Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient n...Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.展开更多
The medium for next-generation communication is considered as fiber for fast,secure communication and switching capability.Mode division and space division multiplexing provide an excellent switching capability with h...The medium for next-generation communication is considered as fiber for fast,secure communication and switching capability.Mode division and space division multiplexing provide an excellent switching capability with high data transmission rate.In this work,the authors have approached an inverse modeling technique using regression-based machine learning to design a weakly coupled few-mode fiber for facilitating mode division multiplexing.The technique is adapted to predict the accurate profile parameters for the proposed few-mode fiber to obtain the maximum number of modes.It is for a three-ring-core few-mode fiber for guiding five,ten,fifteen,and twenty modes.Three types of regression models namely ordinary least-square linear multi-output regression,k-nearest neighbors of multi-output regression,and ID3 algorithm-based decision trees for multi-output regression are used for predicting the multiple profile parameters.It is observed that the ID3-based decision tree for multioutput regression is the robust,highly-accurate machine learning model for fast modeling of FMFs.The proposed fiber claims to be an efficient candidate for the next-generation 5G and 6G backhaul networks using mode division multiplexing.展开更多
Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation.To bridge the...Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation.To bridge the technical gap between virtual and real environments,we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans,taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner.First,we infer hierarchical geometry using two networks,which are optimized via the differentiable renderer.We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model.Then,ocean dynamics can be evolved using the reconstructed wave components.Through extensive experiments,we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation.Moreover,the new framework has the inverse modeling potential to facilitate a host of graphics applications,such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.展开更多
In multi-component oil and gas exploration using ocean bottom nodes,converted wave data is rich in lithological and fracture information.One of the urgent problems to be solved is how to construct an accurate shear wa...In multi-component oil and gas exploration using ocean bottom nodes,converted wave data is rich in lithological and fracture information.One of the urgent problems to be solved is how to construct an accurate shear wave velocity model of the shallow sea bottom by leveraging the seismic wave information at the fluid-solid interface in the ocean,and improve the lateral resolution of marine converted wave data.Given that the dispersion characteristics of surface waves are sensitive to the S-wave velocity of subsurface media,and that Scholte surface waves,which propagate at the interface between liquid and solid media,exist in the data of marine oil and gas exploration,this paper proposes a Scholte wave inversion and modeling method based on oil and gas exploration using ocean bottom nodes.By using the method for calculating the Scholte wave dispersion spectrum based on the Bessel kernel function,the accuracy of dispersion spectrum analysis is improved,and more accurate dispersion curves are picked up.Through the adaptive weighted least squares Scholte wave dispersion inversion algorithm,the Scholte wave dispersion equation for liquid-solid media is solved,and the shear wave velocity model of the shallow sea bottom is calculated.Theoretical tests and applications of realdata have proven that this method can significantly improve the lateral resolution of converted wave data,provide high-quality data for subsequent inversion of marine multi-component oil and gas exploration data and reservoir reflection information,and contribute to the development of marine oil and gas exploration technology.展开更多
Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characterist...Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.展开更多
Runoff coefficient is an important parameter for the decision support of urban stormwater management. However, factors like comprehensive land-use type, variable spatial elevation, dynamic rainfall and groundwater ele...Runoff coefficient is an important parameter for the decision support of urban stormwater management. However, factors like comprehensive land-use type, variable spatial elevation, dynamic rainfall and groundwater elevation, make the direct estimation of runoff coefficient difficult. This paper presented a novel method to estimate the urban runoff coefficient using the inverse method, where observed time-series catchment outfall flow volume was employed as input for the water balance model and runoff coefficients of different catchments were treated as unknown parameters. A developed constrained minimization objective function was combined to solve the model and minimized error between observed and modeled outfall flow is satisfactory for the presenting of a set of runoff coefficients. Estimated runoff coefficients for the urban catchments in Shanghai downtown area demonstrated that practice of low impact design could play an important role in reducing the urban runoff.展开更多
