The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of ...The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.展开更多
A simulation model developed by the authors (Huang et al., 1999) was validated against independent field measurements of methane emission from rice paddy soils in Texas of USA, Tuzu Of China and Vercelli of Italy.A si...A simulation model developed by the authors (Huang et al., 1999) was validated against independent field measurements of methane emission from rice paddy soils in Texas of USA, Tuzu Of China and Vercelli of Italy.A simplified version of the simulation model was further validated against methane emission measurements from various regions of the world, including italy, China, Indonesia, Philippines and the United States. Model validation suggested that the seasonal variation of methane emission was mainly regulated by rice growth and development and that methane emission could be predicted from rice net productivity, cultivar character, soil texture and temperature, and organic matter amendments. Model simulations in general agreed with the observations. The comparison between computed and measured methane emission resulted in correlation coefficients r2 values from 0.450 to 0.952, significant at 0.01-0.001 probability level.On the basis of available information on rice cultivated area, growth duration, grain yield, soil texture and temperature, methane emission from rice paddy soils of China's Mainland was estimated for 28 rice cultivated provinces/municipal cities by employing the validated model. The calculated daily methane emission rates, on a provincial scale, ranged from 0.12 to 0.71 g m-2 with an average of 0.26 g m-2. A total amount of 7.92 Tg CH4 per year, ranging from 5.89 to 11.17 Tg year-1, was estimated to be released from Chinese rice paddy soils. Of the total, 45% was emitted from the single-rice growing season, and 19% and 36% were from the early-rice and the late-rice growing seasons, respectively. Approximately 70% of the total was emitted in the region located at latitude between 25°and 32°N. The emissions from rice fields in Sichuan and Hunan provinces were calculated to be 2.34 Tg year-1, accounting for approximately 30% of the total.展开更多
The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model...The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.展开更多
This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictabi...This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictability of water quality thus plays a crucial role in managing our ecosystems to make informed decisions and,hence,proper environmental management.This study addresses these challenges by proposing an effective machine learning methodology applied to the“Water Quality”public dataset.The methodology has modeled the dataset suitable for providing prediction classification analysis with high values of the evaluating parameters such as accuracy,sensitivity,and specificity.The proposed methodology is based on two novel approaches:(a)the SMOTE method to deal with unbalanced data and(b)the skillfully involved classical machine learning models.This paper uses Random Forests,Decision Trees,XGBoost,and Support Vector Machines because they can handle large datasets,train models for handling skewed datasets,and provide high accuracy in water quality classification.A key contribution of this work is the use of custom sampling strategies within the SMOTE approach,which significantly enhanced performance metrics and improved class imbalance handling.The results demonstrate significant improvements in predictive performance,achieving the highest reported metrics:accuracy(98.92%vs.96.06%),sensitivity(98.3%vs.71.26%),and F1 score(98.37%vs.79.74%)using the XGBoost model.These improvements underscore the effectiveness of our custom SMOTE sampling strategies in addressing class imbalance.The findings contribute to environmental management by enabling ecology specialists to develop more accurate strategies for monitoring,assessing,and managing drinking water quality,ensuring better ecosystem and public health outcomes.展开更多
Spherical objects are widely used in target localization applications,and the existing sphere localization methods with cameras or total stations both have some limitations.A new high-precision sphere localization met...Spherical objects are widely used in target localization applications,and the existing sphere localization methods with cameras or total stations both have some limitations.A new high-precision sphere localization method with a theodolite is proposed in this paper.From the view point of the theodolite,the contour points of a sphere with a known radius are measured as latitude-longitude coordinates.It is observed that the center of the target sphere is located on a cylindrical surface constructed with the latitude-longitude coordinates,and therefore the latitude-longitude coordinates of at least three contour points can be used to construct a set of ternary quadratic equations.The Gröbner basis method is used to compute at most four real solutions of the sphere center coordinates.To distinguish the only meaningful solution from the other possible real solutions,a pre-processing of the measured longitude values is also proposed.The factors affecting the positioning accuracy of the sphere center are evaluated in simulation experiments,which are used to obtain an empirical estimation model of the positioning error.Real data experiments are also performed and the results show that the proposed method can achieve high localization precision.展开更多
