Estimate of the ASTER GDEM regional systematic errors and their removal

The aim of this article is to analyse and estimate the systematic errors of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), assuming the existence of systematic influences due to the erroneously determined altitude datum. The assumption is based on the identical sign for the majority of differences between the control point elevations and the elevations derived from GDEM. The question is how to model a systematic influence and whether such a model can improve the global GDEM accuracy. The error analysis comprises two unbiased test areas (flat and hilly areas), where one uses the elevations of the control points as conditionally correct values. Three methods of estimating and eliminating the systematic errors are presented: a constant-systematic-error model, a variable-systematic-error model by first-order polynomial and a variable-systematic error model by second-order polynomial. The accuracy analysis concerning the original and calibrated GDEMs indicates a significant accuracy improvement in the case of the calibrated methods for both test areas, especially for the flat area.     

Accuracy assessment of the ASTER GDEM and SRTM3 DEM: an example in the Loess Plateau and North China Plain of China

The digital elevation model (DEM) produced by the Shuttle Radar Topographic Mission (SRTM) has provided important fundamental data for topographic analysis in many fields. The recently released global digital elevation model (GDEM) produced by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has higher spatial resolution and wider coverage than the SRTM3 DEM, and thus may be of more value to researchers. Taking two typical study areas—the Loess Plateau and the North China Plain of China—as an example, this article assesses the accuracy of the SRTM3 DEM and ASTER GDEM by collecting ground control points from topographical maps. It is found that both the SRTM3 DEM and the ASTER GDEM are far more accurate for the North China Plain than for the Loess Plateau. For the Loess Plateau, the accuracy of the ASTER GDEM is similar to that of the SRTM3 DEM; whereas for the North China Plain, it is much worse than that of the SRTM3 DEM. Considering the negative bias of the ASTER GDEM for flat or gentle regions, we improve its accuracy by adding the difference of the mean value between the SRTM3 DEM and ASTER GDEM for the North China Plain; then, the root mean square error (RMSE) of ±7.95 m from the original ASTER GDEM is improved to ±5.26 m, which demonstrates that it is a simple but useful way to improve the accuracy of the ASTER GDEM in flat or gentle regions.     

A logit model for predicting wetland location using ASTER and GIS

Data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to develop a logistic regression model to predict the location of wetlands in the Coastal Plain of Virginia. We used the first five bands from two ASTER scenes (spanning 0.52–2.18 µm) covering the same area, acquired 6 March 2005 and 16 October 2005. March Band 3 contributed the most in discriminating wetlands over the other ASTER bands (marginal effect = 7.277), and it predicted the location of 60% of the total wetlands. We used a canonical discriminant analysis to test the significance of GIS variables in separating wetlands from uplands. Soil Survey Geographic Database soil data had the highest correlation with the first canonical component (0.876), followed by March Band 3 (?0.803), and the National Hydrography Dataset water (0.725). We included GIS data layers into the logit model. The resulting model predicted the location of over 78% of total wetlands, highlighting the potential of models incorporating ASTER data for speeding the wetland mapping process, lowering costs of map production, and improving wetland mapping accuracy.     

A new error model for adaptive systems

A simple error model is described for both discrete and continuous time adaptive systems. The stability of the model for both bounded and unbounded inputs is analyzed for the discrete case and extended to continuous time models. It is shown thatDeltaphi(k)in I^{2}in the former case andphi(t)in L^{2}in the latter case due to the presence of a feedback signal and these in turn play an important role in the stability analysis of discrete and continuous time adaptive systems.     

A model for error recovery with global checkpointing

The paper proposes a new technique for providing software fault tolerance in concurrent systems. It combines the traditional global checkpointing mechanism with the recovery block concept in order to come up with an easily implementable error recovery mechanism. This mechanism involves smaller overhead in case of moderate to high process interaction than the schemes considered in past, which are based upon the idea of local checkpointing. A model for computing the optimum checkpointing interval is also presented. A particular distribution is hypothesized for the coverage of the recovery, and the behavior of the model is studied in detail for this case.     

A causal model for software cost estimating error

Software cost estimation is an important concern for software managers and other software professionals. The hypothesized model in this research suggests that an organization's use of an estimate influences its estimating practices which influence both the basis of the estimating process and the accuracy of the estimate. The model also suggests that the estimating basis directly influences the accuracy of the estimate. A study of business information systems managers and professionals at 112 different organizations using causal analysis with the Equations Modeling System (EQS) refined the model. The refined model shows that no managerial practice in this study discourages the use of intuition, guessing and personal memory in cost estimating. Although user commitment and accountability appear to foster algorithm-based estimating, such an algorithmic basis does not portend greater accuracy. Only one managerial practice-the use of the estimate in performance evaluations of software managers and professionals-presages greater accuracy. By implication, the research suggests somewhat ironically that the most effective approach to improve estimating accuracy may be to make estimators, developers and managers more accountable for the estimate even though it may be impossible to direct them explicitly on how to produce a more accurate one     