Nowadays,building energy models(BEMs)are widely used,particularly in the assessment of energy consumption in buildings to address the potential savings that can be generated.The realisation of a dynamic energy model b...Nowadays,building energy models(BEMs)are widely used,particularly in the assessment of energy consumption in buildings to address the potential savings that can be generated.The realisation of a dynamic energy model based on high-fidelity physics(white-box models)requires a tuning process to fit the model to reality,due to many uncertainties involved.Currently some research trends try to reduce this performance gap by modulating different types of experimental parameters such as:capacitances or infiltration.The EnergyPlus simulation software,in its latest versions,has implemented an object:HybridModel:Zone that calculates the infiltration and internal mass of buildings using an inverse modelling approach that employs only the measured indoor temperature data to invert the heat balance equation for the zone under study.The main objective of this paper is to reduce the execution time and uncertainties in the development of quality energy models by generating a new calibration methodology that implements this approach.This uses,as a starting point,a research created by the authors of this study,which was empirically and comparatively validated against the energy models developed by the participants in Annex 58.It is also worth highlighting the empirical validation of the HybridModel:Zone object,since it was activated in all scenarios where its execution is possible:periods of seven days or more of free oscillation and periods in which the building is under load.The findings are promising.The data generated with the new methodology,if compared with those produced by the baseline model,improve their resemblance to the real ones by 22.9%.While those of its predecessor did it by 15.6%.For this study,the two dwellings foreseen in Annex 58 of the IEA ECB project have been modelled and their real monitoring data have been used.展开更多
Inverse models can be used to estimate surface fluxes in terms of the observed atmospheric concentration measurement data.This paper proposes a new nonparametric spatio-temporal inverse model and provides the global e...Inverse models can be used to estimate surface fluxes in terms of the observed atmospheric concentration measurement data.This paper proposes a new nonparametric spatio-temporal inverse model and provides the global expressions for the estimates by employing the B-spline method.The authors establish the asymptotic normality of the estimators under mild conditions.The authors also conduct numerical studies to evaluate the finite sample performance of the proposed methodologies.Finally,the authors apply the method to anthropogenic carbon dioxide(CO_(2))emission data from different provinces of Canada to illustrate the validity of the proposed techniques.展开更多
This paper presents the estimation of Chinese emissions of HCFC-22 and CFC-11 in 2009 by an inverse modeling method based on in-situ measurement data from the Shangdianzi Global Atmosphere Watch (GAW) Regional Station...This paper presents the estimation of Chinese emissions of HCFC-22 and CFC-11 in 2009 by an inverse modeling method based on in-situ measurement data from the Shangdianzi Global Atmosphere Watch (GAW) Regional Station (SDZ) and atmospheric transport simulations. After inversion (a-posteriori) estimates of the Chinese emissions in 2009 increased by 6.6% for HCFC-22 from 91.7 (± 83.6) to 98.3 (± 47.4) kt/yr and by 22.5% for CFC-11 from 13 (±12.6) to 15.8 (±7.2) kt/yr compared to an a-priori emission. While the model simulation with a-priori emissions already captured the main features of the observed variability at the measurement site, the model performance (in terms of correlation and mean-square-error) improved using a-posteriori emissions. The inversion reduced the root-mean-square (RMS) error by 4% and 10% for HCFC-22 and CFC-11, respectively.展开更多
Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation accuracy.This paper comprehensively reviews the current advances and future prospects o...Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation accuracy.This paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter inversion.Initially,the simulation-optimization parameter estimation framework is introduced,which involves the integration of simulation models with metaheuristic algorithms.The subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms-genetic algorithm(GA),particle swarm optimization(PSO),simulated annealing(SA),and differential evolution(DE)-highlighting their recent applications in water resources research and related areas.Then,a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter inversion.Finally,three noteworthy directions are presented to address the common challenges among current studies,including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms,local approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous pa-rameters.In summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.展开更多
This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application...This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.展开更多
Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a...Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a dominant species of riparian ecosystems in arid lands, Populus euphratica Oliv. is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages. In this study, we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P. euphratica leaves. Both the shapes and biochemical properties of P. euphratica leaves were found to change with the heights from ground surface. The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes, and also better than hyperspectral indices. Similar results were obtained for estimations of equivalent water thickness （EWT） and leaf mass per area （LMA）. Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error （RMSE） and R2 values between those obtained with the two calibration strategies using the inversed PROSPECT model. Hence, the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems, but the calibration to the model requires special attention.展开更多