The distribution of the sediment material storage quantity along the debris flow channels(SMSQ_DFC)can provide a foundation for runoffgenerated debris flow prediction or susceptibility assessment.Current models for es...The distribution of the sediment material storage quantity along the debris flow channels(SMSQ_DFC)can provide a foundation for runoffgenerated debris flow prediction or susceptibility assessment.Current models for estimating SMSQ_DFC do not consider the capacity of the channel cross-section to accommodate sediment materials.This accommodation condition serves as a limiting factor in determining whether the expected surplus of sediment materials can ultimately be stored.To address this issue,a mass-conservative index was used to represent the balance of deposit materials at any cross-section,considering the influx from upstream,outflux to downstream,and accommodation capacity.Based on this index,a new model for estimating SMSQ_DFC was developed and subsequently evaluated.The evaluation results show that the model meets the accuracy requirements with average error rates of 14.06%for self-validation and 14.81%for generalization ability validation.To assess its practical applications,the model was applied to the Yeniu Gully in Wenchuan County,Sichuan Province,an area with detailed field survey data.The results show that the model exhibits a commendable performance.Compared to traditional theoretical and semi-theoretical statistical models,our model is easier to use(input parameters can be obtained using Geographic Information Systems(GIS)).The modeling parameters chosen in this study have more theoretical significance than those used in existing purely statistical models,offering more effective technical support for estimating SMSQ_DFC.展开更多
Greenhouse experiments were conducted to determine the ammonia volatilization loss with or withoutapplication of surface film-forming material (SFFM). Ammonia volatilization loss was estimated by the modeldeveloped by...Greenhouse experiments were conducted to determine the ammonia volatilization loss with or withoutapplication of surface film-forming material (SFFM). Ammonia volatilization loss was estimated by the modeldeveloped by Jayaweera and Mikkelsen. The results showed that the model could estimate and predict wellammonia volatilization loss also in case of SFFM addition. There was an emended factor B introduced tothe model calculation when SFPM was used. Simulated calculation showed that the effect of factor B onNHa loss was obvious. The value of B was governed by SFFM and the environmental conditions. Sensitivityanalysis suggested that pH was the main factor coatrolling NH3 volatilization loss from the floodwater.展开更多
An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and pot...An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.展开更多
Grassland is the important component of the terrestrial ecosystems. Estimating net primary productivity （NPP） of grassland ecosystem has been a central focus in global climate change researches. To simulate the gras...Grassland is the important component of the terrestrial ecosystems. Estimating net primary productivity （NPP） of grassland ecosystem has been a central focus in global climate change researches. To simulate the grassland NPP in southern China, we built a new climate productivity model, and validated the model with the measured data from different years in the past. The results showed that there was a logarithmic correlation between the grassland NPP and the mean annual temperature, and there was a linear positive correlation between the grassland NPP and the annual precipitation in southern China. Al these results reached a very signiifcant level （P〈0.01）. There was a good correlation between the simulated and the measured NPP, withR2 of 0.8027, reaching the very signiifcant level. Meanwhile, both root mean square errors （RMSE） and relative root-mean-square errors （RRMSE） stayed at a relatively low level, showing that the simulation results of the model were reliable. The NPP values in the study area had a decreasing trend from east to west and from south to north, and the mean NPP was 471.62 g C m？2 from 2000 to 2011. Additionaly, there was a rising trend year by year for the mean annual NPP of southern grassland and the tilt rate of the mean annual NPP was 3.49 g C m？2 yr？1 in recent 12 years. The above results provided a new method for grassland NPP estimation in southern China.展开更多
The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - ...The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.展开更多
AGB （aboveground fresh biomass） is one of the most important parameters of the crop condition monitored with remote sensing. Hyper spectrum remote sensing with the fine spectrum information becomes the efficient met...AGB （aboveground fresh biomass） is one of the most important parameters of the crop condition monitored with remote sensing. Hyper spectrum remote sensing with the fine spectrum information becomes the efficient method estimating the vegetation AGB. The research was conducted in Xinjiang, the largest cotton planting region of China. The paper analyzed the correlation between the cotton AGB and reflective spectrum and the first derivative spectrum, and the variation coefficient of the waveband reflectance. According to the analysis above, all of 23 parameters, including the hyper spectrum reflectance, the first derivative spectrum parameters and normalization vegetation indexes, were established. And then the estimation models on cotton AGB of relaxing and compact canopy type were established and tested respectively. The tested results showed that Fgo1, [901,502], [901,629], [901,672] among the reflective spectral parameters and D525, D956, D1019, D1751 among the first derivative spectral parameters had the homogenous effect on different cotton canopy types, and the determination coefficients of the models above all arrive at the significant level of 0.99 confidence interval. At last, the tested results of the homogeneity models for different canopy types indicated the parameters of [901, 502], [901,629], [901,672] have more satisfying veracity than others, and the relative errors are as low as 17.0, 16.3 and 16.7% correspondingly; in contrast, the estimation veracity of the first derivative spectrum parameters of single waveband is low.展开更多
Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment...Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given.展开更多
Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun ...Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun coal mine, located in the Mentougou district of Beijing, was chosen as a case study. The ecological damage was analyzed by 3S technology, field investigation and from chemical data. The derivative spectra of the diagnostic absorption bands are derived from the spectra measured in the field and used as characteristic spectral variables. A correlation analysis was conducted for the nitrogen content of the vegetation samples and the fast derivative spectrum and the estimation model of nitrogen content established by a multiple stepwise linear regression method. The spatial distribution of nitrogen content was extracted by a parameter mapping method from the Hyperion data which revealed the distribution of the nitrogen content. In addition, the estimation model was evaluated for two evaluation indicators which are important for the precision of the model. Experimental results indicate that by linear regression and parameter mapping, the estimation model precision was Very high. The coefficient of determination, R2, was 0.795 and the standard deviation of residual （SDR） 0.19. The nitrogen content of most samples was about 1.03% and the nitrogen content in the study site seems inversely proportional to the distance from the piles of coal waste. Therefore, we can conclude that inversely modeling nitrogen content by hyper-spectral remote sensing in exhausted coal mining sites is feasible and our study can be taken as reference in species selection and in subseauent management and maintenance in ecological restoration.展开更多
For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For ...For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.展开更多
This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance de...This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.展开更多
Prognosis is a key technology to improve reliability,safety and maintainability of products,a lot of researchers have been devoted to this technology.But to improve the predict accuracy of remaining life of products h...Prognosis is a key technology to improve reliability,safety and maintainability of products,a lot of researchers have been devoted to this technology.But to improve the predict accuracy of remaining life of products has been difficult.To predict the lifetime specification of pneumatic cylinders with high reliability and long lifetime and small specimen,this paper put forward the prognosis algorithm based on the path classification and estimation（PACE） model.PACE model is based entirely on failure data instead of failure threshold.Pneumatic cylinders normally characterize with failure mechanism wear and tear.Since the minimum working pressure increases with the number of working cycles,the minimum working pressure is chosen as degradation signal.PACE model is fundamentally composed of two operations：path classification and remaining useful life（RUL） estimation.Path classification is to classify a current degradation path as belonging to one or more of previously collected exemplary degradation paths.RUL estimation is to use the resulting memberships to estimate the remaining useful life.In order for verification and validation of PACE prognostic method,six pneumatic cylinders are tested.The test data is analyzed by PACE prognostics.It is found that the PACE based prognosis method has higher prediction accuracy and smaller variance and PACE model is significantly outperform population based prognostics especially for small specimen condition.PACE model based method solved the problem of prediction accuracy for small specimen pneumatic cylinders＇ prognosis.展开更多
The stress-strength model is widely applied in reliability. Observations are often subject to right censoring due to some practical limitations. In such circumstances, the statistical inference for the stress-strength...The stress-strength model is widely applied in reliability. Observations are often subject to right censoring due to some practical limitations. In such circumstances, the statistical inference for the stress-strength model is demanding, although lacking. We propose a nonparametric method for the inference of the stress-strength model when the observations are subject to right censoring. The asymptotic properties are also established. The practical utility of the proposed method is assessed through both simulated and real data sets.展开更多
Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented ...Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.展开更多
The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is ...The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.展开更多
An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only smal...An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.展开更多
基金supported by the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2024ZR03)the National Natural Science Foundation of China(No.42407248)+2 种基金the Guizhou Provincial Basic Research Program(Natural Science)(No.QKHJC-[2023]-YB066)the Key Laboratory of Smart Earth(No.KF2023YB04-02)the Fundamental Research Funds for the Central Universities。
文摘The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.