A context-dependent knowledge model for evaluation of regional environment

In this paper we develop a rule-based model for evaluation of regional environment based on both hard and soft data, where by hard data we mean the statistical measurements while by soft data we mean subjective appreciation of human beings of environmental issues. As people's feeling strongly depends on the social and economical characteristics of administrative regions where they live, we firstly use the hard data concerning these characteristics to do clustering in order to obtain clusters corresponding to regions with the homogeneous social and economical characteristics relatively. We then use the soft data, with the help of data-mining techniques, to develop rule-based models which show association between evaluated items of residents in the clusters. Finally, a relationship between hard data and soft data through an integrated model will be explored. It is shown that the soft data are rather reliable and we should integrate subjective knowledge learnt from soft data into modelling of environmental issues.     

A financial resource allocation model for regional water systems

The public water supply sector is facing significant external pressures for change from decreasing water resource availability, stricter water quality regulations, decreasing federal subsidies, increasing public scrutiny, decreasing financial health, and increasing infrastructure replacement costs. These forces necessitate greater accountability by community water systems (CWS) to their stakeholders. This paper presents a method for comparative efficiency analysis to improve the accountability of CWS to their stakeholders while maintaining the level of service. The method is achieved through three objectives, namely: (1) to construct standard efficiency metric parameters based on the techniques of data envelopment analysis; (2) to incorporate these uniform efficiency metric parameters into a transparent decision support system (DSS) based on the standard linear programming resource allocation problem; and (3) to utilize the DSS to determine the efficient allocation of limited budgetary resources among CWS operating as a regional water system (RWS). The paper is a significant departure, in three ways, from the current planning and management approach, which treats CWS as independent entities. First, it provides an open and transparent method for planning and management of CWS; second, it provides a uniform and consistent method for evaluating relative efficiencies across the CWS. Third, the DSS facilitates comparative efficiency analysis across the RWS, and guides financial allocation decisions among CWS operating as a RWS.     

A regional techno-economic energy supply-distribution-demand model for North America

This paper describes an oil, gas, and “other” supply distribution model including sample results. The model was iniated before the energy crisis, and was initially concerned only with oil and gas. It was, however, designed as an open ended model, with provision for the addition of other energy supplies. The incorporation of these is in process, and the paper distinguishes between the existing and developing capabilities of the model.     

A method of MPC model error detection

This work introduces a method of multivariable model error detection in model prediction control (MPC). The idea is to use non-disturbing small sinusoidal test signals to obtain accurate estimates of process frequency responses at several frequency points. Then, the differences between estimated frequency responses and the frequency responses of current MPC model are used to form the model error index matrix which is used to access the model error of the MPC controller. An upper error bound is developed for quantifying the error of frequency response estimation. The method works in closed-loop operation with the MPC controller online. Simulation studies are used to demonstrate the use of the method.     

A hybrid model for spelling error detection and correction for Urdu language

Neural Computing and Applications - Detecting and correcting misspelled words in a written text are of great importance in many natural language processing applications. Errors can be broadly...     

A semi-implicit time-splitting scheme for a regional nonhydrostatic atmospheric model

In this study a semi-implicit finite difference scheme is proposed for the nonhydrostatic atmospheric model based on the Euler equations for compressible ideal gas. Fast acoustic and gravity waves are approximated implicitly, while slow inertial processes are treated explicitly. Such time approximation requires the solution of 3D elliptic equations at each time step. An efficient elliptic solver is based on decoupling in the vertical direction and splitting in the horizontal directions. Stability analysis of the scheme shows that the time step is restricted only by the maximum wind speed and does not depend on the propagation velocity of the fast waves. A specific approximation of the advection terms keeping the second order of accuracy and possessing extended stability is employed to achieve larger time steps. The performed numerical experiments show the computational efficiency of the designed scheme and accuracy of the predicted atmospheric fields.     

A model for nonpolynomial decrease in error rate with increasingsample size

Much theoretical evidence exists for an inverse proportionality between the error rate of a classifier and the number of samples used to train it. Cohn and Tesauro (1992) have, however, discovered various problems which experimentally display an approximately exponential decrease in error rate. We present evidence that the observed exponential decrease is caused by the finite nature of the problems studied. A simple model classification problem is presented, which demonstrates how the error rate approaches zero exponentially or faster when sufficiently many training samples are used.     

