An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dyna...An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).展开更多
The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,...The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,which combines the advantages of mechanism model and black-box model,is proposed in this paper.To improve the performance,the static neural network and variable neural network are used to build the black-box model.The static neural network can significantly improve the static performance of the hybrid model,and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy.Finally,the hybrid dynamic model is validated with a 500 W PEM fuel cell.The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.展开更多
Passive bionic feet,known for their human-like compliance,have garnered attention for their potential to achieve notable environmental adaptability.In this paper,a method was proposed to unifying passive bionic feet s...Passive bionic feet,known for their human-like compliance,have garnered attention for their potential to achieve notable environmental adaptability.In this paper,a method was proposed to unifying passive bionic feet static supporting stability and dynamic terrain adaptability through the utilization of the Rigid-Elastic Hybrid(REH)dynamics model.First,a bionic foot model,named the Hinge Tension Elastic Complex(HTEC)model,was developed by extracting key features from human feet.Furthermore,the kinematics and REH dynamics of the HTEC model were established.Based on the foot dynamics,a nonlinear optimization method for stiffness matching(NOSM)was designed.Finally,the HTEC-based foot was constructed and applied onto BHR-B2 humanoid robot.The foot static stability is achieved.The enhanced adaptability is observed as the robot traverses square steel,lawn,and cobblestone terrains.Through proposed design method and structure,the mobility of the humanoid robot is improved.展开更多
In the process of grid-connected photovoltaic power generation,there are high requirements for the quality of the power that the inverter breaks into the grid.In this work,to improve the power quality of the grid-conn...In the process of grid-connected photovoltaic power generation,there are high requirements for the quality of the power that the inverter breaks into the grid.In this work,to improve the power quality of the grid-connected inverter into the grid,and the output of the system can meet the grid-connected requirements more quickly and accurately,we exhibit an approach toward establishing a mixed logical dynamical(MLD)model where logic variables were introduced to switch dynamics of the single-phase photovoltaic inverters.Besides,based on the model,our recent efforts in studying the finite control set model predictive control(FCS-MPC)and devising the output current full state observer are exciting for several advantages,including effectively avoiding the problem of the mixed-integer quadratic programming(MIQP),lowering the THD value of the output current of the inverter circuit,improving the quality of the power that the inverter breaks into the grid,and realizing the current output and the grid voltage same frequency and phase to meet grid connection requirements.Finally,the effectiveness of the mentioned methods is verified by MATLAB/Simulink simulation.展开更多
Insulin secreted by pancreatic islet ˇ-cells is the principal regulating hormone of glucose metabolism.Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus.Alt...Insulin secreted by pancreatic islet ˇ-cells is the principal regulating hormone of glucose metabolism.Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus.Although deterministic models of the insulin secretion pathway have been developed, the stochastic aspect of this biological pathway has not been explored. The first step in this direction presented here is a hybrid model of the insulin secretion pathway, in which the delayed rectifying KCchannels are treated as stochastic events. This hybrid model can not only reproduce the oscillation dynamics as the deterministic model does, but can also capture stochastic dynamics that the deterministic model does not. To measure the insulin oscillation system behavior, a probability-based measure is proposed and applied to test the effectiveness of a new remedy.展开更多
基金Botnia-Atlantica, an EU-programme financing cross border cooperation projects in Sweden, Finland and Norway, for their support of this work through the WindCoE project
文摘An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).
基金Supported by the National Science Fund for Distinguished Young Scholars of China (60925011)
文摘The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,which combines the advantages of mechanism model and black-box model,is proposed in this paper.To improve the performance,the static neural network and variable neural network are used to build the black-box model.The static neural network can significantly improve the static performance of the hybrid model,and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy.Finally,the hybrid dynamic model is validated with a 500 W PEM fuel cell.The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.
基金supported by the National Natural Science Foundation of China(Grant No.62073041)the Open Fund of Laboratory of Aerospace Servo Actuation and Transmission(Grant No.LASAT-2023A04)the Fundamental Research Funds for the Central Universities(Grant Nos.2024CX06011,2024CX06079)。
文摘Passive bionic feet,known for their human-like compliance,have garnered attention for their potential to achieve notable environmental adaptability.In this paper,a method was proposed to unifying passive bionic feet static supporting stability and dynamic terrain adaptability through the utilization of the Rigid-Elastic Hybrid(REH)dynamics model.First,a bionic foot model,named the Hinge Tension Elastic Complex(HTEC)model,was developed by extracting key features from human feet.Furthermore,the kinematics and REH dynamics of the HTEC model were established.Based on the foot dynamics,a nonlinear optimization method for stiffness matching(NOSM)was designed.Finally,the HTEC-based foot was constructed and applied onto BHR-B2 humanoid robot.The foot static stability is achieved.The enhanced adaptability is observed as the robot traverses square steel,lawn,and cobblestone terrains.Through proposed design method and structure,the mobility of the humanoid robot is improved.
基金supported by the National Natural Science Foundation of China(Grant No.51667013)the Science and Technology Project of State Grid Corporation of China(Grant No.52272219000 V).
文摘In the process of grid-connected photovoltaic power generation,there are high requirements for the quality of the power that the inverter breaks into the grid.In this work,to improve the power quality of the grid-connected inverter into the grid,and the output of the system can meet the grid-connected requirements more quickly and accurately,we exhibit an approach toward establishing a mixed logical dynamical(MLD)model where logic variables were introduced to switch dynamics of the single-phase photovoltaic inverters.Besides,based on the model,our recent efforts in studying the finite control set model predictive control(FCS-MPC)and devising the output current full state observer are exciting for several advantages,including effectively avoiding the problem of the mixed-integer quadratic programming(MIQP),lowering the THD value of the output current of the inverter circuit,improving the quality of the power that the inverter breaks into the grid,and realizing the current output and the grid voltage same frequency and phase to meet grid connection requirements.Finally,the effectiveness of the mentioned methods is verified by MATLAB/Simulink simulation.
基金supported by the National Science Foundation under award DMS-1225160,CCF-0726763,and CCF-0953590the National Institutes of Health under award GM078989
文摘Insulin secreted by pancreatic islet ˇ-cells is the principal regulating hormone of glucose metabolism.Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus.Although deterministic models of the insulin secretion pathway have been developed, the stochastic aspect of this biological pathway has not been explored. The first step in this direction presented here is a hybrid model of the insulin secretion pathway, in which the delayed rectifying KCchannels are treated as stochastic events. This hybrid model can not only reproduce the oscillation dynamics as the deterministic model does, but can also capture stochastic dynamics that the deterministic model does not. To measure the insulin oscillation system behavior, a probability-based measure is proposed and applied to test the effectiveness of a new remedy.
