it was similar to side-fed square patch antenna with the Minkowski fractal structure. The resonant frequency decreased with fractal iteration, and the size of the antenna could be miniaturized. And antenna pattern unchanged with fractal iteration mostly, which meet the application requirements basically.

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Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble

Applied Computational Electromagnetics Society ◽

10.47037/2020.aces.j.351205 ◽

2021 ◽

Vol 35 (12) ◽

pp. 1485-1492

Author(s):

Tianliang Zhang ◽

Yubo Tian ◽

Xuezhi Chen ◽

Jing Gao

Keyword(s):

Resonant Frequency ◽

Gaussian Process ◽

Surrogate Model ◽

Coplanar Waveguide ◽

Optimization Methods ◽

Adaboost Algorithm ◽

Full Wave ◽

Modeling Experiment ◽

Global Optimization Methods ◽

Rectangular Microstrip Antenna

The design of electromagnetic components generally relies on simulation of full-wave electromagnetic field software exploiting global optimization methods. The main problem of the method is time consuming. Aiming at solving the problem, this study proposes a regression surrogate model based on AdaBoost Gaussian process (GP) ensemble (AGPE). In this method, the GP is used as the weak model, and the AdaBoost algorithm is introduced as the ensemble framework to integrate the weak models, and the strong learner will eventually be used as a surrogate model. Numerical simulation experiment is used to verify the effectiveness of the model, the mean relative error (MRE) of the three classical benchmark functions decreases, respectively, from 0.0585, 0.0528, 0.0241 to 0.0143, 0.0265, 0.0116, and then the method is used to model the resonance frequency of rectangular microstrip antenna (MSA) and coplanar waveguide butterfly MSA. The MRE of test samples based on the APGE are 0.0069, 0.0008 respectively, and the MRE of a single GP are 0.0191, 0.0023 respectively. The results show that, compared with a single GP regression model, the proposed AGPE method works better. In addition, in the modeling experiment of resonant frequency of rectangular MSA, the results obtained by AGPE are compared with those obtained by using neural network (NN). The results show that the proposed method is more effective.

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Efficient Resonant Frequency Modeling for Dual-Band Microstrip Antennas by Gaussian Process Regression

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2014.2362937 ◽

2015 ◽

Vol 14 ◽

pp. 337-341 ◽

Cited By ~ 20

Author(s):

J. P. Jacobs

Keyword(s):

Resonant Frequency ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Microstrip Antennas ◽

Dual Band

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On Differential Imaging Using Electromagnetic Simulation for Vehicular Antenna Signature Analysis

Sensors ◽

10.3390/s21113796 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3796

Author(s):

Jose Antonio Solano-Perez ◽

María-Teresa Martínez-Inglés ◽

Jose-Maria Molina-Garcia-Pardo ◽

Jordi Romeu ◽

Lluis Jofre-Roca ◽

...

Keyword(s):

Current Trend ◽

Surface Current ◽

Simulation Software ◽

Electromagnetic Simulation ◽

The Novel ◽

Full Wave ◽

Vehicle Structure ◽

Novel Concept ◽

Differential Imaging ◽

Vehicle Platform

The current trend in vehicles is to integrate a wide number of antennae and sensors operating at a variety of frequencies for sensing and communications. The integration of these antennae and sensors in the vehicle platform is complex because of the way in which the antenna radiation patterns interact with the vehicle structure and other antennae/sensors. Consequently, there is a need to study the radiation pattern of each antenna or, alternatively, the currents induced on the surface of the vehicle to optimize the integration of multiple antennae. The novel concept of differential imaging represents one method by which it is possible to obtain the surface current distribution without introducing any perturbing probe. The aim of this study was to develop and confirm the assumptions that underpin differential imaging by means of full-wave electromagnetic simulation, thereby providing additional verification of the concept. The simulation environment and parameters were selected to replicate the conditions in which real measurements were taken in previous studies. The simulations were performed using Ansys HFSS simulation software. The results confirm that the approximations are valid, and the differential currents are representative of the induced surface currents generated by a monopole positioned on the top of a vehicle.

