Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient...Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.展开更多
The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable...The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable harness design is presented.The model,based on continuum mechanics,is established by analyzing the force of microelement in equilibrium.During the analysis procedure,three coordinate systems：inertial,Frenet and main-axis coordinate systems are used.By variable substitution and dimensionless processing,the equation set is discretized by differential quadrature method and subsequently becomes an overdetermined nonlinear equation set with boundary conditions solved by Levenberg-Marquardt method.With the profile of motional cable harness obtained from the integral of arithmetic solution,a motion simulation system based on＂path＂and＂profile＂as well as the experimental equipments is built.Using the same parameters as input for the simulation and the real cable harness correspondingly,the issue in designing,such as collision,can be easily found by the simulation system.This research obtains a better result which has no potential collisions by redesign,and the proposed method can be used as an accurate and efficient way in motional cable harness design work.展开更多
As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-...As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-sampled geometry is becoming ubiquitous in graphics and geometric information processing, and poses new challenges which have not been fully resolved by the state-of-art graphical techniques. In this paper, we address the challenges by proposing a meshless computational framework for dynamic modeling and simulation of solids and thin-shells represented as point sam- ples. Our meshless framework can directly compute the elastic deformation and fracture propagation for any scanned point geometry, without the need of converting them to polygonal meshes or higher order spline representations. We address the necessary computational techniques, such as Moving Least Squares, Hierarchical Discretization, and Modal Warping, to effectively and efficiently compute the physical simulation in real-time. This meahless computational framework aims to bridge the gap between the point-sampled geometry with physics-based modeling and simulation governed by partial differential equations.展开更多
The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,ex...The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.展开更多
A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. Physics-based modeling incorporate physical laws into shape representation ...A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. Physics-based modeling incorporate physical laws into shape representation to provide direct shape interaction. The combination presents a new method inherits the attractive properties of the manifold surface as well as that of the physics-based models.展开更多
The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical mod...The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.展开更多
The instantaneous reversible soft logic upset induced by the electromagnetic interference（EMI） severely affects the performances and reliabilities of complementary metal–oxide–semiconductor（CMOS） inverters. This...The instantaneous reversible soft logic upset induced by the electromagnetic interference（EMI） severely affects the performances and reliabilities of complementary metal–oxide–semiconductor（CMOS） inverters. This kind of soft logic upset is investigated in theory and simulation. Physics-based analysis is performed, and the result shows that the upset is caused by the non-equilibrium carrier accumulation in channels, which can ultimately lead to an abnormal turn-on of specific metal–oxide–semiconductor field-effect transistor（MOSFET） in CMOS inverter. Then a soft logic upset simulation model is introduced. Using this model, analysis of upset characteristic reveals an increasing susceptibility under higher injection powers, which accords well with experimental results, and the influences of EMI frequency and device size are studied respectively using the same model. The research indicates that in a range from L waveband to C waveband, lower interference frequency and smaller device size are more likely to be affected by the soft logic upset.展开更多
A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional stream...A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional streamwise velocity fluctuations, taking account of different instability modes. The fluctuation velocity components normal to the streamwise direction are modeled by another transport equation. Transition is triggered automatically with the development of the pretransitional velocity fluctuations. In the fully turbulent region, the model reverts to the k-ω turbulence model. Different test cases, including subsonic, supersonic and hypersonic flows around flat plates, airfoils and straight cones, are numerically simulated to validate the performance of the model. The results demonstrate the excellent predictive capabilities of the model in different paths of transition. The model can serve as a basis for the extension of additional transition mechanisms,such as rotation and curvature effects, roughness-induced transition and crossflow-induced transition.展开更多
Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on mo...Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.展开更多