文摘A simulation model developed by the authors (Huang et al., 1999) was validated against independent field measurements of methane emission from rice paddy soils in Texas of USA, Tuzu Of China and Vercelli of Italy.A simplified version of the simulation model was further validated against methane emission measurements from various regions of the world, including italy, China, Indonesia, Philippines and the United States. Model validation suggested that the seasonal variation of methane emission was mainly regulated by rice growth and development and that methane emission could be predicted from rice net productivity, cultivar character, soil texture and temperature, and organic matter amendments. Model simulations in general agreed with the observations. The comparison between computed and measured methane emission resulted in correlation coefficients r2 values from 0.450 to 0.952, significant at 0.01-0.001 probability level.On the basis of available information on rice cultivated area, growth duration, grain yield, soil texture and temperature, methane emission from rice paddy soils of China's Mainland was estimated for 28 rice cultivated provinces/municipal cities by employing the validated model. The calculated daily methane emission rates, on a provincial scale, ranged from 0.12 to 0.71 g m-2 with an average of 0.26 g m-2. A total amount of 7.92 Tg CH4 per year, ranging from 5.89 to 11.17 Tg year-1, was estimated to be released from Chinese rice paddy soils. Of the total, 45% was emitted from the single-rice growing season, and 19% and 36% were from the early-rice and the late-rice growing seasons, respectively. Approximately 70% of the total was emitted in the region located at latitude between 25°and 32°N. The emissions from rice fields in Sichuan and Hunan provinces were calculated to be 2.34 Tg year-1, accounting for approximately 30% of the total.
基金Supported by the National Natural Science Foundation of China(41205126)the Discipline Construction and Macroscopic Agricultural Research Project of Anhui Academy of Agricultural Sciences(13A1424)+2 种基金the Fund for Youth Innovation of Anhui Academy of Agricultural Sciences(14B1460)the Innovative Research Team for Agricultural Disaster Risk Analysis in Anhui ProvinceAnhui Academy of Agricultural Sciences(14C1409)~~
文摘The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.
文摘This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictability of water quality thus plays a crucial role in managing our ecosystems to make informed decisions and,hence,proper environmental management.This study addresses these challenges by proposing an effective machine learning methodology applied to the“Water Quality”public dataset.The methodology has modeled the dataset suitable for providing prediction classification analysis with high values of the evaluating parameters such as accuracy,sensitivity,and specificity.The proposed methodology is based on two novel approaches:(a)the SMOTE method to deal with unbalanced data and(b)the skillfully involved classical machine learning models.This paper uses Random Forests,Decision Trees,XGBoost,and Support Vector Machines because they can handle large datasets,train models for handling skewed datasets,and provide high accuracy in water quality classification.A key contribution of this work is the use of custom sampling strategies within the SMOTE approach,which significantly enhanced performance metrics and improved class imbalance handling.The results demonstrate significant improvements in predictive performance,achieving the highest reported metrics:accuracy(98.92%vs.96.06%),sensitivity(98.3%vs.71.26%),and F1 score(98.37%vs.79.74%)using the XGBoost model.These improvements underscore the effectiveness of our custom SMOTE sampling strategies in addressing class imbalance.The findings contribute to environmental management by enabling ecology specialists to develop more accurate strategies for monitoring,assessing,and managing drinking water quality,ensuring better ecosystem and public health outcomes.
基金supported in part by the National Natural Science Foundation of China under Grants 61703373,61873246,U1504604in part by the Key research project of Henan Province Universities under Grant 19A413014.
文摘Spherical objects are widely used in target localization applications,and the existing sphere localization methods with cameras or total stations both have some limitations.A new high-precision sphere localization method with a theodolite is proposed in this paper.From the view point of the theodolite,the contour points of a sphere with a known radius are measured as latitude-longitude coordinates.It is observed that the center of the target sphere is located on a cylindrical surface constructed with the latitude-longitude coordinates,and therefore the latitude-longitude coordinates of at least three contour points can be used to construct a set of ternary quadratic equations.The Gröbner basis method is used to compute at most four real solutions of the sphere center coordinates.To distinguish the only meaningful solution from the other possible real solutions,a pre-processing of the measured longitude values is also proposed.The factors affecting the positioning accuracy of the sphere center are evaluated in simulation experiments,which are used to obtain an empirical estimation model of the positioning error.Real data experiments are also performed and the results show that the proposed method can achieve high localization precision.
基金supported by Geological Disaster Patterns and Mitigation Strategies Under River-Reservoir Hydrodynamics in the Three Gorges Reservoir Fluctuation Zone(5000002024CC20004)the National Key Research and Development Program of China(2023YFC3007205)+1 种基金the National Natural Science Foundation of China(No.42271013)the West Light Foundation of the Chinese Academy of Sciences.
