The energy-saving electrohydraulic flow matching （EFM） system opens up an opportunity to minimize valve losses by fully opening the control valves, but the controllability is lost under overrunning load conditions. ...The energy-saving electrohydraulic flow matching （EFM） system opens up an opportunity to minimize valve losses by fully opening the control valves, but the controllability is lost under overrunning load conditions. To address this issue, this paper proposes a valve-based compensator to improve the controllability of the energy-saving EFM system. The valve-based compensator consists of a static compensator and a differential dynamic compensator based on load conditions. The energy effi- ciency, the stability performance, and the damping characteristic are analyzed under different control parameters. A parameter selection method is used to improve the efficiency, ensure the stability performance, and obtain good dynamic behavior. A test rig with a 2-t hydraulic excavator is built, and experimental tests are carried out to validate the proposed valve-based compensator. The experimental results indicate that the controllability of the EFM system is improved, and the characteristic of high energy efficiency is obtained by the proposed compensator.展开更多
A heavy-duty gas turbine,designed for natural gas,was used to bum the syngas with two different calorific values.This study was mainly to optimize the flow matching scheme for the gas turbine.Two models of gas turbine...A heavy-duty gas turbine,designed for natural gas,was used to bum the syngas with two different calorific values.This study was mainly to optimize the flow matching scheme for the gas turbine.Two models of gas turbine burning syngas with different calorific values were established and the calculation models of different flow matching schemes were provided.The optimum scheme was obtained by evaluating thermal efficiency and work output under different operating conditions.The results showed that the highest unit efficiency was achieved by,without significant drop in work output,increasing the throat area of the turbine nozzle and reducing the initial temperature of the gas.On the premise of ensuring the safety of the gas turbine unit,increasing the pressure ratio of the compressor could further improve the unit efficiency and the work output.Simply adjusting the angle of the inlet guide vane fails to match the flow of compressor and turbine.The measures such as reducing inlet temperature of turbine or air bleed still need to be adopted,but the thermal efficiency dropped significantly in this process.展开更多
Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate...Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification;this can consume much time and produce building facade contained results.To address this problem,a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper.Firstly,3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images,respectively.Then,the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points.Subsequently,the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline(LR-B)interpolation method with triangular mesh constraint for the point clouds void area,and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice.Finally,a seamline network is automatically searched using a disparity map optimization algorithm,and DOM is smartly mosaicked.The qualitative and quantitative experimental results on three datasets were produced and evaluated,which confirmed the feasibility of the proposed method,and the DOM accuracy can reach 1 Ground Sample Distance(GSD)level.The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.展开更多
Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended time...Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended timescales of months to years and low signal-to-noise ratios,detecting and analyzing EMRIs with confidence generally relies on long-term observations.Besides the length of data,parameter estimation is particularly challenging due to non-local parameter degeneracies,arising from multiple local maxima,as well as flat regions and ridges inherent in the likelihood function.These factors lead to exceptionally high time complexity for parameter analysis based on traditional matched filtering and random sampling methods.To address these challenges,the present study explores a machine learning approach to Bayesian posterior estimation of EMRI signals,leveraging the recently developed flow matching technique based on ordinary differential equation neural networks.To our knowledge,this is also the first instance of applying continuous normalizing flows to EMRI analysis.Our approach demonstrates an increase in computational efficiency by several orders of magnitude compared to the traditional Markov chain Monte Carlo(MCMC)methods,while preserving the unbiasedness of results.However,we note that the posterior distributions generated by FMPE may exhibit broader uncertainty ranges than those obtained through full Bayesian sampling,requiring subsequent refinement via methods such as MCMC.Notably,when searching from large priors,our model rapidly approaches the true values while MCMC struggles to converge to the global maximum.Our findings highlight that machine learning has the potential to efficiently handle the vast EMRI parameter space of up to seventeen dimensions,offering new perspectives for advancing space-based GW detection and GW astronomy.展开更多
基金Project supported by the National Natural Science Foundation of China （No. 51375431）, the Open Fund of the State Key Laboratory of Fluid Power and Mechatronic Systems （No. GZKF-201503）, and the Research Fund of the State Key Laboratory of Mechanical Transmission （No. SKLMT-ZZKT-2015Z10）, China
文摘The energy-saving electrohydraulic flow matching （EFM） system opens up an opportunity to minimize valve losses by fully opening the control valves, but the controllability is lost under overrunning load conditions. To address this issue, this paper proposes a valve-based compensator to improve the controllability of the energy-saving EFM system. The valve-based compensator consists of a static compensator and a differential dynamic compensator based on load conditions. The energy effi- ciency, the stability performance, and the damping characteristic are analyzed under different control parameters. A parameter selection method is used to improve the efficiency, ensure the stability performance, and obtain good dynamic behavior. A test rig with a 2-t hydraulic excavator is built, and experimental tests are carried out to validate the proposed valve-based compensator. The experimental results indicate that the controllability of the EFM system is improved, and the characteristic of high energy efficiency is obtained by the proposed compensator.
