Accuracy Evaluation Indexes Research Articles

A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies. This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models. Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied. Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes. An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect. The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models. The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.

New Accuracy Evaluation Index for Track Fusion Algorithms

When evaluating the track fusion algorithm, common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one, and fail to effectively distinguish the performance of the algorithm when the state estimation is similar. Therefore, it is necessary to construct a high-resolution evaluation index, which can evaluate the track fusion algorithm more accurately, reasonably and comprehensively. Firstly, the advantages and disadvantages of the optimal subpattern assignment (OSPA) distance as the accuracy index to evaluate the track fusion algorithm are analyzed. Then, its deficiencies are improved by using the Hellinger distance instead of the original Euclidean distance, and the distance is index transformed. Finally, a new evaluation index for track fusion algorithms is proposed, which is the OSPA distance based on Hellinger distance and index transformation. The simulation results show that the new index can not only correctly evaluate the fusion precision, but also consider the state uncertainty, making that can evaluate the track fusion algorithm more sensitively, and effectively solves the sensitivity of the index to the cut-off parameter c through index transformation.

District heating systems load forecasting: a deep neural networks model based on similar day approach

ABSTRACT Accurate heat load forecasting is an important issue to ensure the reliable and efficient operation of a district heating system. In this paper, a hybrid model that combines similar day (SD) selection and Deep Neural Networks (DNNs) to construct SD-DNNs model for short-term load forecasting (STLF) is presented. A new Euclidean Norm (EN) weighted by eXtreme Gradient Boosting (XGBoost) is used to evaluate the similarity between the forecasting day and historical days. In this EN, the outdoor temperature, wind force and day-ahead load are simultaneously considered. And eight features are chosen as inputs of the DNNs to predict the heat load. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are used as accuracy evaluation indexes. The experimental results demonstrate that the SD-DNNs model can accurately forecast the heat load.

Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models

The uncertainty research of hydrological forecast attracts the attention of a host of hydrological experts. Prediction Interval (PI) is a convinced method that can ensure the forecasting accuracy meanwhile take uncertainty range into consideration. While the existed Prediction Interval methods need algorithm optimization and are susceptible to local optima, so it is particularly urgent to provide an efficient Prediction Interval (PI) model with excellent performance. This paper proposes a novel upper and lower bound interval estimation model to rapidly define the PI and reduce the amount of calculation to implement convenient and high precise hydrological forecast. Above all, the ideal upper and lower bounds are defined according to the relative width or absolute width. Then, the proposed model is utilized to forecast interval runoff via least square method and multiple linear regression methods. The estimated interval inclusion ratio, interval width, symmetry, and root-mean-square error which are popular used to judge the precision serve as accuracy evaluation indexes. The measured discharge data from five hydrological stations which located upstream of the Yangtze River is applied for interval forecasting. Compared with the results of neural network-based upper and lower bound interval estimation model, the proposed method yields higher forecasting accuracy, meanwhile, the ideal upper and lower bounds successfully minimize the number of processes which require a mass of parameter searching and optimization.

