An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of t...An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.展开更多
To design the control system for some homing missile so that the autopilot can transfer guidance command correctly and be robust to disturbances, such as the measurement noises and parameter variation caused by areody...To design the control system for some homing missile so that the autopilot can transfer guidance command correctly and be robust to disturbances, such as the measurement noises and parameter variation caused by areodynamic floating. The model reference adaptive control was combined with the variable structure control to design a model reference variable structure (MRVS) control system whose control structure is simple and can be realized easily. The simulation results indicate that MRVS can complete the task of transferring guidance command and suppress the distrubances effectively.展开更多
The case variable model is put forward by analyzing the system model and the IDEFO model. The component variables of the machining system are classified into four types, I. E uncontrolled variables, process variables,...The case variable model is put forward by analyzing the system model and the IDEFO model. The component variables of the machining system are classified into four types, I. E uncontrolled variables, process variables, controlled variables and output variables. The process of building the case base is given. The high-speedcutting data base system is developed based on the presented variable model.展开更多
In this paper, a unified internal state variable(ISV) model for predicting microstructure evolution during hot working process of AZ80 magnesium alloy was developed. A novel aspect of the proposed model is that the in...In this paper, a unified internal state variable(ISV) model for predicting microstructure evolution during hot working process of AZ80 magnesium alloy was developed. A novel aspect of the proposed model is that the interactive effects of material hardening, recovery and dynamic recrystallization(DRX) on the characteristic deformation behavior were considered by incorporating the evolution laws of viscoplastic flow, dislocation activities, DRX nucleation and boundary migration in a coupled manner. The model parameters were calibrated based on the experimental data analysis and genetic algorithm(GA) based objective optimization. The predicted flow stress, DRX fraction and average grain size match well with experimental results. The proposed model was embedded in the finite element(FE) software DEFORM-3 D via user defined subroutine to simulate the hot compression and equal channel angular extrusion(ECAE) processes. The heterogeneous microstructure distributions at different deformation zones and the dislocation density evolution with competitive deformation mechanisms were captured.This study can provide a theoretical solution for the hot working problems of magnesium alloy.展开更多
The inherent compliance of continuum robots holds great promise in the fields of soft manipulation and safe human–robot interaction.This compliance reduces the risk of damage to the manipulated object and its surroun...The inherent compliance of continuum robots holds great promise in the fields of soft manipulation and safe human–robot interaction.This compliance reduces the risk of damage to the manipulated object and its surroundings.However,continuum robots possess theoretically infinite degrees of freedom,and this high flexibility usually leads to complex deformations when subjected to external forces and positional constraints.Describing these complex deformations is the main challenge in modeling continuum robots.In this study,we investigated a novel variable curvature modeling method for continuum robots,considering external forces and positional constraints.The robot configuration curve is described using the developed mechanical model,and then the robot is fitted to the curve.A ten-section continuum robot prototype with a length of 1 m was developed in order to validate the model.The feasibility and accuracy of the model were verified by the ability of the robot to reach target points and track complex trajectories with a load.This work was able to serve as a new perspective for the design analysis and motion control of continuum robots.展开更多
The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a ...The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking accuracy.An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed.The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set.Furthermore,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is proposed.For the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.展开更多
An optimal control procedure is developed for the front and rear wheels of a three-axle vehicle moving on a complex typical road based on model following variable structure control strategy. The actual vehicle may be ...An optimal control procedure is developed for the front and rear wheels of a three-axle vehicle moving on a complex typical road based on model following variable structure control strategy. The actual vehicle may be considered as an uncertain system. Cornering stiffness of front and rear wheels and external disturbances are varied in a limited range. The model-following variable structure control method is used to control both front and rear wheels steering operations of the vehicle, so that steering responses of the vehicle follow from those of the reference model. By numerical results obtained from computer simulation, it is demonstrated that the control system model can cope with the effects of parameter perturbations and outside disturbances.展开更多
Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the probl...Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the problem of 3D target tracking with strong maneuverability, on the basis of the modified three-dimensional variable turn (3DVT) model, an adaptive tracking algorithm is proposed by combining with the cubature Kalman filter (CKF) in this paper. Through ideology of real-time identification, the parameters of the model are changed to adjust the state transition matrix and the state noise covariance matrix. Therefore, states of the target are matched in real-time to achieve the purpose of adaptive tracking. Finally, four simulations are analyzed in different settings by the Monte Carlo method. All results show that the proposed algorithm can update parameters of the model and identify motion characteristics in real-time when targets tracking also has a better tracking accuracy.展开更多
The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an...The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an inaccurate pose estimation or failed tracking.To address the challenge above,a new method of feature point matching is proposed in this paper,which combines the variable velocity model with the reverse optical flow method.First,the constant velocity model is extended to a new variable velocity model,and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method.Then the search range of feature points is accurately determined according to the results of the reverse optical flow method,thereby improving the accuracy and reliability of feature matching,with strengthened interframe tracking effects.Finally,we tested on TUM data set based on the RGB-D camera.Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM(Simultaneous Localization and Mapping)systems.Compared with the traditional ORB-SLAM2,the test error of this method on each sequence in the TUM data set is significantly reduced,and the root mean square error is only 63.8%of the original system under the optimal condition.展开更多
Cosmic inflation is considered assuming a cosmologically varying Newtonian gravitational constant, <em>G.</em> Utilizing two specific models for, <em>G</em><sup>-1</sup>(a), where, ...Cosmic inflation is considered assuming a cosmologically varying Newtonian gravitational constant, <em>G.</em> Utilizing two specific models for, <em>G</em><sup>-1</sup>(a), where, a, is the cosmic scale parameter, we find that the Hubble parameter, <em>H</em>, at inception of <em style="white-space:normal;">G</em><sup style="white-space:normal;">-1</sup>, may be as high as 7.56 E53 km/(s Mpc) for model A, or, 8.55 E53 km/(s Mpc) for model B, making these good candidates for inflation. The Hubble parameter is inextricably linked to <em>G</em> by Friedmanns’ equation, and if <em>G</em> did not exist prior to an inception temperature, then neither did expansion. The CBR temperatures at inception of <em style="white-space:normal;">G</em><sup style="white-space:normal;">-1</sup> are estimated to equal, 6.20 E21 Kelvin for model A, and 7.01 E21 for model B, somewhat lower than CBR temperatures usually associated with inflation. These temperatures would fix the size of Lemaitre universe in the vicinity of 3% of the Earths’ radius at the beginning of expansion, thus avoiding a singularity, as is the case in the ΛCDM model. In the later universe, a variable<em> G </em>model cannot be dismissed based on SNIa events. In fact, there is now some compelling astronomical evidence, using rise times and luminosity, which we discuss, where it could be argued that SNIa events can only be used as good standard candles if a variation in <em>G</em> is taken into account. Dark energy may have more to do with a weakening <em>G</em> with increasing cosmological time, versus an unanticipated acceleration of the universe, in the late stage of cosmic evolution.展开更多
We investigate the continuous variable quomtum teleportation in atmosphere channels. The beam-wandering mode/is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is dete...We investigate the continuous variable quomtum teleportation in atmosphere channels. The beam-wandering mode/is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is deterministic by increasing the aperture size of the detecting device and one is probabilistic by entanglement distillation, are proposed to improve the teleportation fidelity in the presence of atmosphere noises.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
Land surface evapotranspiration(ET)is a critical component in the hydrological cycle but has not well been understood in data-scarce areas especially in river basins,like Nujiang River(NRB)which is characterized by la...Land surface evapotranspiration(ET)is a critical component in the hydrological cycle but has not well been understood in data-scarce areas especially in river basins,like Nujiang River(NRB)which is characterized by large elevation gradient and different vegetation zones with complex processes of water and energy exchange.The quality of ET from optical remote sensing is constrained by cloud cover which is common in the NRB in the monsoon seasons.To understand factors controlling the spatial-temporal heterogeneity of ET in NRB,we employed the Variable Infiltration Capacity(VIC)hydrological model by parameter optimization with support of quality controlled remote sensing ET product and observed river runoff series in the river.The modeled ET has increased during 1984-2018,which might be one of the reasons for the runoff decrease but precipitation increase in the same period.ET increase and runoff decrease tended to be quicker within altitudinal band of 2000-4000 m than in other areas in NRB.We observed that ET variation in different climatic zones were controlled by different factors.ET is generally positively correlated with precipitation,temperature,and shortwave radiation but negatively with relative humidity.In the Tundra Climate(Et)zone in the upper reach of NRB,ET is controlled by precipitation,while it is controlled by shortwave radiation in the snow climate with dry winter(Dw)zone.ET increase is influenced by the increase of temperature,wind speed,and shortwave radiation in the middle and downstream of NRB with warm temperate climate,fully humid(Cf)and warm temperate climate with dry winter(Cw).展开更多
Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservati...Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.展开更多
We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckion...We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two varying G models, where G, is Newton’s constant, and G<sup>-1</sup>, increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if G increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, ± the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low CMB temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.展开更多
Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention ...Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.展开更多
This paper describes a flood routing method applied in an ungauged basin, utilizing the Muskingum model with variable parameters of wave travel time K and weight coefficient of discharge x based on the physical charac...This paper describes a flood routing method applied in an ungauged basin, utilizing the Muskingum model with variable parameters of wave travel time K and weight coefficient of discharge x based on the physical characteristics of the river reach and flood, including the reach slope, length, width, and flood discharge. Three formulas for estimating parameters of wide rectangular, triangular, and parabolic cross sections are proposed. The influence of the flood on channel flow routing parameters is taken into account. The HEC-HMS hydrological model and the geospatial hydrologic analysis module HEC-GeoHMS were used to extract channel or watershed characteristics and to divide sub-basins. In addition, the initial and constant-rate method, user synthetic unit hydrograph method, and exponential recession method were used to estimate runoff volumes, the direct runoff hydrograph, and the baseflow hydrograph, respectively. The Muskingum model with variable parameters was then applied in the Louzigou Basin in Henan Province of China, and of the results, the percentages of flood events with a relative error of peak discharge less than 20% and runoff volume less than 10% are both 100%. They also show that the percentages of flood events with coefficients of determination greater than 0.8 are 83.33%, 91.67%, and 87.5%, respectively, for rectangular, triangular, and parabolic cross sections in 24 flood events. Therefore, this method is applicable to ungauged basins.展开更多
This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algo...This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.展开更多
A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and le...A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task ow...Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.展开更多
基金supported by the National Natural Science Foundation of China(No.51905123)Major Scientific and Technological Innovation Program of Shandong Province,China(Nos.2020CXGC010303,2022ZLGX04)Key R&D Programme of Shandong Province,China(No.2022JMRH0308).
文摘An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.
文摘To design the control system for some homing missile so that the autopilot can transfer guidance command correctly and be robust to disturbances, such as the measurement noises and parameter variation caused by areodynamic floating. The model reference adaptive control was combined with the variable structure control to design a model reference variable structure (MRVS) control system whose control structure is simple and can be realized easily. The simulation results indicate that MRVS can complete the task of transferring guidance command and suppress the distrubances effectively.
文摘The case variable model is put forward by analyzing the system model and the IDEFO model. The component variables of the machining system are classified into four types, I. E uncontrolled variables, process variables, controlled variables and output variables. The process of building the case base is given. The high-speedcutting data base system is developed based on the presented variable model.
基金funding supported by National Natural Science Foundation of China(No.52175285)Beijing Municipal Natural Science Foundation(No.3182025)+1 种基金National Defense Science and Technology Rapid support Project(No.61409230113)Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB and Fundamental Research Funds for the Central Universities(No.FRFBD-20-08A,FRF-TP-20-009A2)。
文摘In this paper, a unified internal state variable(ISV) model for predicting microstructure evolution during hot working process of AZ80 magnesium alloy was developed. A novel aspect of the proposed model is that the interactive effects of material hardening, recovery and dynamic recrystallization(DRX) on the characteristic deformation behavior were considered by incorporating the evolution laws of viscoplastic flow, dislocation activities, DRX nucleation and boundary migration in a coupled manner. The model parameters were calibrated based on the experimental data analysis and genetic algorithm(GA) based objective optimization. The predicted flow stress, DRX fraction and average grain size match well with experimental results. The proposed model was embedded in the finite element(FE) software DEFORM-3 D via user defined subroutine to simulate the hot compression and equal channel angular extrusion(ECAE) processes. The heterogeneous microstructure distributions at different deformation zones and the dislocation density evolution with competitive deformation mechanisms were captured.This study can provide a theoretical solution for the hot working problems of magnesium alloy.
