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
In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dyn...In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dynamic friction inside the valve may cause hysteresis and a dead zone. In this paper, a dither compensation mechanism is proposed to reduce negative effects on the basis of analyzing the mechanism of friction force. The specific dither signal（using a sinusoidal signal） was superimposed on the control signal of the valve. Based on the relationship between the parameters of the dither signal and the inherent characteristics of the proportional servo valve, a parameter tuning method was proposed, which uses a displacement sensor to measure the maximum static friction inside the valve. According to the experimental results, the proper amplitude ranges are determined for different pressures. In order to get the optimal parameters of the dither signal, some dither compensation experiments have been carried out on different signal amplitude and gas pressure conditions. Optimal parameters are determined under two kinds of pressure conditions. Using tuning parameters the valve spool displacement experiment has been taken. From the experiment results, hysteresis of the proportional servo valve is significantly reduced. And through simulation and experiments, the cut-off frequency of the proportional valve has also been widened. Therefore after adding the dither signal, the static and dynamic characteristics of the proportional valve are both improved to a certain degree. This research proposes a parameter tuning method of dither signal, and the validity of the method is verified experimentally.展开更多
Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condit...Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condition, a method based on small signal model and least mean square（LMS） algorithm is proposed. According to the method, the initial values of adaptive filter＇s weight vector are calculated with the solved model parameters through small signal model at first,then the small amount of direction cosine and its derivative are set as the input of the filter, and the small amount of the interference is set as the filter＇s expected vector. After that, the aircraft magnetic interference is compensated by LMS algorithm. Finally, the method is verified by simulation and experiment. The result shows that the compensation effect can be improved obviously by the LMS algorithm when original solved parameters have low precision. The method can further improve the compensation effect even if the solved parameters have high precision.展开更多
Recently,azobenzene-4,4'-dicarboxylic acid(ADCA)has been produced gradually for use as an organic synthesis or pharmaceutical intermediate due to its eminent performance.With large quantities put into application ...Recently,azobenzene-4,4'-dicarboxylic acid(ADCA)has been produced gradually for use as an organic synthesis or pharmaceutical intermediate due to its eminent performance.With large quantities put into application in the future,the thermal stability of this substance during storage,transportation,and use will become quite important.Thus,in this work,the thermal decomposition behavior,thermal decomposition kinetics,and thermal hazard of ADCA were investigated.Experiments were conducted by using a SENSYS evo DSC device.A combination of differential iso-conversion method,compensation parameter method,and nonlinear fitting evaluation were also used to analyze thermal kinetics and mechanism of ADCA decomposition.The results show that when conversion rate α increases,the activation energies of ADCA's first and main decomposition peaks fall.The amount of heat released during decomposition varies between 182.46 and 231.16 J·g^(-1).The proposed kinetic equation is based on the Avrami-Erofeev model,which is consistent with the decomposition progress.Applying the Frank-Kamenetskii model,a calculated self-accelerating decomposition temperature of 287.0℃is obtained.展开更多
The integration of deep learning into computational imaging has driven substantial advancements in coherent diffraction imaging(CDI).While physics-driven neural networks have emerged as a promising approach through th...The integration of deep learning into computational imaging has driven substantial advancements in coherent diffraction imaging(CDI).While physics-driven neural networks have emerged as a promising approach through their unsupervised learning paradigm,their practical implementation faces critical challenges:measurement uncertainties in physical parameters(e.g.,the propagation distance and the size of sample area)severely degrade reconstruction quality.To overcome this limitation,we propose a deep-learning-enabled spatial sample interval optimization framework that synergizes physical models with neural network adaptability.Our method embeds spatial sample intervals as trainable parameters within a PhysenNet architecture coupled with Fresnel diffraction physics,enabling simultaneous image reconstruction and system parameter calibration.Experimental validation demonstrates robust performance with structural similarity(SSIM)values consistently maintained at 0.6 across diffraction distances spanning of 10-200 mm,using a 1024×1024 region of interest(ROI)from a 1624×1440 CCD(pixel size:4.5μm)under 632.8 nm illumination.This framework has excellent fault tolerance,that is,it can still maintain high-quality image restoration even when the propagation distance measurement error is large.Compared to conventional iterative reconstruction algorithms,this approach can transform fixed parameters into learnable parameters,making almost all image restoration experiments easier to implement,enhancing system robustness against experimental uncertainties.This work establishes,to our knowledge,a new paradigm for adaptive diffraction imaging systems capable of operating in complex real scenarios.展开更多
Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths....Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths.Consequently,it is difficult to guarantee the integrity and accuracy of three-dimensional(3D)measurements.In this study,a novel measurement trajectory planning method is developed to accurately obtain the 3D point clouds of large complex components by accounting for the field of view and overlapping area constraints.A hybrid identification algorithm based on the quasi-Newton and Levenberg Marquardt method is then proposed to realize the synchronous identification of kinematic parameter errors of the measurement system,allowing it to accurately reach the planning viewpoint.Finally,robotic calibration and measurement experiments of a high-speed rail headstock are conducted to evaluate the effectiveness and practicality of the proposed methods.展开更多
To address the problem of insufficient system inertia and improve the power quality of grid-connected inverters,and to enhance the stability of the power system,a method to control a virtual synchronous generator(VSG)...To address the problem of insufficient system inertia and improve the power quality of grid-connected inverters,and to enhance the stability of the power system,a method to control a virtual synchronous generator(VSG)output voltage based on model predictive control(MPC)is proposed.Parameters of the inductors,capacitors and other components of the VSG can vary as the temperature and current changes.Consequently the VSG output voltage and power control accuracy using the conventional MPC method may be reduced.In this paper,to improve the parameter robustness of the MPC method,a new weighted predictive capacitor voltage control method is proposed.Through detailed theoretical analysis,the principle of the proposed method to reduce the influence of parameter errors on voltage tracking accuracy is analyzed.Finally,the effectiveness and feasibility of the proposed method are verified by experimental tests using the Typhoon control hardware-in-the-loop experimental platform.展开更多
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.
基金Supported by National Natural Science Foundation of China(Grant No.51375045)the State Key Laboratory Program(Grant No.GZKF-201214)
文摘In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dynamic friction inside the valve may cause hysteresis and a dead zone. In this paper, a dither compensation mechanism is proposed to reduce negative effects on the basis of analyzing the mechanism of friction force. The specific dither signal（using a sinusoidal signal） was superimposed on the control signal of the valve. Based on the relationship between the parameters of the dither signal and the inherent characteristics of the proportional servo valve, a parameter tuning method was proposed, which uses a displacement sensor to measure the maximum static friction inside the valve. According to the experimental results, the proper amplitude ranges are determined for different pressures. In order to get the optimal parameters of the dither signal, some dither compensation experiments have been carried out on different signal amplitude and gas pressure conditions. Optimal parameters are determined under two kinds of pressure conditions. Using tuning parameters the valve spool displacement experiment has been taken. From the experiment results, hysteresis of the proportional servo valve is significantly reduced. And through simulation and experiments, the cut-off frequency of the proportional valve has also been widened. Therefore after adding the dither signal, the static and dynamic characteristics of the proportional valve are both improved to a certain degree. This research proposes a parameter tuning method of dither signal, and the validity of the method is verified experimentally.
基金co-supported by the National Basic Research Program of China (No. 623125020103)
文摘Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condition, a method based on small signal model and least mean square（LMS） algorithm is proposed. According to the method, the initial values of adaptive filter＇s weight vector are calculated with the solved model parameters through small signal model at first,then the small amount of direction cosine and its derivative are set as the input of the filter, and the small amount of the interference is set as the filter＇s expected vector. After that, the aircraft magnetic interference is compensated by LMS algorithm. Finally, the method is verified by simulation and experiment. The result shows that the compensation effect can be improved obviously by the LMS algorithm when original solved parameters have low precision. The method can further improve the compensation effect even if the solved parameters have high precision.
基金supported by National Natural Science Foundation of China(51974166).