A new wave energy dissipation structure is proposed, aiming to optimize the dimensions of the structure and make the reflection of the structure maintain a low level within the scope of the known frequency band. An op...A new wave energy dissipation structure is proposed, aiming to optimize the dimensions of the structure and make the reflection of the structure maintain a low level within the scope of the known frequency band. An optimal extended ANFIS model combined with the wave reflection coefficient analysis for the estimation of the structure dimensions is established. In the premise of lower wave reflection coefficient, the specific sizes of the structure are obtained inversely, and the contribution of each related parameter on the structural reflection performance is analyzed. The main influencing factors are determined. It is found that the optimal dimensions of the proposed structure exist, which make the wave absorbing performance of the structure reach a perfect status under a wide wave frequency band.展开更多
Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Dopple...Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Doppler Lidar detection,and combining Dynamic Bayesian Networks(DBN)with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN),this paper proposes a model for the inversion of wake vortex parameters.During the wake vortex flow field simulation analysis,the wind and turbulent environment were initially superimposed onto the simulated wake velocity field.Subsequently,Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training.In the case study validation,real measured data underwent preprocessing and were then input into the established model.This allowed us to construct the wake vortex characteristic parameter inversion model.The final results demonstrated that our model achieved parameter inversion with only minor errors.In a practical example,our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm.This study holds significant promise for real-time monitoring of wake vortices at airports,and is proved a crucial step in developing wake vortex interval standards.展开更多
A model predictive inverse method (MPIM) is presented to estimate the time- and space-dependent heat flux onthe ablated boundary and the ablation velocity of the two-dimensional ablation system. For the method, first ...A model predictive inverse method (MPIM) is presented to estimate the time- and space-dependent heat flux onthe ablated boundary and the ablation velocity of the two-dimensional ablation system. For the method, first of all, therelationship between the heat flux and the temperatures of the measurement points inside the ablation material is establishedby the predictive model based on an influence relationship matrix. Meanwhile, the estimation task is formulated as aninverse heat transfer problem (IHTP) with consideration of ablation, which is described by an objective function of thetemperatures at the measurement point. Then, the rolling optimization is used to solve the IHTP to online estimate theunknown heat flux on the ablated boundary. Furthermore, the movement law of the ablated boundary is reconstructedaccording to the estimation of the boundary heat flux. The effects of the temperature measurement errors, the numberof future time steps, and the arrangement of the measurement points on the estimation results are analyzed in numericalexperiments. On the basis of the numerical results, the effectiveness of the presented method is clarified.展开更多
Inverse technique is a widely used method in oceanography, but it has a problem that the retrieved solutions often violate model prior assumptions. To tune the model has consistent solutions, an iteration approach, wh...Inverse technique is a widely used method in oceanography, but it has a problem that the retrieved solutions often violate model prior assumptions. To tune the model has consistent solutions, an iteration approach, which successively utilizes the posterior statistics for next round inverse estimation, is introduced and tested from a real case study. It is found that the consistency may become elusive as the determinants of solution and noise covariance matrices become zero in the iteration process. However, after several steps of such operation, the difference between posterior statistics and the model prior ones can be gradually reduced.展开更多
This paper is to determine the flow stress curve of 5049-O aluminium alloy by a tube hydraulic bulging test with fixed end-conditions. During this test, several tubular specimens are bulged under different internal pr...This paper is to determine the flow stress curve of 5049-O aluminium alloy by a tube hydraulic bulging test with fixed end-conditions. During this test, several tubular specimens are bulged under different internal pressures before their bursting, and the corresponding bulging height and wall thickness at the pole are measured. An inverse strategy is developed to determine the constitutive parameters of tubular materials based on experimental data, which combines the finite element method with gradient-based optimization techniques. In this scheme, the objective function is formulated with the sum of least squares of the error between numerical and experimental data, and finite difference approximation is used to calculate the gradient. The tubular material behavior is assumed to meet the von Mises yield criterion and Hollomon exponential hardening law. Then, constitutive parameters identification is performed by minimization of the objective function. In order to validate the performance of this framework, identified parameters are compared with those obtained by two types of theoretical models, and tensile tests are performed on specimens cut from the same tubes. The comparison shows that this inverse framework is robust and can achieve a more accurate parameter identification by eliminating mechanical and geometrical assumptions in classical theoretical analysis.展开更多
An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs...An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.展开更多
Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities.In this study,three theoretical models,including linear,polynomial,and...Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities.In this study,three theoretical models,including linear,polynomial,and exponential models,are proposed to inverse the mining-induced stress through the acquisition and analysis of hydraulic support stress and micro-seismicity in the coal mining face.The distribution of mining-induced stress in the coal seam are graphed by fitting two key stress parameters including hydraulic support stress and peak stress,and two key zones including goaf zone and in situ stress zone.These key stress parameters and zones are defined based on the critical nodes of the model curve.According to the geological background of Mataihao coal mine in Erdos,Inner Mongolia Autonomous Region,China,the contours of mining-induced stress are graphed through the stress calculation of these three inversion theoretical models.The multi-monitoring data of micro-seismicity,drilling chips,advanced borehole stress and bolts axial force are used to verify the key stress parameters and zones of the theoretical models.It shows that the monitoring data are in good agreement with the distribution of inversed results.It should be emphasized that,if the fault structure exists around the mining face,the mining-induced stress decreases obviously when the mining face is passing through the faults,and the location of the peak stress will be closer to the mining face.The results in this study could provide methods for early prevention of extreme mining-induced stress and disaster control in the mining activities.展开更多
The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integ...The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management(IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.展开更多
基金This work was supported by National Natural Science Foundation of China(No.60276037).
文摘Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.
文摘The medium for next-generation communication is considered as fiber for fast,secure communication and switching capability.Mode division and space division multiplexing provide an excellent switching capability with high data transmission rate.In this work,the authors have approached an inverse modeling technique using regression-based machine learning to design a weakly coupled few-mode fiber for facilitating mode division multiplexing.The technique is adapted to predict the accurate profile parameters for the proposed few-mode fiber to obtain the maximum number of modes.It is for a three-ring-core few-mode fiber for guiding five,ten,fifteen,and twenty modes.Three types of regression models namely ordinary least-square linear multi-output regression,k-nearest neighbors of multi-output regression,and ID3 algorithm-based decision trees for multi-output regression are used for predicting the multiple profile parameters.It is observed that the ID3-based decision tree for multioutput regression is the robust,highly-accurate machine learning model for fast modeling of FMFs.The proposed fiber claims to be an efficient candidate for the next-generation 5G and 6G backhaul networks using mode division multiplexing.
基金sponsored by grants from the National Natural Science Foundation of China(62002010,61872347)the CAMS Innovation Fund for Medical Sciences(2019-I2M5-016)the Special Plan for the Development of Distinguished Young Scientists of ISCAS(Y8RC535018).
文摘Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation.To bridge the technical gap between virtual and real environments,we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans,taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner.First,we infer hierarchical geometry using two networks,which are optimized via the differentiable renderer.We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model.Then,ocean dynamics can be evolved using the reconstructed wave components.Through extensive experiments,we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation.Moreover,the new framework has the inverse modeling potential to facilitate a host of graphics applications,such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.
基金financially supported by the Scientific Research and Technology Development Project of China National Petroleum Corporation(No.2021ZG02)titled"Development of Seismic Data Processing Software for Ocean Nodes(OBN)"。
文摘In multi-component oil and gas exploration using ocean bottom nodes,converted wave data is rich in lithological and fracture information.One of the urgent problems to be solved is how to construct an accurate shear wave velocity model of the shallow sea bottom by leveraging the seismic wave information at the fluid-solid interface in the ocean,and improve the lateral resolution of marine converted wave data.Given that the dispersion characteristics of surface waves are sensitive to the S-wave velocity of subsurface media,and that Scholte surface waves,which propagate at the interface between liquid and solid media,exist in the data of marine oil and gas exploration,this paper proposes a Scholte wave inversion and modeling method based on oil and gas exploration using ocean bottom nodes.By using the method for calculating the Scholte wave dispersion spectrum based on the Bessel kernel function,the accuracy of dispersion spectrum analysis is improved,and more accurate dispersion curves are picked up.Through the adaptive weighted least squares Scholte wave dispersion inversion algorithm,the Scholte wave dispersion equation for liquid-solid media is solved,and the shear wave velocity model of the shallow sea bottom is calculated.Theoretical tests and applications of realdata have proven that this method can significantly improve the lateral resolution of converted wave data,provide high-quality data for subsequent inversion of marine multi-component oil and gas exploration data and reservoir reflection information,and contribute to the development of marine oil and gas exploration technology.
