Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
The aim of this paper is to simulate and study the early moments of the reactive ballistics of a large caliber projectile fired from a gun,combining 0D and 2D axisymmetric Computational Fluid Dynamics(CFD)approaches.F...The aim of this paper is to simulate and study the early moments of the reactive ballistics of a large caliber projectile fired from a gun,combining 0D and 2D axisymmetric Computational Fluid Dynamics(CFD)approaches.First,the methodology is introduced with the development of an interior ballistics(IB)lumped parameter code(LPC),integrating an original image processing method for calculating the specific regression of propellant grains that compose the gun propellant.The ONERA CFD code CEDRE,equipped with a Dynamic Mesh Technique(DMT),is then used in conjunction with the developed LPC to build a dedicated methodology to calculate IB.First results obtained on the AGARD gun and 40 mm gun test cases are in a good agreement with the existing literature.CEDRE is also used to calculate inter-mediate ballistics(first milliseconds of free flight of the projectile)with a multispecies and reactive approach either starting from the gun muzzle plane or directly following IB.In the latter case,an inverse problem involving a Latin hypercube sampling method is used to find a gun propellant configuration that allows the projectile to reach a given exit velocity and base pressure when IB ends.The methodology developed in this work makes it possible to study the flame front of the intermediate flash and depressurization that occurs in a base bleed(BB)channel at the gun muzzle.Average pressure variations in the BB channel during depressurization are in good agreement with literature.展开更多
In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes fu...In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.展开更多
Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characterist...Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.展开更多
This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncerta...This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.展开更多
In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to...In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.展开更多
To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on th...To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.展开更多
基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project...基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project)数值模拟试验,探讨了植被水力方案的引入对中国夏季降水模拟的影响。结果表明,植被水力方案的引入能够显著降低CAS-ESM2.0模式对中国夏季降水气候态的模拟偏差,特别是显著改进了中国东部、青藏高原降水的低估,青藏高原以东的川西地区降水高估的偏差,同时也改善了夏季降水年际变率和极端大雨日数的模拟性能。进一步分析显示,植被水力方案的改进显著减小了土壤湿度在长江流域偏干、青藏高原偏湿的模式模拟偏差,降低了我国中东部以及青藏高原地表感热通量和潜热通量的模拟偏差,改善了模式对陆气相互作用过程的模拟能力。陆气相互作用的改进显著提升了模式对东亚季风环流的模拟,改进后的模式模拟的西北太平洋海平面气压的负偏差显著降低,有利于西南季风以及西北太平洋向我国东部的水汽输送,同时在对流层低层出现反气旋异常响应,有效改善了中国东部南风偏弱及水汽辐合偏弱的模拟偏差,使得我国东部降水负偏差显著减小。以上结果表明,包括植被水力过程的陆气相互作用的合理表述是改善东亚夏季降水模拟的重要途径之一。展开更多
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
基金the French Defense Innovation Agency (AID)the French Procurement Agency for Armament (DGA)ONERA's scientific direction for funding and supporting the present work
文摘The aim of this paper is to simulate and study the early moments of the reactive ballistics of a large caliber projectile fired from a gun,combining 0D and 2D axisymmetric Computational Fluid Dynamics(CFD)approaches.First,the methodology is introduced with the development of an interior ballistics(IB)lumped parameter code(LPC),integrating an original image processing method for calculating the specific regression of propellant grains that compose the gun propellant.The ONERA CFD code CEDRE,equipped with a Dynamic Mesh Technique(DMT),is then used in conjunction with the developed LPC to build a dedicated methodology to calculate IB.First results obtained on the AGARD gun and 40 mm gun test cases are in a good agreement with the existing literature.CEDRE is also used to calculate inter-mediate ballistics(first milliseconds of free flight of the projectile)with a multispecies and reactive approach either starting from the gun muzzle plane or directly following IB.In the latter case,an inverse problem involving a Latin hypercube sampling method is used to find a gun propellant configuration that allows the projectile to reach a given exit velocity and base pressure when IB ends.The methodology developed in this work makes it possible to study the flame front of the intermediate flash and depressurization that occurs in a base bleed(BB)channel at the gun muzzle.Average pressure variations in the BB channel during depressurization are in good agreement with literature.
文摘In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.
文摘Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.
基金partially supported by the Natural Science Foundation of China (Grant Nos.62103052,52272358)partially supported by the Beijing Institute of Technology Research Fund Program for Young Scholars。
文摘This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.
基金supported by the National Natural Science Foundation of China(6193101562071335)+1 种基金the Technological Innovation Project of Hubei Province of China(2019AAA061)the Natural Science F oundation of Hubei Province of China(2021CFA002)。
文摘In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.
基金supported by the National Natural Science Foundation of China(61701140).
文摘To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.
文摘基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project)数值模拟试验,探讨了植被水力方案的引入对中国夏季降水模拟的影响。结果表明,植被水力方案的引入能够显著降低CAS-ESM2.0模式对中国夏季降水气候态的模拟偏差,特别是显著改进了中国东部、青藏高原降水的低估,青藏高原以东的川西地区降水高估的偏差,同时也改善了夏季降水年际变率和极端大雨日数的模拟性能。进一步分析显示,植被水力方案的改进显著减小了土壤湿度在长江流域偏干、青藏高原偏湿的模式模拟偏差,降低了我国中东部以及青藏高原地表感热通量和潜热通量的模拟偏差,改善了模式对陆气相互作用过程的模拟能力。陆气相互作用的改进显著提升了模式对东亚季风环流的模拟,改进后的模式模拟的西北太平洋海平面气压的负偏差显著降低,有利于西南季风以及西北太平洋向我国东部的水汽输送,同时在对流层低层出现反气旋异常响应,有效改善了中国东部南风偏弱及水汽辐合偏弱的模拟偏差,使得我国东部降水负偏差显著减小。以上结果表明,包括植被水力过程的陆气相互作用的合理表述是改善东亚夏季降水模拟的重要途径之一。
