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 concept of TNT(Trinitrotoluene,C_7H_5N_3O_6)equivalence is often invoked to evaluate the performance and predict the explosion parameters of different types of explosives.However,due to its low prediction accuracy...The concept of TNT(Trinitrotoluene,C_7H_5N_3O_6)equivalence is often invoked to evaluate the performance and predict the explosion parameters of different types of explosives.However,due to its low prediction accuracy and limited application range,the use of TNT equivalence for predicting explosion parameters in a confined space is rare.Compared with explosions in free fields,the process of explosive energy release in a confined space is closely related to various factors such as oxygen balance,combustible components content,and surrounding oxygen content.Studies have shown that in a confined space,negative oxygen balance explosives react with surrounding oxygen during afterburning,resulting in additional energy release and enhanced blast effects.The mechanism of energy release during afterburning is highly complex,making it challenging to determine the TNT equivalence for blast effects in a confined space.Therefore,this remains an active area of research.In this study,internal blast experiments were conducted using TNT and three other explosives under both air and N_2(Nitrogen)conditions to obtain explosion parameters including blast wave overpressure,quasi-static pressure,and temperature.The influences of oxygen balance and external oxygen content on energy release are analyzed.The author proposes principles for determining TNT equivalence for internal explosions while verifying the accuracy of obtained blast parameters through calculations based on TNT equivalence.These findings can serve as references for predicting blast performance.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.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
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
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.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
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
Based on 310 horizontal-component digital seismograms recorded at 14 seismic stations in Shanxi Digital Seis-mograph Network, the inelastic attenuation coefficient in Shanxi region is studied. By the methods of Atkins...Based on 310 horizontal-component digital seismograms recorded at 14 seismic stations in Shanxi Digital Seis-mograph Network, the inelastic attenuation coefficient in Shanxi region is studied. By the methods of Atkinson and Moya, the site response of each station and several source parameters are obtained and the inversion results from both methods are compared and analyzed. The frequency-dependent inelastic attenuation coefficient Q is estimated as Q( f )=323.2 f 0.506. The site responses of 14 seismic stations do not show significant amplification, which is consistent with their basement on rock. We also found the dependence of corner frequency on seismic moment, seismic moment on stress drop, source radius on stress drop.展开更多
Using the data of regional seismic network, this paper analyzes the current faulting behaviors of different segments of the Anninghe-Zemuhe fault zone, western Sichuan, and identifies the likely risky segments for pot...Using the data of regional seismic network, this paper analyzes the current faulting behaviors of different segments of the Anninghe-Zemuhe fault zone, western Sichuan, and identifies the likely risky segments for potential large earthquakes. The authors map the probable asperities from the abnormally low b-value distribution, develop and employ a method for identifying current faulting behaviors of individual fault segment from the combinations of multiple seismicity parameter values, and make an effort to estimate the average recurrence intervals of character-istic earthquakes by using the parameters of magnitude-frequency relationship of the asperity segment. The result suggests that the studied fault zone contains 5 segments of different current faulting behaviors. Among them, the Mianning-Xichang segment of the Anninghe fault has been locked under high stress, its central part is probably an asperity with a relatively large scale. The Xichang-Puge segment of the Zemuhe fault displays very low seismicity under low stress. Both the locked segment and the low-seismicity segment can be outlined on the across-profile of relocated hypocenter depths. The Mianning-Xichang segment is identified to be the one with potential large earth-quake risk, for which the average recurrence interval between the latest M = 6.7 earthquake in 1952 and the next characteristic event is estimated to be 55 to 67 years, and the magnitude of the potential earthquake between 7.0 and 7.5. Also, it has been preliminarily suggested that for a certain fault segment, its faulting behaviors may change and evolve with time gradually.展开更多
Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of severa...Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of several strong earthquakes in China and New Zealand. Akaikes AIC criterion is used to discriminate whether an accelerating mode of earthquake activity precedes those events or not. Finally, regional accelerating seismic activity and possible prediction approach for future strong earthquakes are discussed.展开更多