基金Supported by National Natural Science Foundation of China(Grant Nos.51975566,61821005,U1908214)Key Research Program of Frontier Sciences,CAS,China(Grant No.ZDBS-LY-JSC011).
文摘The inherent compliance of continuum robots holds great promise in the fields of soft manipulation and safe human–robot interaction.This compliance reduces the risk of damage to the manipulated object and its surroundings.However,continuum robots possess theoretically infinite degrees of freedom,and this high flexibility usually leads to complex deformations when subjected to external forces and positional constraints.Describing these complex deformations is the main challenge in modeling continuum robots.In this study,we investigated a novel variable curvature modeling method for continuum robots,considering external forces and positional constraints.The robot configuration curve is described using the developed mechanical model,and then the robot is fitted to the curve.A ten-section continuum robot prototype with a length of 1 m was developed in order to validate the model.The feasibility and accuracy of the model were verified by the ability of the robot to reach target points and track complex trajectories with a load.This work was able to serve as a new perspective for the design analysis and motion control of continuum robots.
基金the Joint Fund of Advanced Aerospace Manufacturing Technology Research(No.2017-JCJQ-ZQ-031)。
文摘The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking accuracy.An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed.The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set.Furthermore,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is proposed.For the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.
文摘An optimal control procedure is developed for the front and rear wheels of a three-axle vehicle moving on a complex typical road based on model following variable structure control strategy. The actual vehicle may be considered as an uncertain system. Cornering stiffness of front and rear wheels and external disturbances are varied in a limited range. The model-following variable structure control method is used to control both front and rear wheels steering operations of the vehicle, so that steering responses of the vehicle follow from those of the reference model. By numerical results obtained from computer simulation, it is demonstrated that the control system model can cope with the effects of parameter perturbations and outside disturbances.
基金supported by the National Natural Science Foundation of China(51467013)
文摘Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the problem of 3D target tracking with strong maneuverability, on the basis of the modified three-dimensional variable turn (3DVT) model, an adaptive tracking algorithm is proposed by combining with the cubature Kalman filter (CKF) in this paper. Through ideology of real-time identification, the parameters of the model are changed to adjust the state transition matrix and the state noise covariance matrix. Therefore, states of the target are matched in real-time to achieve the purpose of adaptive tracking. Finally, four simulations are analyzed in different settings by the Monte Carlo method. All results show that the proposed algorithm can update parameters of the model and identify motion characteristics in real-time when targets tracking also has a better tracking accuracy.
基金This work was supported by The National Natural Science Foundation of China under Grant No.61304205 and NO.61502240The Natural Science Foundation of Jiangsu Province under Grant No.BK20191401 and No.BK20201136Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.SJCX21_0364 and No.SJCX21_0363.
文摘The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an inaccurate pose estimation or failed tracking.To address the challenge above,a new method of feature point matching is proposed in this paper,which combines the variable velocity model with the reverse optical flow method.First,the constant velocity model is extended to a new variable velocity model,and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method.Then the search range of feature points is accurately determined according to the results of the reverse optical flow method,thereby improving the accuracy and reliability of feature matching,with strengthened interframe tracking effects.Finally,we tested on TUM data set based on the RGB-D camera.Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM(Simultaneous Localization and Mapping)systems.Compared with the traditional ORB-SLAM2,the test error of this method on each sequence in the TUM data set is significantly reduced,and the root mean square error is only 63.8%of the original system under the optimal condition.
