A Neuro-Fuzzy Network for the Design of Circular and Triangular Equilateral Microstrip Antennas

A fast method for the design of circular and equilateral triangular microstrip antennas, based on a Neuro-Fuzzy Network, is presented. Numerical results obtained by using this technique agree quite well with the Moment Method results. The proposed technique can be fruitfully used in microwave and millimeter wave CAD applications.     

Annular Ring Microstrip Antennas for Millimeter Wave Applications

This work presents a fullwave analysis for annular ring microstrip antennas on uniaxial anisotropic substrates. The effect of the anisotropic substrates on the antenna performance at the millimeter wave band is investigated. The analysis is performed in the Hankel transform domain using Hertz vector potentials and Galerkin Method. Numerical results are presented for the antenna main characteristics such as resonant frequency and radiation pattern.     

Artificial Neural Networks for Resonant Frequency Calculation of Rectangular Microstrip Antennas with Thin and Thick Substrates

Neural models based on multilayered perceptrons for computing the resonant frequency of rectangular microstrip antennas with thin and thick substrates are presented. Eleven learning algorithms, Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, conjugate gradient of Powell-Beale, bayesian regularization, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, resilient backpropagation, conjugate of Polak-Ribiére, backpropagation with adaptive learning rate, one-step secant, and backpropagation with momentum, are used to train the multilayered perceptrons. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by Levenberg-Marquardt algorithm.     

Optimization of Circular Ring Microstrip Antenna Using Genetic Algorithm

Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity model analysis along with the genetic optimization algorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimization algorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.     

The Method of Angular Line to Calculate Eigenvalues for Microstrip Antennas

The method of angular line (MAL) is presented for calculating the resonant frequency of microstrip antenna, which is available in other literature and originally used to analyze various waveguide structure. The results obtained by using this method are in conformity with those reported elsewhere.     

Support Vector Driven Genetic Algorithm for the Design of Circular Polarized Microstrip Antenna

In this paper, a hybrid soft computing method for designing specific microstrip antenna is presented. Evolutionary algorithm such as genetic algorithm (GA) is one of the promising ways of finding global optimum solution from a multivariate nonlinear feature space. Being a stochastic iterative algorithm, it requires much computation power when the function to be optimized is complex and time consuming. Various meta-modelling techniques such as neural network, response surface methods, kriging, etc. can be used to model the process under optimization in order to reduce the computational expenses. In this paper, we investigate one such technique – support vector regression (SVR) – to model the complex analytical process. The model, thus obtained, is used for optimization using genetic algorithms. This approach is demonstrated for the design of circular polarized microstrip antenna at 2.6 GHz band. The results of SVR model are compared with other meta-models generated with neural network and response surface methodology.     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

基于自适应神经模糊推理系统的光谱预测

由于神经网络具有自适应以及自学习功能,模糊系统具有很好的非线性推理能力。将二者相结合的自适应神经模糊推理系统吸收了二者的优点,将其应用于对模型特征的分析和建模上,通过对数据的学习可以预测特性非常复杂的系统,如荧光光谱。荧光光谱分析法操作简单,精度高,分析速度快,是研究分子内部结构的重要手段。预测荧光光谱更具有重要意义。文章以N₂分子的脉冲放电的发射谱为例进行了预测,预测结果显示,该方法可以预测光谱谱线的重要信息,误差均小于1.66%,达到了此次实验的精度要求,具有满意的效果。对光谱的预测是切实可行的。     

Calculation of Magnetic Configuration and Resistance for Nanomagnets

Based on a classical Heisenberg lattice model with dipole interaction and the method of spin dynamic simulation,the magnetic configurations (MC),hysteresis loop (HL) and magnetic resistance (MR) of the nanomagnets with different geometries,such as circle,square and rectangle,are studied for different directions of applied field.In the case of perpendicular field to the plane,the magnetization and MR are reversible and have not hysteresis.When the field is applied in the plane,the HL is irreversible and is qualitatively well agreeable with the current experimental results.The MR loop is also irreversible and appears two peaks distributed at two sides around zero field.The peaks of magnetic resistance are relative to the vortex state of similar configuration.Large easy-axis anisotropy will suppress the MC anisotropy,and the large magnetoresistance effect disappears.     

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.     

面向燃料电池双极板的不锈钢表相掺杂耐蚀性高通量计算与分析

通过基于密度泛函理论(DFT)的第一性原理高通量计算,筛选了可以提高不锈钢双极板钝化层在PEMFCs工作环境中的耐蚀性的掺杂元素.基于Cr₂O₃的■切面构建计算模型,对其进行了48种元素的掺杂替换,考虑228种掺杂构型．根据掺杂结构和位移能量、掺杂元素被分为4类,并采用不同的吸附、空位计算方案．根据不同掺杂类型采用不同的吸附、空位计算方案,计算了每种掺杂元素对功函数、F的吸附能和Cr/O的空位形成能的影响.计算结果显示,C、N、In、Ru对于不锈钢钝化层的各项耐蚀性参数提升效果较好;Au、Rh、Pt、Ir、Co、Ni也能较好地提升耐蚀性,但是存在成本高或者危害质子膜的问题;Ag、Cu、Sn、Ge可以抑制空位形成和F吸附,但会造成功函数下降;Zn可以提高功函数并抑制F吸附.此外,对于电子态密度的计算表明,掺杂原子与Cr原子形成的稳定键合是耐蚀性参数提升的重要因素.     

太阳能光伏-热伏发电板的自适应追光系统设计与实现

在小型太阳能光伏-热伏发电系统中,为了提高其发电效率,通常会辅以自动追光系统。针对现有的基于多光电二极管的自动追光系统结构复杂等问题,提出了一种基于光敏电阻的自适应追光系统。为了实现发电板对光源的追踪,采用了基于负反馈的自适应追光原理,通过软件设计控制追光精度,同时增加了系统位置消抖控制。在硬件方面,采用光敏电阻感应光强,借助AD转换电路和舵机,以驱动太阳能光伏-热伏发电板转动直至发电板平面垂直于太阳光;优化设计了光敏电阻与板面的夹角大小以调整追光的区域及误差。最后完成了整体装置的机械结构设计和电路图设计及仿真,结果表明,与现有的多光电二极管追光系统相比,所设计的追光系统机械装置部分的设计更加简单,便于实现,同时通过优化设计改善了追光的稳定性。