文摘Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.
基金Project supported by the China’s Major Science and Technology Program on Water Bodies Pollution Control and Treatment(Grant No.2013ZX07304-002)
文摘Runoff coefficient is an important parameter for the decision support of urban stormwater management. However, factors like comprehensive land-use type, variable spatial elevation, dynamic rainfall and groundwater elevation, make the direct estimation of runoff coefficient difficult. This paper presented a novel method to estimate the urban runoff coefficient using the inverse method, where observed time-series catchment outfall flow volume was employed as input for the water balance model and runoff coefficients of different catchments were treated as unknown parameters. A developed constrained minimization objective function was combined to solve the model and minimized error between observed and modeled outfall flow is satisfactory for the presenting of a set of runoff coefficients. Estimated runoff coefficients for the urban catchments in Shanghai downtown area demonstrated that practice of low impact design could play an important role in reducing the urban runoff.
基金funded by the Government of Navarra under the project“From BIM to BEM:B&B”(ref.0011-1365-2020-000227).
文摘Nowadays,building energy models(BEMs)are widely used,particularly in the assessment of energy consumption in buildings to address the potential savings that can be generated.The realisation of a dynamic energy model based on high-fidelity physics(white-box models)requires a tuning process to fit the model to reality,due to many uncertainties involved.Currently some research trends try to reduce this performance gap by modulating different types of experimental parameters such as:capacitances or infiltration.The EnergyPlus simulation software,in its latest versions,has implemented an object:HybridModel:Zone that calculates the infiltration and internal mass of buildings using an inverse modelling approach that employs only the measured indoor temperature data to invert the heat balance equation for the zone under study.The main objective of this paper is to reduce the execution time and uncertainties in the development of quality energy models by generating a new calibration methodology that implements this approach.This uses,as a starting point,a research created by the authors of this study,which was empirically and comparatively validated against the energy models developed by the participants in Annex 58.It is also worth highlighting the empirical validation of the HybridModel:Zone object,since it was activated in all scenarios where its execution is possible:periods of seven days or more of free oscillation and periods in which the building is under load.The findings are promising.The data generated with the new methodology,if compared with those produced by the baseline model,improve their resemblance to the real ones by 22.9%.While those of its predecessor did it by 15.6%.For this study,the two dwellings foreseen in Annex 58 of the IEA ECB project have been modelled and their real monitoring data have been used.
基金the National Social Science Fund of China under Grant No.22BTJ021“Qinglan project”of Colleges and Universities of Jiangsu Province and Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX211941。
文摘Inverse models can be used to estimate surface fluxes in terms of the observed atmospheric concentration measurement data.This paper proposes a new nonparametric spatio-temporal inverse model and provides the global expressions for the estimates by employing the B-spline method.The authors establish the asymptotic normality of the estimators under mild conditions.The authors also conduct numerical studies to evaluate the finite sample performance of the proposed methodologies.Finally,the authors apply the method to anthropogenic carbon dioxide(CO_(2))emission data from different provinces of Canada to illustrate the validity of the proposed techniques.