浑善达克沙地6种灌木生物量模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 浑善达克沙地6种灌木生物量模拟 DOI: 10.5846/stxb201803290637 作者: 作者单位: 中国林业科学研究院荒漠化所,中国林业科学研究院 林业新技术研究所,内蒙古农业大学沙漠治理学院,中国林业科学研究院 荒漠化研究所,中国林业科学研究院荒漠化所,华北油田技术监督检验处计量中心站 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0500801-03）；中国林业科学研究院基金（IDS2018JY-3，IDS2018JY-4）；国家自然科学基金项目（41401212） Biomass simulation of six shrub species in Otindag sandy land Author: Affiliation: Institute of Desertification Studies, Chinese Academy of Forestry,,,,Chinese Academy of Forestry, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在干旱半干旱区，乔木比较稀疏或难以存活，灌木往往在植被群落中占有很大的优势地位，其生态功能及生态学意义尤其值得重视。浑善达克沙地疏林草地是沙地顶级植物群落，其中乔木稀疏分布，而灌木在沙甸以及沙陇背风坡呈密集分布。灌木在固定沙丘、改良土壤、提供栖息地等方面具有重要的生态意义，其生物量组成也在沙地植被群落中占有很大比重。已往的研究中，灌木相比乔木通常处于次要地位，对灌木的研究尚不充分，灌木生物量的模拟方法亦多采用乔木生物量的模拟方法。然而灌木形态结构与乔木有明显差异，专门针对灌木的生物量模拟方法研究尚不多见。以6种沙地灌木为样本，基于异速生长模型，对比了若干地表测量指标对灌木生物量的预测能力，其中设计了一种更贴近灌木实际形态的圆台体积作为新的预测指标。研究结果表明：（1）在单因素指标中，相比高度和地径，冠幅与灌木生物量的相关性更强。（2）相比单因素指标，复合指标与灌木生物量之间的相关性更强。其中冠幅相关的复合指标更优于地径相关的复合指标。这预示着冠幅以及冠幅相关的复合指标对灌木生物量具有较好的预测能力。（3）圆台体积能进一步提高对灌木生物量的预测能力。相关分析和拟合评价结果显示，圆台体积与灌木生物量的相关性更强，拟合误差较小，并且对于不同的灌木种类，其相关性和拟合精度表现出较高的稳定性。这意味着圆台体积对于不同的灌木种类，均具有较好的生物量预测效果。因此建议，在灌木属性测量较为充分的情况下，圆台体积是更为理想的预测指标，而在测量不充分情况下，冠幅及其相关复合指标更适宜进行灌木生物量预测。研究结果最终建立了6种沙地灌木的圆台体积-生物量的异速生长模拟方程，为进一步研究沙地灌木的碳储量以及灌木在半干旱植物群落中的生态意义提供科学基础。 Abstract:In arid and semi-arid regions, trees are sparse or have difficulty surviving, and shrubs often occupy a dominant position in vegetation communities. The ecological functions and ecological value of shrubs are particularly worthy of attention in these regions. The sparse-elm grassland in Otindag Sandy Land is the climatic climax community; the trees are sparsely distributed, while the shrubs are densely distributed between the dunes or on the leeward slopes of the dunes. Shrubs have significant ecological functions in fixing sand dunes, improving soils, providing habitats, and increasing vegetation biodiversity. Shrub biomass accounts for a large proportion of the sandy land vegetation community. According to previous studies, shrubs are usually less important than trees, but the research on shrubs is not yet sufficient. The present shrub biomass simulation method is mostly adapted from the methods used for trees. However, the morphological characteristics of shrubs are obviously different from those of trees. A biomass prediction model specific for shrubs urgently needs to be developed. In this study, six dominant shrub species were investigated. Based on an allometric model, seven shrub measurements were used as predictors and compared to assess their abilities to predict shrub biomass. Among the seven measurements, a circular platform volume model, which has a structure close to the actual shape of a shrub, was designed as a new predictor. In this study, correlation coefficients and three model-fitting accuracy evaluation indices[including determination of the R2 coefficient, significance index p-values, and standard error of the estimate (SEE)] were used to assess the prediction ability of the seven measurements. The results showed that:(1) Among the single measurements, the correlation coefficient between the crown diameter and biomass was highest, compared to those between height or ground diameter and biomass. (2) The correlation between the composite indices and shrub biomass was much stronger than that between the single measurements and biomass. In addition, the crown-related composite indices had stronger correlations with biomass than the ground diameter-related composite index did. This indicates that the crown diameter or crown-related composite indices may be better in predicting shrub biomass. (3) The circular platform volume model further improved the ability to predict shrub biomass. The results of correlation analysis and fitting analysis showed that the correlation between the circular platform volume and biomass was stronger and the fitting error was smaller compared with other prediction indicators. The correlation and fitting accuracy between circular platform volume and biomass were similar across different shrub species. This means that the circular platform volume model has better and more-stable prediction ability than other models for many shrub species, which implies that it is also more scalable. Therefore, we suggest that the circular platform volume is an ideal predictor when shrubs are adequately measured in the field. Otherwise, if the shrubs are not sufficiently measured, the crown diameter and crown-related composite indices are more suitable for the prediction of shrub biomass. In sum, this study established a biomass prediction model based on the circular platform volume of the six sandy shrub species, which provides a scientific basis for further study of the shrub carbon sink in sandy land and the ecological significance of shrubs in semi-arid and arid regions. 参考文献 相似文献 引证文献

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

Research on Nonlinear Decoupling Method of Piezoelectric Six-Dimensional Force Sensor Based on BP Neural Network

The six-dimensional force sensor has become one of the major bottlenecks restricting the development of robots in China. In this paper, the problem of the decoupling of the piezoelectric six-dimensional force sensor with four-point support structure is studied, and the static decoupling method is studied. Firstly, the principle of nonlinear decoupling algorithm for six-dimensional force sensor is analyzed, and experimental data obtained by decoupling are acquired through calibration experiments, and sample selection and normalization processing are performed. After that, the BP forward feedback neural network was used to optimize the multi-dimensional nonlinear characteristics of the sensor output system, and the input and output mapping relationship of the sensor was determined, and the decoupled sensor output data was obtained. The determinant sensor’s measurement accuracy evaluation index is compared with linearity error and coupling rate error.