Two mainstream approaches for solving inverse sample reconstruction problems in programmable illumination computational microscopy rely on either deep models or physical models.Solutions based on physical models posse...Two mainstream approaches for solving inverse sample reconstruction problems in programmable illumination computational microscopy rely on either deep models or physical models.Solutions based on physical models possess strong generalization capabilities while struggling with global optimization of inverse problems due to a lack of sufficient physical constraints.In contrast,deep-learning methods have strong problem-solving abilities,but their generalization ability is often questioned because of the unclear physical principles.In addition,conventional deep models are difficult to apply to some specific scenes because of the difficulty in acquiring high-quality training data and their limited capacity to generalize across different scenarios.To combine the advantages of deep models and physical models together,we propose a hybrid framework consisting of three subneural networks(two deep-learning networks and one physics-based network).We first obtain a result with rich semantic information through a light deeplearning neural network and then use it as the initial value of the physical network to make its output comply with physical process constraints.These two results are then used as the input of a fusion deeplearning neural work that utilizes the paired features between the reconstruction results of two different models to further enhance imaging quality.The proposed hybrid framework integrates the advantages of both deep models and physical models and can quickly solve the computational reconstruction inverse problem in programmable illumination computational microscopy and achieve better results.We verified the feasibility and effectiveness of the proposed hybrid framework with theoretical analysis and actual experiments on resolution targets and biological samples.展开更多
Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propag...Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propagation in a three-dimensional(3D)regional-scale geophysics model and the finite element method(FEM)for fine simulation of structural response including soil-structure interaction,and performs a physics-based simulation from initial fault rupture on an ancient wood structure.After verification of the hybrid workflow,a large-scale model of an ancient wood structure in the Beijing area,The Tower of Buddhist Incense,is established and its responses under the 1665 Tongxian earthquake and the 1730 Yiheyuan earthquake are simulated.The results from the simulated ground motion and seismic response of the wood structure under the two earthquakes demonstrate that this hybrid workflow can be employed to efficiently provide insight into the relationships between geophysical parameters and the structural response,and is of great significance toward accurate input for seismic simulation of structures under specific site and fault conditions.展开更多
Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,...Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.展开更多
A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,t...A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.展开更多
Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-bas...Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-based model according to the characteristics of the surfaceto be reconstructed in RE. At last, the intensity diagrams of reflected field distribution areprovided to prove the feasibility of the presented model and some spheres are rendered with thismodel.展开更多
A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques inclu...A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques include obtaining gravity data from the Gravity Field and Steady-State Ocean Circulation Explorer(GOCE),creating seismic profiles,analyzing the receiver functions of seismic data,obtaining information from boreholes,and providing geological interpretations.GOCE satellite gravity data were processed to construct a preliminary model based on nonlinear inversions of the data.A regional crustal thickness model was developed using receiver functions,seismic refraction profiles,and geological insights.The inverted model was validated using borehole data and compared with seismic estimates.The model exhibited strong consistency and revealed a correlation between crustal thickness,geology,and tectonics of Egypt.It showed that the shallowest depths of the Moho are located in the north along the Mediterranean Sea and in the eastern part along the Red Sea,reflecting an oceanic plate with a thin,high-density crust.The deepest Moho depths are located in the southwestern part of Egypt,Red Sea coastal mountains,and Sinai Peninsula.The obtained 3D model of crustal thickness provided finely detailed Moho depth estimates that aligned closely with geology and tectonic characteristics of Egypt,contributing valuable insights into the subsurface structure and tectonic processes of region.展开更多
Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism rem...Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.展开更多
In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis...In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.展开更多
Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model...Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.展开更多
To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precis...To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.展开更多
基金supported by the Natural Science Foundation of China(Grant Nos.42088101 and 42205149)Zhongwang WEI was supported by the Natural Science Foundation of China(Grant No.42075158)+1 种基金Wei SHANGGUAN was supported by the Natural Science Foundation of China(Grant No.41975122)Yonggen ZHANG was supported by the National Natural Science Foundation of Tianjin(Grant No.20JCQNJC01660).
文摘Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.
基金Supported by National Natural Science Foundation of China(Grant No.51275047)
文摘The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable harness design is presented.The model,based on continuum mechanics,is established by analyzing the force of microelement in equilibrium.During the analysis procedure,three coordinate systems：inertial,Frenet and main-axis coordinate systems are used.By variable substitution and dimensionless processing,the equation set is discretized by differential quadrature method and subsequently becomes an overdetermined nonlinear equation set with boundary conditions solved by Levenberg-Marquardt method.With the profile of motional cable harness obtained from the integral of arithmetic solution,a motion simulation system based on＂path＂and＂profile＂as well as the experimental equipments is built.Using the same parameters as input for the simulation and the real cable harness correspondingly,the issue in designing,such as collision,can be easily found by the simulation system.This research obtains a better result which has no potential collisions by redesign,and the proposed method can be used as an accurate and efficient way in motional cable harness design work.