基金supported by the National Natural Science Foundation of China (41030107)Chinese Ministry of Science and Technology(2010CB950601)+2 种基金EUS & T Cooperative Project 2SMONGS&T Cooperation Project of the MOST and Eu (1015)CAMS Fundamental Research Funds-General Program (2010Y003)
文摘This paper presents the estimation of Chinese emissions of HCFC-22 and CFC-11 in 2009 by an inverse modeling method based on in-situ measurement data from the Shangdianzi Global Atmosphere Watch (GAW) Regional Station (SDZ) and atmospheric transport simulations. After inversion (a-posteriori) estimates of the Chinese emissions in 2009 increased by 6.6% for HCFC-22 from 91.7 (± 83.6) to 98.3 (± 47.4) kt/yr and by 22.5% for CFC-11 from 13 (±12.6) to 15.8 (±7.2) kt/yr compared to an a-priori emission. While the model simulation with a-priori emissions already captured the main features of the observed variability at the measurement site, the model performance (in terms of correlation and mean-square-error) improved using a-posteriori emissions. The inversion reduced the root-mean-square (RMS) error by 4% and 10% for HCFC-22 and CFC-11, respectively.
基金supported by the Fundamental Research Funds for the Central Universities(XJ2023005201)the National Natural Science Foundation of China(NSFC:U2267217,42141011,and 42002254).
文摘Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation accuracy.This paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter inversion.Initially,the simulation-optimization parameter estimation framework is introduced,which involves the integration of simulation models with metaheuristic algorithms.The subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms-genetic algorithm(GA),particle swarm optimization(PSO),simulated annealing(SA),and differential evolution(DE)-highlighting their recent applications in water resources research and related areas.Then,a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter inversion.Finally,three noteworthy directions are presented to address the common challenges among current studies,including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms,local approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous pa-rameters.In summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.
基金financially was supported by Colorado School of Minessupported by the Science and Technology Ministry of China (2016ZX05033003)+1 种基金China Academy of Sciences (XDA14010204)Sinopec (G5800-15-ZS-KJB016)
文摘This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.
基金supported by the West Light Talents Cultivation Program of Chinese Academy of Sciences (XBBS 200801)the National Natural Science Foundation of China (40801146)the JSPS Project (21403001)
文摘Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a dominant species of riparian ecosystems in arid lands, Populus euphratica Oliv. is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages. In this study, we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P. euphratica leaves. Both the shapes and biochemical properties of P. euphratica leaves were found to change with the heights from ground surface. The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes, and also better than hyperspectral indices. Similar results were obtained for estimations of equivalent water thickness （EWT） and leaf mass per area （LMA）. Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error （RMSE） and R2 values between those obtained with the two calibration strategies using the inversed PROSPECT model. Hence, the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems, but the calibration to the model requires special attention.
基金financially supported by the National Natural Science Foundation of China(Grant No.51279028)the Public Science and Technology Research Funds Projects of Ocean(Grant No.201405025-1)
文摘A new wave energy dissipation structure is proposed, aiming to optimize the dimensions of the structure and make the reflection of the structure maintain a low level within the scope of the known frequency band. An optimal extended ANFIS model combined with the wave reflection coefficient analysis for the estimation of the structure dimensions is established. In the premise of lower wave reflection coefficient, the specific sizes of the structure are obtained inversely, and the contribution of each related parameter on the structural reflection performance is analyzed. The main influencing factors are determined. It is found that the optimal dimensions of the proposed structure exist, which make the wave absorbing performance of the structure reach a perfect status under a wide wave frequency band.
基金supported by the National Natural Science Foundation of China (No.U2133210).
文摘Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Doppler Lidar detection,and combining Dynamic Bayesian Networks(DBN)with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN),this paper proposes a model for the inversion of wake vortex parameters.During the wake vortex flow field simulation analysis,the wind and turbulent environment were initially superimposed onto the simulated wake velocity field.Subsequently,Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training.In the case study validation,real measured data underwent preprocessing and were then input into the established model.This allowed us to construct the wake vortex characteristic parameter inversion model.The final results demonstrated that our model achieved parameter inversion with only minor errors.In a practical example,our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm.This study holds significant promise for real-time monitoring of wake vortices at airports,and is proved a crucial step in developing wake vortex interval standards.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51876010 and 51676019).
