Hierarchical Magnetic Network Constructed by CoFe Nanoparticles Suspended Within “Tubes on Rods” Matrix Toward Enhanced Microwave Absorption

Hierarchical magnetic-dielectric composites are promising functional materials with prospective applications in microwave absorption(MA)field.Herein,a three-dimension hierarchical“nanotubes on microrods,”core–shell magnetic metal–carbon composite is rationally constructed for the first time via a fast metal–organic frameworksbased ligand exchange strategy followed by a carbonization treatment with melamine.Abundant magnetic CoFe nanoparticles are embedded within one-dimensional graphitized carbon/carbon nanotubes supported on micro-scale Mo2N rod(Mo2N@CoFe@C/CNT),constructing a special multi-dimension hierarchical MA material.Ligand exchange reaction is found to determine the formation of hierarchical magnetic-dielectric composite,which is assembled by dielectric Mo2N as core and spatially dispersed CoFe nanoparticles within C/CNTs as shell.Mo2N@CoFe@C/CNT composites exhibit superior MA performance with maximum reflection loss of−53.5 dB at 2 mm thickness and show a broad effective absorption bandwidth of 5.0 GHz.The Mo2N@CoFe@C/CNT composites hold the following advantages:(1)hierarchical core–shell structure offers plentiful of heterojunction interfaces and triggers interfacial polarization,(2)unique electronic migration/hop paths in the graphitized C/CNTs and Mo2N rod facilitate conductive loss,(3)highly dispersed magnetic CoFe nanoparticles within“tubes on rods”matrix build multi-scale magnetic coupling network and reinforce magnetic response capability,confirmed by the off-axis electron holography.

Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

In the non-linear microwave drying process,the incremental improved back-propagation (BP) neural network and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (the microwave power,the acting time and the rotational frequency) for microwave drying of selenium-rich slag.The optimum operating conditions obtained from the quadratic form of the RSM are:the microwave power of 14.97 kW,the acting time of 89.58 min,the rotational frequency of 10.94 Hz,and the temperature of 136.407 °C.The relative dehydration rate of 97.1895% is obtained.Under the optimum operating conditions,the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables.The verification experiments demonstrate the prediction accuracy of the network,and the mean squared error is 0.16.The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.

Optimization of Process Parameters of Continuous Microwave Drying Raspberry Puree Based on RSM and ANN-GA

To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexes(average temperature,average moisture content,average retention rate of the total anthocyanin content,temperature contrast value,and moisture dispersion value)were investigated via the response surface method(RSM)and the artificial neural network(ANN)with genetic algorithm(GA).The results showed that the microwave intensity and drying time dominated the changes of evaluation indexes.Overall,the ANN model was superior to the RSM model with better estimation ability,and higher drying uniformity and anthocyanin retention rate were achieved for the ANN-GA model compared with RSM.The optimal parameters were microwave intensity of 5.53 W•g^(-1),air velocity of 1.22 m·s^(-1),and drying time of 5.85 min.This study might provide guidance for process optimization of microwave drying berry fruits.

ANALYSIS OF MICROWAVE N-PORT NETWORK BY A MODIFIED BOUNDARY ELEMENT METHOD

A new approach based on resonance technique and modified boundary ele-ment method is presented to calculate the impedance parameter matrix of a microwaveN-port network of waveguide structure.A two port network is taken as a numerical ex-ample and the results show that the approach occupys the advantages of high accuracyand less computation effort.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

BP Neural Network Based on Microwave Method for Measuring the Moisture Content of Textile

The moisture content of yarn and fabric is an important factor in textiles industry.A novel microwave method used for material moisture content measurements is described in this paper.It can estimate the moisture content of the yarn roll with a standard deviation of 1.58% in the range of 0% to 90.00%.According to the actual size of the yarn,the yarn roll simulation model is established.The microwave attenuation variations arising from the changes in the conductivity and dielectric constant of the wet cone yarn from1.8 GHz to 5.0 GHz frequency are obtained by ultra-wideband antenna.The measured data are analyzed using the BP neural network.The result shows that it is a non-contact and online method to solve the moisture content of the yarn in the wide moisture content range.

Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles

In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.

3D Seed-Germination-Like MXene with In Situ Growing CNTs/Ni Heterojunction for Enhanced Microwave Absorption via Polarization and Magnetization

Ti_(3)C_(2)Tx MXene is widely regarded as a potential micro-wave absorber due to its dielectric multi-layered structure.However,missing magnetic loss capability of pure MXene leads to the unmatched electromagnetic parameters and unsatisfied impedance matching condi-tion.Herein,with the inspiration from dielectric-magnetic synergy,this obstruction is solved by fabricating magnetic CNTs/Ni hetero-structure decorated MXene substrate via a facile in situ induced growth method.Ni2+ions are successfully attached on the surface and interlamination of each MXene unit by intensive electrostatic adsorption.Benefiting from the possible“seed-germination”effect,the“seeds”Ni^(2+)grow into“buds”Ni nanoparticles and“stem”carbon nanotubes(CNTs)from the enlarged“soil”of MXene skeleton.Due to the improved impedance matching con-dition,the MXene-CNTs/Ni hybrid holds a superior microwave absorp-tion performance of−56.4 dB at only 2.4 mm thickness.Such a distinctive 3D architecture endows the hybrids:(i)a large-scale 3D magnetic coupling network in each dielectric unit that leading to the enhanced magnetic loss capability,(ii)a massive multi-heterojunction interface structure that resulting in the reinforced polarization loss capability,confirmed by the off-axis electron holography.These outstanding results provide novel ideas for developing magnetic MXene-based absorbers.

0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.

Advanced Synthesis Techniques for Microwave Filters

With the advent of the ‘digital revolution’ that has made possible services such as the world wide web, satellite broadcasting and mobile and trunk telephony, the finite RF spectrum allocated for terrestrial and satellite telecommunication systems is becoming increasingly crowded. This has impacted significantly upon the performance required from the microwave equipment that comprises these systems. In the case of microwave filters, greater in-band linearity to avoid signal distortion and out-of-band isolation to suppress interference are routinely specified, which can only be satisfied by advanced filtering characteristics. This article presents the coupling matrix approach to the synthesis of prototype filter networks, enabling the realization of the hardware embodying the enhanced performance needed by today’s high capacity systems.

Simulation and Experimental Method for Microwave Oven

The simulation software, HFSS （high frequency structure simulator）, is introduced in microwave oven design. In the cold test, a network analyzer is used to measure the reflection coefficient （S11） of the cavity under empty and loaded states over the frequency range from 2.448 GHz to 2.468 GHz. In the hot test, a piece of wet thermal paper and an infrared thermal imaging camera are used to measure the electric field distributions on the mica and turntable. In the cold test, the simulation agrees well with the experiment no matter in empty state or loaded state. In the hot test, the simulation agrees well with the experiment in general in empty state and approximately in loaded state. The little difference in both cold and hot test may be due to that the model in simulation is not absolutely identical with that in experiment or the inadequate precision of infrared thermal imaging camera.

Sea surface temperature retrieval based on simulated microwave polarimetric measurements of a one-dimensional synthetic aperture microwave radiometer

Compared with traditional real aperture microwave radiometers,one-dimensional synthetic aperture microwave radiometers have higher spatial resolution.In this paper,we proposed to retrieve sea surface temperature using a one-dimensional synthetic aperture microwave radiometer that operates at frequencies of 6.9 GHz,10.65 GHz,18.7 GHz and 23.8 GHz at multiple incidence angles.We used the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and a radiation transmission forward model to calculate the model brightness temperature.The brightness temperature measured by the spaceborne one-dimensional synthetic aperture microwave radiometer was simulated by adding Gaussian noise to the model brightness temperature.Then,a backpropagation(BP)neural network algorithm,a random forest(RF)algorithm and two multiple linear regression algorithms(RE1 and RE2)were developed to retrieve sea surface temperature from the measured brightness temperature within the incidence angle range of 0°-65°.The results show that the retrieval errors of the four algorithms increase with the increasing Gaussian noise.The BP achieves the lowest retrieval errors at all incidence angles.The retrieval error of the RE1 and RE2 decrease first and then increase with the incidence angle and the retrieval error of the RF is contrary to that of RE1 and RE2.