文摘The distribution of the sediment material storage quantity along the debris flow channels(SMSQ_DFC)can provide a foundation for runoffgenerated debris flow prediction or susceptibility assessment.Current models for estimating SMSQ_DFC do not consider the capacity of the channel cross-section to accommodate sediment materials.This accommodation condition serves as a limiting factor in determining whether the expected surplus of sediment materials can ultimately be stored.To address this issue,a mass-conservative index was used to represent the balance of deposit materials at any cross-section,considering the influx from upstream,outflux to downstream,and accommodation capacity.Based on this index,a new model for estimating SMSQ_DFC was developed and subsequently evaluated.The evaluation results show that the model meets the accuracy requirements with average error rates of 14.06%for self-validation and 14.81%for generalization ability validation.To assess its practical applications,the model was applied to the Yeniu Gully in Wenchuan County,Sichuan Province,an area with detailed field survey data.The results show that the model exhibits a commendable performance.Compared to traditional theoretical and semi-theoretical statistical models,our model is easier to use(input parameters can be obtained using Geographic Information Systems(GIS)).The modeling parameters chosen in this study have more theoretical significance than those used in existing purely statistical models,offering more effective technical support for estimating SMSQ_DFC.
文摘Greenhouse experiments were conducted to determine the ammonia volatilization loss with or withoutapplication of surface film-forming material (SFFM). Ammonia volatilization loss was estimated by the modeldeveloped by Jayaweera and Mikkelsen. The results showed that the model could estimate and predict wellammonia volatilization loss also in case of SFFM addition. There was an emended factor B introduced tothe model calculation when SFPM was used. Simulated calculation showed that the effect of factor B onNHa loss was obvious. The value of B was governed by SFFM and the environmental conditions. Sensitivityanalysis suggested that pH was the main factor coatrolling NH3 volatilization loss from the floodwater.
文摘An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.
基金funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (PAPD)the Science and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences (2015)
文摘Grassland is the important component of the terrestrial ecosystems. Estimating net primary productivity （NPP） of grassland ecosystem has been a central focus in global climate change researches. To simulate the grassland NPP in southern China, we built a new climate productivity model, and validated the model with the measured data from different years in the past. The results showed that there was a logarithmic correlation between the grassland NPP and the mean annual temperature, and there was a linear positive correlation between the grassland NPP and the annual precipitation in southern China. Al these results reached a very signiifcant level （P〈0.01）. There was a good correlation between the simulated and the measured NPP, withR2 of 0.8027, reaching the very signiifcant level. Meanwhile, both root mean square errors （RMSE） and relative root-mean-square errors （RRMSE） stayed at a relatively low level, showing that the simulation results of the model were reliable. The NPP values in the study area had a decreasing trend from east to west and from south to north, and the mean NPP was 471.62 g C m？2 from 2000 to 2011. Additionaly, there was a rising trend year by year for the mean annual NPP of southern grassland and the tilt rate of the mean annual NPP was 3.49 g C m？2 yr？1 in recent 12 years. The above results provided a new method for grassland NPP estimation in southern China.
基金supported financially by the Knowledge Innovation Project of Chinese Academy of Sciences (KZCX2-YW-Q03-5)the National Science and Technology Support Plan Project (2009BAK56B05)the National Natural Science Foundation of China (40802072)
文摘The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.
文摘AGB （aboveground fresh biomass） is one of the most important parameters of the crop condition monitored with remote sensing. Hyper spectrum remote sensing with the fine spectrum information becomes the efficient method estimating the vegetation AGB. The research was conducted in Xinjiang, the largest cotton planting region of China. The paper analyzed the correlation between the cotton AGB and reflective spectrum and the first derivative spectrum, and the variation coefficient of the waveband reflectance. According to the analysis above, all of 23 parameters, including the hyper spectrum reflectance, the first derivative spectrum parameters and normalization vegetation indexes, were established. And then the estimation models on cotton AGB of relaxing and compact canopy type were established and tested respectively. The tested results showed that Fgo1, [901,502], [901,629], [901,672] among the reflective spectral parameters and D525, D956, D1019, D1751 among the first derivative spectral parameters had the homogenous effect on different cotton canopy types, and the determination coefficients of the models above all arrive at the significant level of 0.99 confidence interval. At last, the tested results of the homogeneity models for different canopy types indicated the parameters of [901, 502], [901,629], [901,672] have more satisfying veracity than others, and the relative errors are as low as 17.0, 16.3 and 16.7% correspondingly; in contrast, the estimation veracity of the first derivative spectrum parameters of single waveband is low.
