论环境会计的计量与报告

建立和推行环境会计管理，在我国有现实必要性；环境会计的计量基础可以采用机会成本、边际成本、替代成本；具体的计量方法可采用效用效益分析法、数量模型法；环境会计报告主要披露生态效益、社会效益指标。

Variable Relation Parametric Model on Graphics Modelon for Collaboration Design

A new approach to variable relation parametric model for collaboration design based on the graphic modelon has been put forward. The paper gives a parametric description model of graphic modelon, and relating method for different graphic modelon based on variable constraint. At the same time, with the aim of engineering application in the collaboration design, the autonmous constraint in modelon and relative constraint between two modelons are given. Finally, with the tool of variable and relation dbase, the solving method of variable relating and variable-driven among different graphic modelon in a part, and double-acting variable relating parametric method among different parts for collaboration are given.

Spatiotemporal interpolation of precipitation across Xinjiang, China using space-time CoKriging

In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.

Measurement of Mass Transfer Coefficients and Their Modelling of Continuous Counter-Current Aqueous Two-Phase System in a Packed Extraction Column

Overall dispersed side volumetric mass transfer coefficients for protein and amino acids were measured in continuous countercurrent PEG4000/KHP aqueous two-phase systems in a 57mm I.D. packed extraction column. A model for overall dispersed side volumetric mass transfer coefficients was derived by describing the motion of the drops based upon Navier-Stokes equation combined with the relationship between mass transfer coefficients and the drop velocity. The model provides good predictions and can be successfully used in aqueous two-phase extraction. The average relative deviation between calculated values and experimental data ranges from 8% to 14%.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

DEM Production/Updating Based on Environmental Variables Modeling and Conflation of Data Sources

Availability of digital elevation models （DEMs） of a high quality is becoming more and more important in spatial studies. Standard methods for DEM creation use only intentionally acquired data sources. Two approaches which employ various types of data sets for DEM production are proposed： （1） Method of weighted sum of different data sources with morphological enhancement that conflates any additional data sources to principal DEM, and （2） DEM updating methods of modeling absolute and relative temporal changes, considering landslides, earthquakes, quarries, watererosion, building and highway constructions, etc. Spatial modeling of environmental variables concerning both approaches for （a） quality control of data sources, considering regions, （b） pre-processing of data sources, and （c） processing of the final DEM, have been applied. The variables are called rate of karst, morphologic roughness （modeled from slope, profile curvature and elevation）, characteristic features, rate of forestation, hydrological network, and rate of urbanization. Only the variables evidenced as significant were used in spatial modeling to generate homogeneous regions in spatial modeling a-c. The production process uses different regions to define high quality conflation of data sources to the final DEM. The methodology had been confirmed by case studies. The result is an overall high quality DEM with various well-known parameters.

Enhancing pose accuracy of space robot by improved differential evolution

Due to the intense vibration durirLg launching and rigorous orbital temperature environment, the kinematic parameters of space robot may be largely deviated from their nominal parameters. The disparity will cause the real pose （including position and orientation） of the end effector not to match the desired one, and further hinder the space robot from performing the scheduled mission. To improve pose accuracy of space robot, a new self-calibration method using the distance measurement provided by a laser-ranger fixed on the end-effector is proposed. A distance-measurement model of the space robot is built according to the distance from the starting point of the laser beam to the intersection point at the declining plane. Based on the model, the cost function about the pose error is derived. The kinematic calibration is transferred to a non-linear system optimization problem, which is solved by the improved differential evolution （DE） algoritlun. A six-degree of freedom （6-DOF） robot is used as a practical simulation example, and the simulation results show： 1） A significant improvement of pose accuracy of space robot can be obtained by distance measurement only; 2） Search efficiency is increased by improved DE; 3） More calibration configurations may make calibration results better.

Efficient Compression of Vector Data Map Based on a Clustering Model

This paper proposes a new method for the compression of vector data map. Three key steps are encompassed in the proposed method, namely, the simplification of vector data map via the elimination of vertices, the compression of re- moved vertices based on a clustering model, and the decoding of the compressed vector data map. The proposed compres- sion method was implemented and applied to compress vector data map to investigate its performance in terms of the com- pression ratio and distortions of geometric shapes. The results show that the proposed method provides a feasible and effi- cient solution for the compression of vector data map and is able to achieve a promising ratio of compression and maintain the main shape characteristics of the spatial objects within the compressed vector data map.

Self-contained eigenvector algorithm applied to the identification of aerodynamic derivatives of bridge model

On one hand, when the bridge stays in a windy environment, the aerodynamic power would reduce it to act as a non-classic system. Consequently, the transposition of the system’s right eigenmatrix will not equal its left eigenmatrix any longer. On the other hand, eigenmatrix plays an important role in model identification, which is the basis of the identification of aerodynamic derivatives. In this study, we follow Scanlan’s simple bridge model and utilize the information provided by the left and right eigenmatrixes to structure a self-contained eigenvector algorithm in the frequency domain. For the purpose of fitting more accurate transfer function, the study adopts the combined sine-wave stimulation method in the numerical simulation. And from the simulation results, we can conclude that the derivatives identified by the self-contained eigenvector algorithm are more dependable.

Vertical vibration characteristics of seated human bodies and a biodynamic model with two degrees of freedom

Understanding the vibration characteristics of a seated human body is critical for evaluation and improvement of ride comfort of various passenger vehicles. There have been very little publications about the vibration characteristics of a seated Chinese human body. By using wide-band white noise excitations and a homemade seat sensor, vertical vibration tests were carried out on 28 volunteers. Apparent masses were obtained for each volunteer at a frequency range of 1-20 Hz for various excitation le-vels. A biodynamic model, which has two degrees of freedom in parallel and includes a frame mass, was chosen to describe the vertical vibration characteristics of the seated human body. The model parameters were identified by means of a Gauss-Newton method with an error function defined in terms of both real and imaginary parts of the apparent mass against frequency. Based on the averaged data of the mass-normalized apparent mass from experiments, the model parameters and corresponding modal parameters were obtained for seated Chinese people at ages of 20-25 with standard weight. The apparent masses predicted by the biodynamic model with identified parameters agree very well with those obtained from experiments. Statistical analysis demonstrates the influence of volunteer’s height and weight on the model parameters for a seated human body.

Progress on quantitative assessment of the impacts of climate change and human activities on cropland change

It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.

Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.

A HYBRID PSO-SA OPTIMIZING APPROACH FOR SVM MODELS IN CLASSIFICATION

Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.