基金Supported by the National Science Foundation (Grant Nos. CCF-0727098, IIS-0710819)
文摘As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-sampled geometry is becoming ubiquitous in graphics and geometric information processing, and poses new challenges which have not been fully resolved by the state-of-art graphical techniques. In this paper, we address the challenges by proposing a meshless computational framework for dynamic modeling and simulation of solids and thin-shells represented as point sam- ples. Our meshless framework can directly compute the elastic deformation and fracture propagation for any scanned point geometry, without the need of converting them to polygonal meshes or higher order spline representations. We address the necessary computational techniques, such as Moving Least Squares, Hierarchical Discretization, and Modal Warping, to effectively and efficiently compute the physical simulation in real-time. This meahless computational framework aims to bridge the gap between the point-sampled geometry with physics-based modeling and simulation governed by partial differential equations.
基金supported by the National Natural Science Foundation of China(grant no.32201020)the general program(grant no.2023A04J0083)+1 种基金the Guangzhou–HKUST(GZ)joint funding program(grant no.2023A03J0060)of Guangzhou Municipal Science and Technology Projectfunded by the Municipal Key Laboratory Construction Program of Guangzhou Municipal Science and Technology Project(grant no.2023A03J0003).
文摘The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.
基金Funded by the Chinese National Natural Science Foundation (No.50105013).
文摘A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. Physics-based modeling incorporate physical laws into shape representation to provide direct shape interaction. The combination presents a new method inherits the attractive properties of the manifold surface as well as that of the physics-based models.
基金supported by the Open Project of Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle(No.ZDSYS202304)the National Natural Science Foundation of China(No.62303007)the Anhui Provincial Natural Science Foundation(No.2308085ME142)。
文摘The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.60776034)the Open Fund of Key Laboratory of Complex Electromagnetic Environment Science and Technology,China Academy of Engineering Physics(Grant No.2015-0214.XY.K)
文摘The instantaneous reversible soft logic upset induced by the electromagnetic interference（EMI） severely affects the performances and reliabilities of complementary metal–oxide–semiconductor（CMOS） inverters. This kind of soft logic upset is investigated in theory and simulation. Physics-based analysis is performed, and the result shows that the upset is caused by the non-equilibrium carrier accumulation in channels, which can ultimately lead to an abnormal turn-on of specific metal–oxide–semiconductor field-effect transistor（MOSFET） in CMOS inverter. Then a soft logic upset simulation model is introduced. Using this model, analysis of upset characteristic reveals an increasing susceptibility under higher injection powers, which accords well with experimental results, and the influences of EMI frequency and device size are studied respectively using the same model. The research indicates that in a range from L waveband to C waveband, lower interference frequency and smaller device size are more likely to be affected by the soft logic upset.
基金supported by grants from the National Natural Science Foundation of China(No.11721202)the Academic Excellence Foundation of Beihang University for Ph D Students,China。
文摘A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional streamwise velocity fluctuations, taking account of different instability modes. The fluctuation velocity components normal to the streamwise direction are modeled by another transport equation. Transition is triggered automatically with the development of the pretransitional velocity fluctuations. In the fully turbulent region, the model reverts to the k-ω turbulence model. Different test cases, including subsonic, supersonic and hypersonic flows around flat plates, airfoils and straight cones, are numerically simulated to validate the performance of the model. The results demonstrate the excellent predictive capabilities of the model in different paths of transition. The model can serve as a basis for the extension of additional transition mechanisms,such as rotation and curvature effects, roughness-induced transition and crossflow-induced transition.
基金The Chinese team is supported by the National Natural Science Foundation of China(62076150,62173216,61903226)the Taishan Scholar Project of Shandong Province(TSQN201812092)+2 种基金the Key Research and Development Program of Shandong Province(2019GGX101072,2019JZZY010115)the Youth Innovation Technology Project of Higher School in Shandong Province(2019KJN005)the Key Research and Development Program of Shandong Province(2019JZZY010115)。
文摘Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.
基金supported by the National Natural Science Foundation of China(Grant No.62275020).