甘肃黄土高原地区土壤水热特征分析研究

利用甘肃黄土高原代表站地温、土壤含水量及降水资料,运用统计模拟方法,分析了土壤温度的日变化及土中热交换特性,评述了水热耦合效应。地温的日变化特性用谐波分析方法描述,各季典型天气下24小时热通量值由热平衡台站规范方法计算。结果表明,各层次地温的日变化基本表现为一阶谐波,这种正弦的波形尤以晴天最为明显。不同季节典型天气下土中热通量的变化由正值转为负值的时间基本一致出现在16时,阴天提前1时,由负值转为正值的时间基本一致出现在7时,冬季阴天出现在9时。水热交互作用与土壤含水量的变化有显著相关关系。     

A posteriori error analysis of an augmented mixed-primal formulation for the stationary Boussinesq model

In an earlier work of us, a new mixed finite element scheme was developed for the Boussinesq model describing natural convection. Our methodology consisted of a fixed-point strategy for the variational problem that resulted after introducing a modified pseudostress tensor and the normal component of the temperature gradient as auxiliary unknowns in the corresponding Navier-Stokes and advection-diffusion equations defining the model, respectively, along with the incorporation of parameterized redundant Galerkin terms. The well-posedness of both the continuous and discrete settings, the convergence of the associated Galerkin scheme, as well as a priori error estimates of optimal order were stated there. In this work we complement the numerical analysis of our aforementioned augmented mixed-primal method by carrying out a corresponding a posteriori error estimation in two and three dimensions. Standard arguments relying on duality techniques, and suitable Helmholtz decompositions are used to derive a global error indicator and to show its reliability. A globally efficiency property with respect to the natural norm is further proved via usual localization techniques of bubble functions. Finally, an adaptive algorithm based on a reliable, fully local and computable a posteriori error estimator induced by the aforementioned one is proposed, and its performance and effectiveness are illustrated through a few numerical examples in two dimensions.     

A posteriori error analysis of an augmented fully-mixed formulation for the stationary Boussinesq model

In this paper we undertake an a posteriori error analysis along with its adaptive computation of a new augmented fully-mixed finite element method that we have recently proposed to numerically simulate heat driven flows in the Boussinesq approximation setting. Our approach incorporates as additional unknowns a modified pseudostress tensor field and an auxiliary vector field in the fluid and heat equations, respectively, which possibilitates the elimination of the pressure. This unknown, however, can be easily recovered by a postprocessing formula. In turn, redundant Galerkin terms are included into the weak formulation to ensure well-posedness. In this way, the resulting variational formulation is a four-field augmented scheme, whose Galerkin discretization allows a Raviart–Thomas approximation for the auxiliary unknowns and a Lagrange approximation for the velocity and the temperature. In the present work, we propose a reliable and efficient, fully-local and computable, residual-based a posteriori error estimator in two and three dimensions for the aforementioned method. Standard arguments based on duality techniques, stable Helmholtz decompositions, and well-known results from previous works, are the main underlying tools used in our methodology. Several numerical experiments illustrate the properties of the estimator and further validate the expected behavior of the associated adaptive algorithm.     

基于BART噪声器的中文语法纠错模型

在中文语法纠错中,基于神经机器翻译的方法被广泛应用,该方法在训练过程中需要大量的标注数据才能保障性能,但中文语法纠错的标注数据较难获取.针对标注数据有限导致中文语法纠错系统性能不佳问题,提出一种基于BART噪声器的中文语法纠错模型——BN-CGECM.首先,为了加快模型的收敛,使用基于BERT的中文预训练语言模型对BN...     

一个从ASTER数据中反演地表温度的劈窗算法

根据EOS/Terra多传感器的特点,提出了一个适合于ASTER数据的劈窗算法,该算法包括两个必要的参数大气透过率和比辐射率。大气透过率是通过利用MODIS的3个近红外波段反演大气水汽含量并根据大气水汽含量与热红外波段的统计关系计算得到。由于MODIS和ASTER是在同一颗星上,这种大气透过率估计方法保证了地表温度反演过程中所需大气参数的同步获取。对于比辐射率则是通过分类和JPL提高的光谱库获得。最后用大气模拟校正法对算法进行了验证,在比辐射率已知的情况下,当使用大气模型模拟得到的大气透过率时,对Planck函数优化简化后的平均精度为0.56℃;当大气透过率是从大气水汽含量计算得到时,优化平均精度为0.58℃,表明该算法可行。     

Performance of a spatio-temporal error model for raster datasets under complex error patterns

The CLC (Combined Location Classification) error model provides indices for overall data uncertainty in thematic spatio-temporal datasets. It accounts for the two major sources of error in such datasets, location error and classification error. The model assumes independence between error components, while recent studies revealed various degrees of correlation between error components in actual datasets. The goal of this study is to determine if the likely violation of model assumptions biases model predictions. A comprehensive algorithm was devised to simulate the entire process of error formation and propagation. Time series thematic maps were constructed, and modified maps were derived as realizations of underlying error patterns. Error rate and pattern (positive autocorrelation) were controlled for location error and for classification error. The magnitude of correlation between errors from different sources and correlation between error at different time steps was also controlled. A very good agreement between model predictions and simulation results was found in the absence of correlation in error between time steps and between error types, while the inclusion of such correlations was shown to affect model fit slightly. Given our current knowledge of spatio-temporal error patterns in real data, the CLC error model can be used reliably to assess the overall uncertainty in thematic change detection analyses.