基于多模型神经网络的湿度廓线反演研究

为提升微波辐射计对大气廓线探测的精度,利用ARM大气观测站提供的地基微波辐射计、毫米波测云雷达以及探空数据,构建了两种添加不同云信息的反向传播神经网络(back propagation neural network,BPNN)模型(添加入云和出云高度的C-BPNN模型与添加雷达反射率因子的Z-BPNN模型)与一种未添加云信息的BPNN模型(记为BPNN0),并对反演结果进行了对比,结果表明:C-BPNN模型和Z-BPNN模型在任何天气下(有云或无云),得到的反演误差都小于BPNN0模型;C-BPNN相较于另外两种模型反演结果具有更高的稳定性。对3种模型各自反演结果最好的个例分析发现,C-BPNN与Z-BPNN模型主要的误差存在于高空无云但是相对湿度却出现跃变的情况,说明神经网络模型对初始权值与阈值较为敏感,因此通过遗传算法(genetic algorithms,GA)对BPNN模型进行优化。经GA优化后的反演结果表明:BPNN0模型与C-BPNN模型具有明显优化效果,而Z-BPNN模型优化效果则不明显。

基于有限时间ADP的微波加热高钛渣温度跟踪控制

针对常规控制方法对微波加热过程控制效果不够理想的问题,提出一种基于数据驱动模型的有限时间自适应动态规划微波加热温度跟踪算法。算法包含模型网络、评价网络和执行网络,这3个网络的实现依赖于神经网络。模型网络实现微波加热过程的数据驱动建模,评价网络和执行网络实现最优性能指标函数和控制功率的逼近。最后将温度跟踪转化为误差的镇定。通过理论推导证明了算法的收敛性及最优性,并进一步开展了微波加热高钛渣温度跟踪实验和仿真研究。结果表明,算法能有效地跟踪高钛渣的加热过程,基于ELMAN神经网络的模型预测误差小于1℃,温度跟踪误差小于0.2℃,在工业微波加热中具有潜在的应用价值。

基于幅度和相位融合的微波两相流测量系统设计

针对油井开采过程中需要对各相含量进行精确预测以调整开采策略的实际问题,设计了一种基于微波法的油水两相流检测系统。该系统利用微波在不同介质中透射能力的差异,构建了一套包含微波信号源、功率放大器、功率分配器、检波器和STM32F103ZET6核心板的硬件电路系统,通过编写AD采集和串口通信的软件代码,来接收检波器端幅度和相位数据。在数据处理方面,分别对幅度数据、相位数据和幅度-相位融合数据采用BP神经网络的方法预测含水率。实验表明:在使用融合数据时,预测准确度可以提高至96.33%,取得了较理想的效果。

无模型自适应滑模控制的微波加热过程温度控制

微波加热模型具有无限维、非线性和时变等特点,导致控制器难于设计和实现。针对此问题,提出了一种适用于微波加热过程的无模型自适应滑模控制方法。首先,对微波加热过程传热数学模型进行分析,建立了微波加热过程输入功率与温度之间的全格式动态线性化数据模型。然后,根据该数据模型设计了无模型自适应滑模控制器,并给出了数据模型中相关未知时变参数和未知干扰的估计算法。最后,利用COMSOL和MATLAB进行仿真,仿真结果验证了所提控制方法的有效性。