基金Project supported by the Open Research Fund Programof the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, WuhanUniversity (No.905276031-04-10) .
文摘Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given.
文摘Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun coal mine, located in the Mentougou district of Beijing, was chosen as a case study. The ecological damage was analyzed by 3S technology, field investigation and from chemical data. The derivative spectra of the diagnostic absorption bands are derived from the spectra measured in the field and used as characteristic spectral variables. A correlation analysis was conducted for the nitrogen content of the vegetation samples and the fast derivative spectrum and the estimation model of nitrogen content established by a multiple stepwise linear regression method. The spatial distribution of nitrogen content was extracted by a parameter mapping method from the Hyperion data which revealed the distribution of the nitrogen content. In addition, the estimation model was evaluated for two evaluation indicators which are important for the precision of the model. Experimental results indicate that by linear regression and parameter mapping, the estimation model precision was Very high. The coefficient of determination, R2, was 0.795 and the standard deviation of residual （SDR） 0.19. The nitrogen content of most samples was about 1.03% and the nitrogen content in the study site seems inversely proportional to the distance from the piles of coal waste. Therefore, we can conclude that inversely modeling nitrogen content by hyper-spectral remote sensing in exhausted coal mining sites is feasible and our study can be taken as reference in species selection and in subseauent management and maintenance in ecological restoration.
基金This work was supported by the National Natural Science Foundation(NNSF)of China under grant no.61673386,62073335the China Postdoctoral Science Foundation(2017M613201,2019T120944).
文摘For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.
基金supported in part by the National Natural Science Foundation of China(No.61773306)the National Key Research and Development Plan,China(Nos.2021YFC2202600 and 2021YFC2202603)。
文摘This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.
基金supported by the Laboratory of Aviation Safety Technical Analysis and Appraisal of China Academy of Civil Aviation Science and Technology(Grant No. 2009-02)
文摘Prognosis is a key technology to improve reliability,safety and maintainability of products,a lot of researchers have been devoted to this technology.But to improve the predict accuracy of remaining life of products has been difficult.To predict the lifetime specification of pneumatic cylinders with high reliability and long lifetime and small specimen,this paper put forward the prognosis algorithm based on the path classification and estimation（PACE） model.PACE model is based entirely on failure data instead of failure threshold.Pneumatic cylinders normally characterize with failure mechanism wear and tear.Since the minimum working pressure increases with the number of working cycles,the minimum working pressure is chosen as degradation signal.PACE model is fundamentally composed of two operations：path classification and remaining useful life（RUL） estimation.Path classification is to classify a current degradation path as belonging to one or more of previously collected exemplary degradation paths.RUL estimation is to use the resulting memberships to estimate the remaining useful life.In order for verification and validation of PACE prognostic method,six pneumatic cylinders are tested.The test data is analyzed by PACE prognostics.It is found that the PACE based prognosis method has higher prediction accuracy and smaller variance and PACE model is significantly outperform population based prognostics especially for small specimen condition.PACE model based method solved the problem of prediction accuracy for small specimen pneumatic cylinders＇ prognosis.
基金Supported by the National Natural Science Foundation of China(11301545,11401341,11326087)the Fundamental Research Fund for the Central Universities(31541311216)+2 种基金Scientific Research Fund of Fujian Education Department(JA13301)Qingyang Regional Technology Cooperation Planning Project(KH201304)Gansu Education Science "twelfth five-year" Planning Project(GS[2013]GHB1097)
文摘The stress-strength model is widely applied in reliability. Observations are often subject to right censoring due to some practical limitations. In such circumstances, the statistical inference for the stress-strength model is demanding, although lacking. We propose a nonparametric method for the inference of the stress-strength model when the observations are subject to right censoring. The asymptotic properties are also established. The practical utility of the proposed method is assessed through both simulated and real data sets.
基金This project is supported by Shanghai Automobile Industry Corporation Technology Foundation, China(No.0224).
文摘Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.
基金supported in part by National Basic Research Program of China(No.2012CB821200)in part by the National Natural Science Foundation of China(No.61174024)
文摘The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
基金Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11_0193)NUAA Research Funding (NJ2010009)
文摘An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.