文摘Two mainstream approaches for solving inverse sample reconstruction problems in programmable illumination computational microscopy rely on either deep models or physical models.Solutions based on physical models possess strong generalization capabilities while struggling with global optimization of inverse problems due to a lack of sufficient physical constraints.In contrast,deep-learning methods have strong problem-solving abilities,but their generalization ability is often questioned because of the unclear physical principles.In addition,conventional deep models are difficult to apply to some specific scenes because of the difficulty in acquiring high-quality training data and their limited capacity to generalize across different scenarios.To combine the advantages of deep models and physical models together,we propose a hybrid framework consisting of three subneural networks(two deep-learning networks and one physics-based network).We first obtain a result with rich semantic information through a light deeplearning neural network and then use it as the initial value of the physical network to make its output comply with physical process constraints.These two results are then used as the input of a fusion deeplearning neural work that utilizes the paired features between the reconstruction results of two different models to further enhance imaging quality.The proposed hybrid framework integrates the advantages of both deep models and physical models and can quickly solve the computational reconstruction inverse problem in programmable illumination computational microscopy and achieve better results.We verified the feasibility and effectiveness of the proposed hybrid framework with theoretical analysis and actual experiments on resolution targets and biological samples.
基金National Natural Science Foundation of China under Grant Nos.52108468 and 52178495。
文摘Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propagation in a three-dimensional(3D)regional-scale geophysics model and the finite element method(FEM)for fine simulation of structural response including soil-structure interaction,and performs a physics-based simulation from initial fault rupture on an ancient wood structure.After verification of the hybrid workflow,a large-scale model of an ancient wood structure in the Beijing area,The Tower of Buddhist Incense,is established and its responses under the 1665 Tongxian earthquake and the 1730 Yiheyuan earthquake are simulated.The results from the simulated ground motion and seismic response of the wood structure under the two earthquakes demonstrate that this hybrid workflow can be employed to efficiently provide insight into the relationships between geophysical parameters and the structural response,and is of great significance toward accurate input for seismic simulation of structures under specific site and fault conditions.
基金supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018)the National Natural Science Foundation of China(grant Nos.42188101,42130204)+4 种基金the B-type Strategic Priority Program of CAS(grant no.XDB41000000)the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301)the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002)the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01)the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.
文摘Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.
基金Supported by National Natural Science Foundation of China(61911530398,12231012)Consultancy Project by the Chinese Academy of Engineering(2022-JB-06,2023-JB-12)+3 种基金the Natural Science Foundation of Fujian Province of China(2021J01621)Special Projects of the Central Government Guiding Local Science and Technology Development(2021L3018)Royal Society of Edinburgh(RSE1832)Engineering and Physical Sciences Research Council(EP/W522521/1).
文摘A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.
文摘Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-based model according to the characteristics of the surfaceto be reconstructed in RE. At last, the intensity diagrams of reflected field distribution areprovided to prove the feasibility of the presented model and some spheres are rendered with thismodel.
文摘A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques include obtaining gravity data from the Gravity Field and Steady-State Ocean Circulation Explorer(GOCE),creating seismic profiles,analyzing the receiver functions of seismic data,obtaining information from boreholes,and providing geological interpretations.GOCE satellite gravity data were processed to construct a preliminary model based on nonlinear inversions of the data.A regional crustal thickness model was developed using receiver functions,seismic refraction profiles,and geological insights.The inverted model was validated using borehole data and compared with seismic estimates.The model exhibited strong consistency and revealed a correlation between crustal thickness,geology,and tectonics of Egypt.It showed that the shallowest depths of the Moho are located in the north along the Mediterranean Sea and in the eastern part along the Red Sea,reflecting an oceanic plate with a thin,high-density crust.The deepest Moho depths are located in the southwestern part of Egypt,Red Sea coastal mountains,and Sinai Peninsula.The obtained 3D model of crustal thickness provided finely detailed Moho depth estimates that aligned closely with geology and tectonic characteristics of Egypt,contributing valuable insights into the subsurface structure and tectonic processes of region.
文摘Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.
文摘In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.
基金supported by the National Key R&D Program of China (Grant No.2022YFF0503700)the National Natural Science Foundation of China (42074196, 41925018)
文摘Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.
基金Yongxian Huang supported by Projects of Guangzhou Science and Technology Plan(2023A04J0409)。
文摘To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.