文摘A model predictive inverse method (MPIM) is presented to estimate the time- and space-dependent heat flux onthe ablated boundary and the ablation velocity of the two-dimensional ablation system. For the method, first of all, therelationship between the heat flux and the temperatures of the measurement points inside the ablation material is establishedby the predictive model based on an influence relationship matrix. Meanwhile, the estimation task is formulated as aninverse heat transfer problem (IHTP) with consideration of ablation, which is described by an objective function of thetemperatures at the measurement point. Then, the rolling optimization is used to solve the IHTP to online estimate theunknown heat flux on the ablated boundary. Furthermore, the movement law of the ablated boundary is reconstructedaccording to the estimation of the boundary heat flux. The effects of the temperature measurement errors, the numberof future time steps, and the arrangement of the measurement points on the estimation results are analyzed in numericalexperiments. On the basis of the numerical results, the effectiveness of the presented method is clarified.
基金The National Natural Science Foundation of China under contract Nos 41490644 and 41490640
文摘Inverse technique is a widely used method in oceanography, but it has a problem that the retrieved solutions often violate model prior assumptions. To tune the model has consistent solutions, an iteration approach, which successively utilizes the posterior statistics for next round inverse estimation, is introduced and tested from a real case study. It is found that the consistency may become elusive as the determinants of solution and noise covariance matrices become zero in the iteration process. However, after several steps of such operation, the difference between posterior statistics and the model prior ones can be gradually reduced.
基金the financial support from China Scholarship Council (CSC) (No. 201706080020)。
文摘This paper is to determine the flow stress curve of 5049-O aluminium alloy by a tube hydraulic bulging test with fixed end-conditions. During this test, several tubular specimens are bulged under different internal pressures before their bursting, and the corresponding bulging height and wall thickness at the pole are measured. An inverse strategy is developed to determine the constitutive parameters of tubular materials based on experimental data, which combines the finite element method with gradient-based optimization techniques. In this scheme, the objective function is formulated with the sum of least squares of the error between numerical and experimental data, and finite difference approximation is used to calculate the gradient. The tubular material behavior is assumed to meet the von Mises yield criterion and Hollomon exponential hardening law. Then, constitutive parameters identification is performed by minimization of the objective function. In order to validate the performance of this framework, identified parameters are compared with those obtained by two types of theoretical models, and tensile tests are performed on specimens cut from the same tubes. The comparison shows that this inverse framework is robust and can achieve a more accurate parameter identification by eliminating mechanical and geometrical assumptions in classical theoretical analysis.
文摘An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.
基金financially supported by the Independent Research fund of Joint National Local Engineering Research Centre for Safe and Precise Coal Mining(Anhui University of Science and Technology)(Grant No.EC2022001)State Key Research Development Program of China(Grant No.2022YFC3004602)the Fundamental Research Funds for the Central Universities(Grant No.2022YJSLJ08).
文摘Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities.In this study,three theoretical models,including linear,polynomial,and exponential models,are proposed to inverse the mining-induced stress through the acquisition and analysis of hydraulic support stress and micro-seismicity in the coal mining face.The distribution of mining-induced stress in the coal seam are graphed by fitting two key stress parameters including hydraulic support stress and peak stress,and two key zones including goaf zone and in situ stress zone.These key stress parameters and zones are defined based on the critical nodes of the model curve.According to the geological background of Mataihao coal mine in Erdos,Inner Mongolia Autonomous Region,China,the contours of mining-induced stress are graphed through the stress calculation of these three inversion theoretical models.The multi-monitoring data of micro-seismicity,drilling chips,advanced borehole stress and bolts axial force are used to verify the key stress parameters and zones of the theoretical models.It shows that the monitoring data are in good agreement with the distribution of inversed results.It should be emphasized that,if the fault structure exists around the mining face,the mining-induced stress decreases obviously when the mining face is passing through the faults,and the location of the peak stress will be closer to the mining face.The results in this study could provide methods for early prevention of extreme mining-induced stress and disaster control in the mining activities.
基金supported by the Chinese National Special Fund for Agro-scientific Research in the Public Interest (201003025 and 201103022)the National Key Research and Development Program of China (2018YFD0201004)the Discipline Construction Project of Liaoning Academy of Agricultural Sciences, China (2019DD082612)。
文摘The soybean aphid, Aphis glycines Matsumura(Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management(IPM). Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field. Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year. In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions. This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data. Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.