基金This work was support by the National Natural Science Foundation of China(Grant No.51976214 and No.51576193)National Science and Technology Major Project(2017-V-0010-0001 and 2017-IV-0100-0047).
文摘A heavy-duty gas turbine,designed for natural gas,was used to bum the syngas with two different calorific values.This study was mainly to optimize the flow matching scheme for the gas turbine.Two models of gas turbine burning syngas with different calorific values were established and the calculation models of different flow matching schemes were provided.The optimum scheme was obtained by evaluating thermal efficiency and work output under different operating conditions.The results showed that the highest unit efficiency was achieved by,without significant drop in work output,increasing the throat area of the turbine nozzle and reducing the initial temperature of the gas.On the premise of ensuring the safety of the gas turbine unit,increasing the pressure ratio of the compressor could further improve the unit efficiency and the work output.Simply adjusting the angle of the inlet guide vane fails to match the flow of compressor and turbine.The measures such as reducing inlet temperature of turbine or air bleed still need to be adopted,but the thermal efficiency dropped significantly in this process.
基金supported by the National Natural Science Foundation of China[Grant No.41771479]the National High-Resolution Earth Observation System(the Civil Part)[Grant No.50-H31D01-0508-13/15]the Japan Society for the Promotion of Science[Grant No.22H03573].
文摘Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification;this can consume much time and produce building facade contained results.To address this problem,a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper.Firstly,3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images,respectively.Then,the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points.Subsequently,the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline(LR-B)interpolation method with triangular mesh constraint for the point clouds void area,and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice.Finally,a seamline network is automatically searched using a disparity map optimization algorithm,and DOM is smartly mosaicked.The qualitative and quantitative experimental results on three datasets were produced and evaluated,which confirmed the feasibility of the proposed method,and the DOM accuracy can reach 1 Ground Sample Distance(GSD)level.The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFC2201901,2021YFC2203004,2020YFC2200100 and 2021YFC2201903)International Partnership Program of the Chinese Academy of Sciences(Grant No.025GJHZ2023106GC)+4 种基金the financial support from Brazilian agencies Funda??o de AmparoàPesquisa do Estado de S?o Paulo(FAPESP)Funda??o de Amparoà Pesquisa do Estado do Rio Grande do Sul(FAPERGS)Fundacao de Amparoà Pesquisa do Estado do Rio de Janeiro(FAPERJ)Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nível Superior(CAPES)。
文摘Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended timescales of months to years and low signal-to-noise ratios,detecting and analyzing EMRIs with confidence generally relies on long-term observations.Besides the length of data,parameter estimation is particularly challenging due to non-local parameter degeneracies,arising from multiple local maxima,as well as flat regions and ridges inherent in the likelihood function.These factors lead to exceptionally high time complexity for parameter analysis based on traditional matched filtering and random sampling methods.To address these challenges,the present study explores a machine learning approach to Bayesian posterior estimation of EMRI signals,leveraging the recently developed flow matching technique based on ordinary differential equation neural networks.To our knowledge,this is also the first instance of applying continuous normalizing flows to EMRI analysis.Our approach demonstrates an increase in computational efficiency by several orders of magnitude compared to the traditional Markov chain Monte Carlo(MCMC)methods,while preserving the unbiasedness of results.However,we note that the posterior distributions generated by FMPE may exhibit broader uncertainty ranges than those obtained through full Bayesian sampling,requiring subsequent refinement via methods such as MCMC.Notably,when searching from large priors,our model rapidly approaches the true values while MCMC struggles to converge to the global maximum.Our findings highlight that machine learning has the potential to efficiently handle the vast EMRI parameter space of up to seventeen dimensions,offering new perspectives for advancing space-based GW detection and GW astronomy.
