The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing s...The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.展开更多
For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For ...For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.展开更多
In this paper a fast output sampling （FOS） estimator is designed for estimation of state-space variables of DC-DC boost converter. Estimated state-space variables are output voltage of the converter and its first de...In this paper a fast output sampling （FOS） estimator is designed for estimation of state-space variables of DC-DC boost converter. Estimated state-space variables are output voltage of the converter and its first derivative, which are suitable for model reference adaptive controllers and sliding mode controllers design. Estimator is designed for operation in continuous and discontinuous conduction modes. The simulation results show that proposed FOS estimator provides good estimation of state-space variables despite the voltage ripple caused by high frequency switching in converter and disturbances （change of load and input voltage）.展开更多
Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human braintissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidlyest...Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human braintissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidlyestimate diffusion tensors (DTs) using only a small quantity of diffusion-weighted (DW) images. However, thesemethods typically use the DW images obtained with fixed q-space sampling schemes as the training data, limitingthe application scenarios of such methods. To address this issue, we develop a new deep neural network calledq-space-coordinate-guided diffusion tensor imaging (QCG-DTI), which can efficiently and correctly estimate DTsunder flexible q-space sampling schemes. First, we propose a q-space-coordinate-embedded feature consistencystrategy to ensure the correspondence between q-space-coordinates and their respective DW images. Second, aq-space-coordinate fusion (QCF) module is introduced which eficiently embeds q-space-coordinates into multiscalefeatures of the corresponding DW images by linearly adjusting the feature maps along the channel dimension,thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense(MRD) module is proposed which enhances the network's feature extraction and image reconstruction capabilitiesby using dual-branch convolutions with different kernel sizes to extract features at diferent scales. Compared tostate-of-the-art methods that rely on a fixed sampling scheme, the proposed network can obtain high-quality diffusiontensors and derived parameters even using DW images acquired with flexible q-space sampling schemes. Comparedto state-of-the-art deep learning methods, QCG-DTI reduces the mean absolute error by approximately 15% onfractional anisotropy and around 25% on mean diffusivity.展开更多
Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order ...Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.展开更多
It is proposed that some possible macroseismic epicenters can be determined quickly from the relationship that the microseismic epicenters located by instruments bear with faults. Based on these so-called macroseismic...It is proposed that some possible macroseismic epicenters can be determined quickly from the relationship that the microseismic epicenters located by instruments bear with faults. Based on these so-called macroseismic epicenters, we can make fast seismic hazard estimation after a shock by use of the empirical distribution model of seismic intensity. In comparison with the method that uses the microseismic epicenters directly, this approach can increase the precision of fast seismic hazard estimation. Statistical analysis of 133 main earthquakes in China was made. The result shows that the deviation distance between the microseismic epicenter and macroseismic epicenter falls within the range of 35 km for 88 % earthquakes of the total and within the range of 35 to 75 km for the remaining ones. Then, we can take the area that has the microseismic epicenter as its center and is 35 km in radius as the area for emphatic analysis, and take the area within 75 km around the microseismic epicenter as the area for general analysis. The relation between the 66 earthquake cases on the N-S Seismic Belt in China and the spatial distribution characteristics of faults and the results of focal mechanism solution were analyzed in detail. We know from the analysis that the error of instrumental epicenter determination is not the only factor that gives effects to the deviation of the macroseismic epicenter. In addition to it, the fault size, fault distribution, fault activity, fault intersection types, earthquake magnitude, etc. are also main affecting factors. By sorting out, processing and analyzing these affecting factors, the principle and procedures for quickly determining the possible position of the macroseismic epicenter were set up. Taking these as a basis and establishing a nationwide database of faults that contains relevant factors, it is possible to apply this method in practical fast estimation of seismic hazard.展开更多
This paper proposes a fast adaptive fault estimator-based active fault-tolerant control strategy for a quadrotor UAV against multiple actuator faults.A fast adaptive fault estimation algorithm is designed to estimate ...This paper proposes a fast adaptive fault estimator-based active fault-tolerant control strategy for a quadrotor UAV against multiple actuator faults.A fast adaptive fault estimation algorithm is designed to estimate the unknown actuator fault parameters.By synthesizing the fast adaptive fault estimator with the embedded control law,an active fault-tolerant control mechanism is established to compensate the adverse e®ects of multiple actuator faults.The e®ectiveness of the proposed strategy is validated through both numerical simulations and experimental tests.展开更多
基金supported by the National Natural Science Foundation of China(11574120,U1636117)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing,Ministry of Education,China(UASP1503)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20161359)Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project
文摘The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.