基于反向传播神经网络PID的高功率微波炉温度控制

针对现有10 kW高功率工业微波炉,采用继电器作为控制执行器,在使用传统控制方法加热时,温度存在较大超调和明显振荡,系统温度稳定性较低,为解决上述问题将反向传播神经网络PID(BPNNPID)控制引入到该装置微波加热温度控制中,并以自来水为加热对象进行仿真对比与实验验证。首先,利用现有输入输出实验数据,建立工业微波炉温度控制模型;其次,运用MATLAB/SIMULINK搭建高功率工业微波炉温度控制系统并进行仿真对比实验;最后,实验验证BPNNPID控制方法在加热5 kg自来水时工业微波炉的温度控制性能,实验结果表明,较常规PID、模糊PID控制,该方法在微波加热过程中对媒质温度控制超调更小且未发生明显温度振荡,有效改善了高功率工业微波炉工作时的系统温度稳定性,有助于提高产品质量和安全性能。

应用微波网络分析的界面刚度超声检测研究

界面刚度是表征装配结构性能的重要参数,作为常见的无损检测方式,超声检测被广泛应用于界面刚度检测,但现有界面刚度超声检测仍存在一定不足。对现有实验方法进行改良,在传统超声检测理论基础上,将微波网络分析从电磁波领域迁移到超声波领域,将“场”理论与“路”理论相结合,通过检测微波网络分析仪S参数表征界面刚度,并与软件有限元仿真和传统超声检测实验结果进行对比,对界面刚度进行检测与评价。研究显示,相比于传统超声检测,使用微波网络S参数表征接触界面刚度时,检测方法简化实验流程,且检测结果更符合实际接触情况。微波网络分析与超声检测相结合的检测方法在重大装备关键装配界面接触情况测试中具有重要应用前景。

多层异构复合材料的制备及吸波性能

通过模板法与物理混合法相结合,成功制备了三维网络结构的BaTiO_(3)(BTO)+Fe_(3)O_(4)三聚氰胺泡沫异质结构复合材料,采用扫描电镜和X射线衍射对复合材料样品的表面形貌和晶体结构进行了表征,并使用矢量网络分析仪测试了该样品在2~18 GHz频率范围内的复介电常数和复磁导率,并根据测量数据计算了反射损耗值。随后,使用COMSOL多物理场仿真软件进行有限元分析,研究了该复合体系的吸收机制和吸波性能。结果表明:成功引入了BTO和Fe_(3)O_(4)形成的大量异质界面到三维网络结构碳中,构建了异质结构。BTO和Fe_(3)O_(4)优化了三维网络结构的阻抗匹配,从而显著提高了多层异构复合材料的有效吸收带宽。此外,BTO、Fe_(3)O_(4)和三维网络碳结构构筑成了高气孔率的三维网络,导致电磁波在材料内部发生多次反射和散射,增强了电磁波与材料之间的相互作用,实现了对电磁波的吸收,提升了吸波效率。该复合材料展示了出色的吸波性能,当复合材料的厚度为2.9 mm时,最强的反射损耗值达到−54.76 dB,而当复合材料的厚度为2.7 mm时,有效吸收带宽提升至7.92 GHz。

基于神经网络的超材料窗片反向设计研究

与固态器件相比,真空电子器件在高频段可以提供更大的功率、更宽的带宽。输能窗作为其中的重要部件,用以隔绝真空电子器件内外环境,保持管内的高真空度,传输高能电磁波。目前,传统输能窗的性能已经制约宽带高频真空器件性能。基于此,本文提出了一种利用神经网络算法设计超材料窗片的方案,实现了低反射和驻波比的宽带输能窗。本文利用神经网络的非线性拟合效应对窗片结构与其频谱之间建立映射关系,从而避免了求解麦克斯韦方程组的繁复过程,提高了频谱性能仿真的速度,简化了设计过程。通过设计双向神经网络并进行训练,实现反向设计窗片的目标,输入目标频谱特性参数即可生成满足指标要求的结构参数。