文摘Recently,azobenzene-4,4'-dicarboxylic acid(ADCA)has been produced gradually for use as an organic synthesis or pharmaceutical intermediate due to its eminent performance.With large quantities put into application in the future,the thermal stability of this substance during storage,transportation,and use will become quite important.Thus,in this work,the thermal decomposition behavior,thermal decomposition kinetics,and thermal hazard of ADCA were investigated.Experiments were conducted by using a SENSYS evo DSC device.A combination of differential iso-conversion method,compensation parameter method,and nonlinear fitting evaluation were also used to analyze thermal kinetics and mechanism of ADCA decomposition.The results show that when conversion rate α increases,the activation energies of ADCA's first and main decomposition peaks fall.The amount of heat released during decomposition varies between 182.46 and 231.16 J·g^(-1).The proposed kinetic equation is based on the Avrami-Erofeev model,which is consistent with the decomposition progress.Applying the Frank-Kamenetskii model,a calculated self-accelerating decomposition temperature of 287.0℃is obtained.
基金supported by the Basic and Applied Basic Research Foundation of Guangdong Province(No.2022A1515110203)the Science and Technology Research Project of Jiangxi Provincial Department of Education(No.GJJ203501)。
文摘The integration of deep learning into computational imaging has driven substantial advancements in coherent diffraction imaging(CDI).While physics-driven neural networks have emerged as a promising approach through their unsupervised learning paradigm,their practical implementation faces critical challenges:measurement uncertainties in physical parameters(e.g.,the propagation distance and the size of sample area)severely degrade reconstruction quality.To overcome this limitation,we propose a deep-learning-enabled spatial sample interval optimization framework that synergizes physical models with neural network adaptability.Our method embeds spatial sample intervals as trainable parameters within a PhysenNet architecture coupled with Fresnel diffraction physics,enabling simultaneous image reconstruction and system parameter calibration.Experimental validation demonstrates robust performance with structural similarity(SSIM)values consistently maintained at 0.6 across diffraction distances spanning of 10-200 mm,using a 1024×1024 region of interest(ROI)from a 1624×1440 CCD(pixel size:4.5μm)under 632.8 nm illumination.This framework has excellent fault tolerance,that is,it can still maintain high-quality image restoration even when the propagation distance measurement error is large.Compared to conventional iterative reconstruction algorithms,this approach can transform fixed parameters into learnable parameters,making almost all image restoration experiments easier to implement,enhancing system robustness against experimental uncertainties.This work establishes,to our knowledge,a new paradigm for adaptive diffraction imaging systems capable of operating in complex real scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.52105514,52075204)the Fundamental Research Funds for the Central Universities(Grant No.2042023kf0114)+1 种基金Wuhan Natural Science Foundation(Grant No.20240408010202220)Hubei Province Key R&D Program(Grant No.2022BAA067).
文摘Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths.Consequently,it is difficult to guarantee the integrity and accuracy of three-dimensional(3D)measurements.In this study,a novel measurement trajectory planning method is developed to accurately obtain the 3D point clouds of large complex components by accounting for the field of view and overlapping area constraints.A hybrid identification algorithm based on the quasi-Newton and Levenberg Marquardt method is then proposed to realize the synchronous identification of kinematic parameter errors of the measurement system,allowing it to accurately reach the planning viewpoint.Finally,robotic calibration and measurement experiments of a high-speed rail headstock are conducted to evaluate the effectiveness and practicality of the proposed methods.
基金supported in part by the National Natural Science Foundation of China(51707176)in part by the Youth Talent Support Project of Henan Province(2019HYTP021)+1 种基金in part by the Youth Talent Support Project of Henan Province(2019HYTP021)in part by the Key Research,Development and Promotion Special Project(Science and Technology)of Henan Province(202102210103).
文摘To address the problem of insufficient system inertia and improve the power quality of grid-connected inverters,and to enhance the stability of the power system,a method to control a virtual synchronous generator(VSG)output voltage based on model predictive control(MPC)is proposed.Parameters of the inductors,capacitors and other components of the VSG can vary as the temperature and current changes.Consequently the VSG output voltage and power control accuracy using the conventional MPC method may be reduced.In this paper,to improve the parameter robustness of the MPC method,a new weighted predictive capacitor voltage control method is proposed.Through detailed theoretical analysis,the principle of the proposed method to reduce the influence of parameter errors on voltage tracking accuracy is analyzed.Finally,the effectiveness and feasibility of the proposed method are verified by experimental tests using the Typhoon control hardware-in-the-loop experimental platform.