基金This work was supported by the National Natural Science Foundation(NNSF)of China under grant no.61673386,62073335the China Postdoctoral Science Foundation(2017M613201,2019T120944).
文摘For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.
文摘In this paper a fast output sampling （FOS） estimator is designed for estimation of state-space variables of DC-DC boost converter. Estimated state-space variables are output voltage of the converter and its first derivative, which are suitable for model reference adaptive controllers and sliding mode controllers design. Estimator is designed for operation in continuous and discontinuous conduction modes. The simulation results show that proposed FOS estimator provides good estimation of state-space variables despite the voltage ripple caused by high frequency switching in converter and disturbances （change of load and input voltage）.
基金Project supported by the National Natural Science Foundation of China(No.62062023)。
文摘Diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human braintissue's microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidlyestimate diffusion tensors (DTs) using only a small quantity of diffusion-weighted (DW) images. However, thesemethods typically use the DW images obtained with fixed q-space sampling schemes as the training data, limitingthe application scenarios of such methods. To address this issue, we develop a new deep neural network calledq-space-coordinate-guided diffusion tensor imaging (QCG-DTI), which can efficiently and correctly estimate DTsunder flexible q-space sampling schemes. First, we propose a q-space-coordinate-embedded feature consistencystrategy to ensure the correspondence between q-space-coordinates and their respective DW images. Second, aq-space-coordinate fusion (QCF) module is introduced which eficiently embeds q-space-coordinates into multiscalefeatures of the corresponding DW images by linearly adjusting the feature maps along the channel dimension,thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense(MRD) module is proposed which enhances the network's feature extraction and image reconstruction capabilitiesby using dual-branch convolutions with different kernel sizes to extract features at diferent scales. Compared tostate-of-the-art methods that rely on a fixed sampling scheme, the proposed network can obtain high-quality diffusiontensors and derived parameters even using DW images acquired with flexible q-space sampling schemes. Comparedto state-of-the-art deep learning methods, QCG-DTI reduces the mean absolute error by approximately 15% onfractional anisotropy and around 25% on mean diffusivity.
文摘Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.
基金the Key Project (9502020104)from China Seismological Bureau under the " Ninth Five-year Plan" , China.
文摘It is proposed that some possible macroseismic epicenters can be determined quickly from the relationship that the microseismic epicenters located by instruments bear with faults. Based on these so-called macroseismic epicenters, we can make fast seismic hazard estimation after a shock by use of the empirical distribution model of seismic intensity. In comparison with the method that uses the microseismic epicenters directly, this approach can increase the precision of fast seismic hazard estimation. Statistical analysis of 133 main earthquakes in China was made. The result shows that the deviation distance between the microseismic epicenter and macroseismic epicenter falls within the range of 35 km for 88 % earthquakes of the total and within the range of 35 to 75 km for the remaining ones. Then, we can take the area that has the microseismic epicenter as its center and is 35 km in radius as the area for emphatic analysis, and take the area within 75 km around the microseismic epicenter as the area for general analysis. The relation between the 66 earthquake cases on the N-S Seismic Belt in China and the spatial distribution characteristics of faults and the results of focal mechanism solution were analyzed in detail. We know from the analysis that the error of instrumental epicenter determination is not the only factor that gives effects to the deviation of the macroseismic epicenter. In addition to it, the fault size, fault distribution, fault activity, fault intersection types, earthquake magnitude, etc. are also main affecting factors. By sorting out, processing and analyzing these affecting factors, the principle and procedures for quickly determining the possible position of the macroseismic epicenter were set up. Taking these as a basis and establishing a nationwide database of faults that contains relevant factors, it is possible to apply this method in practical fast estimation of seismic hazard.
基金National Key Research and Development Program of China(Grant No.2020YFA0711200)National Natural Science Foundation of China(Grant Nos.61833013 and 61973012)+2 种基金Defense Industrial Technology Development Program(Grant No.JCKY2020601C016)Key Research and Development Program of Zhejiang(Grant No.2021C03158)Science and Technology Key Innovative Project of Hangzhou(Grant No.20182014B06).
文摘This paper proposes a fast adaptive fault estimator-based active fault-tolerant control strategy for a quadrotor UAV against multiple actuator faults.A fast adaptive fault estimation algorithm is designed to estimate the unknown actuator fault parameters.By synthesizing the fast adaptive fault estimator with the embedded control law,an active fault-tolerant control mechanism is established to compensate the adverse e®ects of multiple actuator faults.The e®ectiveness of the proposed strategy is validated through both numerical simulations and experimental tests.