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A novel and simple expression to accurately calculate the resonant frequency of annular-ring microstrip antennas

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078714000890 ◽

2014 ◽

Vol 7 (6) ◽

pp. 727-733 ◽

Cited By ~ 3

Author(s):

Abdurrahim Toktas ◽

Mustafa B. Bicer ◽

Ahmet Kayabasi ◽

Deniz Ustun ◽

Ali Akdagli ◽

...

Keyword(s):

Resonant Frequency ◽

Microstrip Antennas ◽

Simple Expression ◽

Simulation Software ◽

Effective Radius ◽

Dielectric Constants ◽

Method Of Moment ◽

Annular Ring ◽

Frequency Calculation ◽

Mode Tm

This paper proposes a novel and simple expression for effective radius of annular-ring microstrip antennas (ARMAs) obtained using a recently emerged optimization algorithm of artificial bee colony (ABC) in calculating the resonant frequency at dominant mode (TM11). A total of 80 ARMAs having different parameters related to antenna dimensions and dielectric constants was simulated in terms of the resonant frequency with the help of an electromagnetic simulation software called IE3D™ based on method of moment. The effective radius expression was constructed and the unknown coefficients belonging to the expression were then optimally determined with the use of ABC algorithm. The proposed expression was verified through comparisons with the methods of resonant frequency calculation reported elsewhere. Also, it was further validated on an ARMA fabricated in this study. The superiority of the presented approach over the other methods proposed in the literature is that it does not need any sophisticated computations while achieving the most accurate results in the resonant frequency calculation of ARMAs.

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Antenna Optimization Design Based on Deep Gaussian Process Model

International Journal of Antennas and Propagation ◽

10.1155/2020/2154928 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xin-Yu Zhang ◽

Yu-Bo Tian ◽

Xie Zheng

Keyword(s):

Gaussian Process ◽

Process Model ◽

Optimization Design ◽

Optimization Method ◽

Simulation Software ◽

Modeling Method ◽

Electromagnetic Problems ◽

Training Samples ◽

Low Efficiency ◽

Time Required

When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.

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Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification

Remote Sensing ◽

10.3390/rs11192288 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2288 ◽

Cited By ~ 1

Author(s):

Xin Song ◽

Xinwei Jiang ◽

Junbin Gao ◽

Zhihua Cai

Keyword(s):

Discriminant Analysis ◽

Gaussian Process ◽

State Of The Art ◽

Hyperspectral Images ◽

Superior Performance ◽

Model Parameters ◽

Optimal Model ◽

Training Samples ◽

Classification Tasks ◽

Gp Model

Dimensionality Reduction (DR) models are highly useful for tackling Hyperspectral Images (HSIs) classification tasks. They mainly address two issues: the curse of dimensionality with respect to spectral features, and the limited number of labeled training samples. Among these DR techniques, the Graph-Embedding Discriminant Analysis (GEDA) framework has demonstrated its effectiveness for HSIs feature extraction. However, most of the existing GEDA-based DR methods largely rely on manually tuning the parameters so as to obtain the optimal model, which proves to be troublesome and inefficient. Motivated by the nonparametric Gaussian Process (GP) model, we propose a novel supervised DR algorithm, namely Gaussian Process Graph-based Discriminate Analysis (GPGDA). Our algorithm takes full advantage of the covariance matrix in GP to constructing the graph similarity matrix in GEDA framework. In this way, more superior performance can be provided with the model parameters tuned automatically. Experiments on three real HSIs datasets demonstrate that the proposed GPGDA outperforms some classic and state-of-the-art DR methods.

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A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

Applied Sciences ◽

10.3390/app11041492 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1492

Author(s):

Hanita Daud ◽

Muhammad Naeim Mohd Aris ◽

Khairul Arifin Mohd Noh ◽

Sarat Chandra Dass

Keyword(s):

Gaussian Process ◽

Inverse Modeling ◽

Mean Squared Error ◽

Computational Effort ◽

Percentage Error ◽

Depth Prediction ◽

Receiver System ◽

Realistic Representation ◽

And Performance ◽

Gp Model

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

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