Influence of multiple structural parameters on the performance of a gun launch system driven by highpressure reactive gases is important for structural design and performance adjustment.A coupled lumped parameter mode...Influence of multiple structural parameters on the performance of a gun launch system driven by highpressure reactive gases is important for structural design and performance adjustment.A coupled lumped parameter model was utilized to predict the propellant combustion,and a dynamic finite element method was applied to approximate the mechanical interactions between the projectile and the barrel.The combustion and the mechanical interactions were coupled through a user subroutine interface in ABAQUS.The correctness and the capability of the finite element approximations in capturing small structural changes were validated by comparing predicted resistance with experiments.Based on the coupled model,the influence of structural parameters of a medium-caliber gun on the system performance was investigated.In order to reduce the research costs,orthogonal tests were designed to investigate the comprehensive effects of the parameters.According to statistical analysis,the important order of the structural parameters on the launching process was obtained.The results indicate that the influence of the width of the rotating band stands out among the studied parameters in the gun.The work provides a method to investigate the influence of multiple parameters on system performance and gives guidance for controlling the system performance.展开更多
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
A more general form of projective synchronization, so called linear generalized synchronization （LGS） is proposed, which includes the generalized projective synchronization （GPS） and the hybrid projective synchron...A more general form of projective synchronization, so called linear generalized synchronization （LGS） is proposed, which includes the generalized projective synchronization （GPS） and the hybrid projective synchronization （HPS） as its special cases, Based on the adaptive technique and Lyapunov stability theory, a general method for achieving the LGS between two chaotic or hyperehaotic systems with uncertain parameters in any scaling matrix is presented. Some numerical simulations are provided to show the effectiveness and feasibility of the proposed synchronization method.展开更多
By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. ...By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.展开更多
Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estima...Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.展开更多
When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian...When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.展开更多
Laser Induced Breakdown Spectroscopic(LIBS)technique was used to detect calcium and silicon in an unknown sample.In this method plasma was generated by Nd∶YAG laser of wavelength 1 064 nm with energy 400 mJ and pulse...Laser Induced Breakdown Spectroscopic(LIBS)technique was used to detect calcium and silicon in an unknown sample.In this method plasma was generated by Nd∶YAG laser of wavelength 1 064 nm with energy 400 mJ and pulse duration between 5~10ns.The method was applied for the qualitative as well as quantitative analysis.In the qualitative analysis,the electron number density(Ne)of plasma containing calcium and silicon is determined showing that Neof neutral particles is equivalent to 1016(cm-3)whereas for ionized particles it is 1017(cm-3).Plasma temperature is measured using Boltzmann plot method which must be greater than 10 000 k.Intensity ratio method is used for the quantitative analysis shows various elements in abundance with calcium and silicon in majority.展开更多
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.
文摘The concept of TNT(Trinitrotoluene,C_7H_5N_3O_6)equivalence is often invoked to evaluate the performance and predict the explosion parameters of different types of explosives.However,due to its low prediction accuracy and limited application range,the use of TNT equivalence for predicting explosion parameters in a confined space is rare.Compared with explosions in free fields,the process of explosive energy release in a confined space is closely related to various factors such as oxygen balance,combustible components content,and surrounding oxygen content.Studies have shown that in a confined space,negative oxygen balance explosives react with surrounding oxygen during afterburning,resulting in additional energy release and enhanced blast effects.The mechanism of energy release during afterburning is highly complex,making it challenging to determine the TNT equivalence for blast effects in a confined space.Therefore,this remains an active area of research.In this study,internal blast experiments were conducted using TNT and three other explosives under both air and N_2(Nitrogen)conditions to obtain explosion parameters including blast wave overpressure,quasi-static pressure,and temperature.The influences of oxygen balance and external oxygen content on energy release are analyzed.The author proposes principles for determining TNT equivalence for internal explosions while verifying the accuracy of obtained blast parameters through calculations based on TNT equivalence.These findings can serve as references for predicting blast performance.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
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.
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.
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
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In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
基金National Scientific Research Target Project of China (2001-BA601B01-01-05) during the tenth Five-year Plan.
文摘Based on 310 horizontal-component digital seismograms recorded at 14 seismic stations in Shanxi Digital Seis-mograph Network, the inelastic attenuation coefficient in Shanxi region is studied. By the methods of Atkinson and Moya, the site response of each station and several source parameters are obtained and the inversion results from both methods are compared and analyzed. The frequency-dependent inelastic attenuation coefficient Q is estimated as Q( f )=323.2 f 0.506. The site responses of 14 seismic stations do not show significant amplification, which is consistent with their basement on rock. We also found the dependence of corner frequency on seismic moment, seismic moment on stress drop, source radius on stress drop.
基金Chinese Joint Seismological Science Foundation (102002).
