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...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.展开更多
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 ind...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 ℃. 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.展开更多
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 indexe...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.展开更多
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 rheol...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.展开更多
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 take...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.展开更多
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 cont...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.展开更多
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 in...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.展开更多
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 par...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.展开更多
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 networ...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%.展开更多
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 sat...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.展开更多
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 un...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.展开更多
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...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.展开更多
针对偏远油气田现场没有电信网络覆盖,导致现场数据无法采集回传,作业现场管理存在盲区,依赖移动通信网络进行的过程控制系统PCS(Process Control System)巡检作业无法使用等问题,提出了不同生产场景下利用通信新技术解决偏远油气田现...针对偏远油气田现场没有电信网络覆盖,导致现场数据无法采集回传,作业现场管理存在盲区,依赖移动通信网络进行的过程控制系统PCS(Process Control System)巡检作业无法使用等问题,提出了不同生产场景下利用通信新技术解决偏远油气田现场数据传输的方法,包括电力线载波、无线微波、NB-IOT、LORA、北斗卫星、高通量卫星等传输方式,结合生产现场实际情况,对每种技术的适用场景、成本效益以及可能的局限性进行了综合评估,得出了在不同应用场景下最适合的通信技术方案。结果表明,采用通信新技术可以快速解决不同偏远生产现场的传输难题,降低传输成本,为企业数字化发展助力。展开更多
基金This work was supported by the Ministry of Science and Technology of China(973 Project No.2018YFA0209102)the National Natural Science Foundation of China(11727807,51725101,51672050,61790581).
文摘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.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘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 ℃. 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.
基金Supported by the National Natural Science Foundation of China(32072352)。
文摘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.
基金the sponsor CSIR (Council of Scientific and Industrial Research), New Delhi for their financial grant to carry out the present research work
文摘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.
文摘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.
基金The Science&Technology Innovation Action Plan of International Science and Technology Cooperation Projects from SSTEC(No.14510711600)
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(51725101,11727807,51672050,61790581)the Ministry of Science and Technology of China(2018YFA0209102)。
文摘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.
基金National Key Research and Development Program of China(2017YFC1501704,2016YFA0600703)Projects of International Cooperation and Exchanges NSFC(NSFC-RCUK_STFC)(61661136005)+2 种基金Major State Basic Research Development Program of China(973 Program)(2013CB430101)Six Talent Peaks Project in Jiangsu Province(2015-JY-013)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center,China Meteorological Administration
文摘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%.
文摘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.
基金supported by the National Natural Science Foundation of China under Grant No.10775029Vacuum Electronics National Laboratory Foundation under Grant No. NKLC001-063Postdoctoral Foundation under Grant No.20070411149
文摘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.
基金The National Natural Science Foundation of China under contract Nos 41475019 and 41705007.
文摘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.
文摘针对偏远油气田现场没有电信网络覆盖,导致现场数据无法采集回传,作业现场管理存在盲区,依赖移动通信网络进行的过程控制系统PCS(Process Control System)巡检作业无法使用等问题,提出了不同生产场景下利用通信新技术解决偏远油气田现场数据传输的方法,包括电力线载波、无线微波、NB-IOT、LORA、北斗卫星、高通量卫星等传输方式,结合生产现场实际情况,对每种技术的适用场景、成本效益以及可能的局限性进行了综合评估,得出了在不同应用场景下最适合的通信技术方案。结果表明,采用通信新技术可以快速解决不同偏远生产现场的传输难题,降低传输成本,为企业数字化发展助力。