文摘Cosmic inflation is considered assuming a cosmologically varying Newtonian gravitational constant, <em>G.</em> Utilizing two specific models for, <em>G</em><sup>-1</sup>(a), where, a, is the cosmic scale parameter, we find that the Hubble parameter, <em>H</em>, at inception of <em style="white-space:normal;">G</em><sup style="white-space:normal;">-1</sup>, may be as high as 7.56 E53 km/(s Mpc) for model A, or, 8.55 E53 km/(s Mpc) for model B, making these good candidates for inflation. The Hubble parameter is inextricably linked to <em>G</em> by Friedmanns’ equation, and if <em>G</em> did not exist prior to an inception temperature, then neither did expansion. The CBR temperatures at inception of <em style="white-space:normal;">G</em><sup style="white-space:normal;">-1</sup> are estimated to equal, 6.20 E21 Kelvin for model A, and 7.01 E21 for model B, somewhat lower than CBR temperatures usually associated with inflation. These temperatures would fix the size of Lemaitre universe in the vicinity of 3% of the Earths’ radius at the beginning of expansion, thus avoiding a singularity, as is the case in the ΛCDM model. In the later universe, a variable<em> G </em>model cannot be dismissed based on SNIa events. In fact, there is now some compelling astronomical evidence, using rise times and luminosity, which we discuss, where it could be argued that SNIa events can only be used as good standard candles if a variation in <em>G</em> is taken into account. Dark energy may have more to do with a weakening <em>G</em> with increasing cosmological time, versus an unanticipated acceleration of the universe, in the late stage of cosmic evolution.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11574400,U1304613,11204197,11204379and 11074244
文摘We investigate the continuous variable quomtum teleportation in atmosphere channels. The beam-wandering mode/is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is deterministic by increasing the aperture size of the detecting device and one is probabilistic by entanglement distillation, are proposed to improve the teleportation fidelity in the presence of atmosphere noises.
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(42171129)the second Tibetan Plateau Scientific Expedition and Research(2019QZKK0208)Yunnan University Talent Introduction Research Project(YJRC3201702)。
文摘Land surface evapotranspiration(ET)is a critical component in the hydrological cycle but has not well been understood in data-scarce areas especially in river basins,like Nujiang River(NRB)which is characterized by large elevation gradient and different vegetation zones with complex processes of water and energy exchange.The quality of ET from optical remote sensing is constrained by cloud cover which is common in the NRB in the monsoon seasons.To understand factors controlling the spatial-temporal heterogeneity of ET in NRB,we employed the Variable Infiltration Capacity(VIC)hydrological model by parameter optimization with support of quality controlled remote sensing ET product and observed river runoff series in the river.The modeled ET has increased during 1984-2018,which might be one of the reasons for the runoff decrease but precipitation increase in the same period.ET increase and runoff decrease tended to be quicker within altitudinal band of 2000-4000 m than in other areas in NRB.We observed that ET variation in different climatic zones were controlled by different factors.ET is generally positively correlated with precipitation,temperature,and shortwave radiation but negatively with relative humidity.In the Tundra Climate(Et)zone in the upper reach of NRB,ET is controlled by precipitation,while it is controlled by shortwave radiation in the snow climate with dry winter(Dw)zone.ET increase is influenced by the increase of temperature,wind speed,and shortwave radiation in the middle and downstream of NRB with warm temperate climate,fully humid(Cf)and warm temperate climate with dry winter(Cw).
文摘Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.
文摘We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two varying G models, where G, is Newton’s constant, and G<sup>-1</sup>, increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if G increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, ± the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low CMB temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
基金supported by the Technological Fund Item of China University of Mining and Technology (Grant No. OF4533)the Key Research Project of the Water Resources Department of Henan Province
文摘This paper describes a flood routing method applied in an ungauged basin, utilizing the Muskingum model with variable parameters of wave travel time K and weight coefficient of discharge x based on the physical characteristics of the river reach and flood, including the reach slope, length, width, and flood discharge. Three formulas for estimating parameters of wide rectangular, triangular, and parabolic cross sections are proposed. The influence of the flood on channel flow routing parameters is taken into account. The HEC-HMS hydrological model and the geospatial hydrologic analysis module HEC-GeoHMS were used to extract channel or watershed characteristics and to divide sub-basins. In addition, the initial and constant-rate method, user synthetic unit hydrograph method, and exponential recession method were used to estimate runoff volumes, the direct runoff hydrograph, and the baseflow hydrograph, respectively. The Muskingum model with variable parameters was then applied in the Louzigou Basin in Henan Province of China, and of the results, the percentages of flood events with a relative error of peak discharge less than 20% and runoff volume less than 10% are both 100%. They also show that the percentages of flood events with coefficients of determination greater than 0.8 are 83.33%, 91.67%, and 87.5%, respectively, for rectangular, triangular, and parabolic cross sections in 24 flood events. Therefore, this method is applicable to ungauged basins.
基金Foundation item： Supported by the National Nature Science Foundation of China （No. 61074053, 61374114） and the Applied Basic Research Program of Ministry of Transport of China （No. 2011-329-225 -390）.
文摘This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.
文摘A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.
文摘Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.