文摘Using the data of regional seismic network, this paper analyzes the current faulting behaviors of different segments of the Anninghe-Zemuhe fault zone, western Sichuan, and identifies the likely risky segments for potential large earthquakes. The authors map the probable asperities from the abnormally low b-value distribution, develop and employ a method for identifying current faulting behaviors of individual fault segment from the combinations of multiple seismicity parameter values, and make an effort to estimate the average recurrence intervals of character-istic earthquakes by using the parameters of magnitude-frequency relationship of the asperity segment. The result suggests that the studied fault zone contains 5 segments of different current faulting behaviors. Among them, the Mianning-Xichang segment of the Anninghe fault has been locked under high stress, its central part is probably an asperity with a relatively large scale. The Xichang-Puge segment of the Zemuhe fault displays very low seismicity under low stress. Both the locked segment and the low-seismicity segment can be outlined on the across-profile of relocated hypocenter depths. The Mianning-Xichang segment is identified to be the one with potential large earth-quake risk, for which the average recurrence interval between the latest M = 6.7 earthquake in 1952 and the next characteristic event is estimated to be 55 to 67 years, and the magnitude of the potential earthquake between 7.0 and 7.5. Also, it has been preliminarily suggested that for a certain fault segment, its faulting behaviors may change and evolve with time gradually.
基金National Natural Science Foundation of China (4007401340134010)Chinese Joint Seismological Science Foundation (042002) and the project during the Tenth Five-year Plan.
文摘Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of several strong earthquakes in China and New Zealand. Akaikes AIC criterion is used to discriminate whether an accelerating mode of earthquake activity precedes those events or not. Finally, regional accelerating seismic activity and possible prediction approach for future strong earthquakes are discussed.
基金the financial support from the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYLX_0399)
文摘Influence of multiple structural parameters on the performance of a gun launch system driven by highpressure reactive gases is important for structural design and performance adjustment.A coupled lumped parameter model was utilized to predict the propellant combustion,and a dynamic finite element method was applied to approximate the mechanical interactions between the projectile and the barrel.The combustion and the mechanical interactions were coupled through a user subroutine interface in ABAQUS.The correctness and the capability of the finite element approximations in capturing small structural changes were validated by comparing predicted resistance with experiments.Based on the coupled model,the influence of structural parameters of a medium-caliber gun on the system performance was investigated.In order to reduce the research costs,orthogonal tests were designed to investigate the comprehensive effects of the parameters.According to statistical analysis,the important order of the structural parameters on the launching process was obtained.The results indicate that the influence of the width of the rotating band stands out among the studied parameters in the gun.The work provides a method to investigate the influence of multiple parameters on system performance and gives guidance for controlling the system performance.
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金the National Natural Science Foundation of China (60574045 10661006).
文摘A more general form of projective synchronization, so called linear generalized synchronization （LGS） is proposed, which includes the generalized projective synchronization （GPS） and the hybrid projective synchronization （HPS） as its special cases, Based on the adaptive technique and Lyapunov stability theory, a general method for achieving the LGS between two chaotic or hyperehaotic systems with uncertain parameters in any scaling matrix is presented. Some numerical simulations are provided to show the effectiveness and feasibility of the proposed synchronization method.
基金supported by the National Natural Science Foundation of China(6120113461201135)+2 种基金the 111 Project(B08038)the Fundamental Research Funds for the Central Universities(72124669)the Open Research Fund of the Academy of Application(2014CXJJ-TX06)
文摘By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.
基金supported by the National Natural Science Foundation of China (61302188)the Nanjing University of Science and Technology Research Foundation (2010ZDJH05)
文摘Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.
基金supported by the National Natural Science Foundation of China(6130513361573285)the Fundamental Research Funds for the Central Universities(3102016CG002)
文摘When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.
文摘Laser Induced Breakdown Spectroscopic(LIBS)technique was used to detect calcium and silicon in an unknown sample.In this method plasma was generated by Nd∶YAG laser of wavelength 1 064 nm with energy 400 mJ and pulse duration between 5~10ns.The method was applied for the qualitative as well as quantitative analysis.In the qualitative analysis,the electron number density(Ne)of plasma containing calcium and silicon is determined showing that Neof neutral particles is equivalent to 1016(cm-3)whereas for ionized particles it is 1017(cm-3).Plasma temperature is measured using Boltzmann plot method which must be greater than 10 000 k.Intensity ratio method is used for the quantitative analysis shows various elements in abundance with calcium and silicon in majority.