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演

PDF HTML阅读 XML下载 导出引用 引用提醒 1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演 DOI: 10.5846/stxb201703290547 作者: 作者单位: 南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划资助项目（2017YFC0505505）；国家自然科学基金项目（31200534）；江苏高校优势学科建设工程资助项目；2014年江苏省普通高校学术学位研究生科研创新计划项目 Quantitative inversion of long sequential leaf area index using remote sensing based on BP neural network from 1988 to 2013 Author: Affiliation: Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:叶面积指数（Leaf Area Index，LAI）高度综合了植被水平覆盖状况和垂直结构，以及枯枝落叶层厚薄和地下生物量多少，是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此，及时准确地掌握区域尺度上长时间序列的植被LAI，对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像，基于反向传播（Back Propagation，BP）神经网络构建LAI反演模型，进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值，验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高，2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174，0.2962和0.2581，0.7713和0.6844，各项精度评价指标均较好；（2）统计分析去除耕地后全市LAI变化，低植被覆盖（LAI<2）面积不断增加，高植被覆盖区（LAI>3）面积先减少后增加，耕地面积不断减少，符合南京市的发展变化规律；（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致，反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的，为区域尺度土壤侵蚀定量遥感监测提供新途径。 Abstract:Leaf Area Index(LAI) can reflect the horizontal coverage, vertical structure of vegetation, the thickness of the litter layer and the amount of underground biomass, which is the main aspect of vegetation affecting soil erosion. It is very important to monitor the changes in the amount of soil erosion, for useful information to guide the planning of soil and water conservation, protect the soil and water resources and control the soil erosion. Therefore, the method by which we obtain high quality and long sequential LAI at a regional scale is very important for analyzing the relationship between the dynamic changes in soil erosion and vegetation. Previous studies showed that the neural network had an incomparable superiority in terms of complex, nonlinear data fitting and pattern recognition, and had been successfully applied to inverse the LAI in Nanjing based on the multi-spectral remote sensing data derived from the Landsat 8 Operational Land Imager(OLI), four types of vegetation indices(Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI; Soil-adjusted Vegetation Index, SAVI; Modified Soil adjusted Vegetation Index, MSAVI), and measured LAI data. The results showed that the accuracy of retrieval was good. In this paper, we used the Back Propagation(BP) neural network model to inverse the LAI in Nanjing during 1988-2013 based on the data derived from Landsat 8 OLI and Landsat 5 Thematic Mapper(TM). Based on the measured values of LAI in 2009 and 2010, the evaluation accuracy and adaptability of the model were verified and discussed. The results showed that:(1) The model had a fitting of higher degree, and average relative errors(MAPE), root mean square errors(RMSE), and correlation coefficients(R) in 2009 and 2010 of 0.2395 and 0.2174, 0.2962 and 0.2581, and 0.7713 and 0.6844, respectively. Each accuracy evaluation index was good.(2) Following statistical analysis, we found that the low vegetation coverage area(LAI < 2) exhibited an increasing trend, the high vegetation coverage area(LAI > 3) presented a first decreasing and then increasing trend, while the cultivated land area decreased with the rapid development in Nanjing.(3) To analyze accurately the LAI, we extracted the LAI in the main urban area, and found that there was a relatively high inversion value, and the inter-annual change in LAI was consistent with the change in vegetation coverage in Nanjing reported by other studies. Therefore, we could see that the BP neural network model had a high accuracy for the time series LAI inversion. It provides a new way for quantitative remote sensing monitoring of regional soil erosion. Moreover, because of other potential limiting factors, such as the errors produced by the BP neural network model, the large area of the inversion area, the complexity of vegetation types and community structure, etc., the inversion accuracy of LAI through remote sensing still needs to be explored, and the inversion method improved. We will try to establish a multi-angle LAI inversion method to construct the coupling model of LAI and soil erosion or quantitative fusion of multi-source remote sensing images in the future study. 参考文献 相似文献 引证文献

A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China

Satellite-derived aerosol optical depth (AOD) has been proven effective for estimating ground-level particles with an aerodynamic diameter <2.5μm (PM2.5) concentrations. Using a time fixed effects regression model, we compared the capacity of two AOD sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), to estimate ground-level PM2.5 concentrations over a heavily polluted region in China. Regarding high-quality AOD data, the results show that the VIIRS model performs better than the MODIS model with respect to all model accuracy evaluation indexes (e.g., the coefficient of determination, R2, of the VIIRS and MODIS models are 0.76 and 0.71 during model fitting and 0.72 and 0.66 in cross validation, respectively), the potential for capturing high PM2.5 concentrations, and the precision of annual and seasonal PM2.5 estimates. However, the spatiotemporal coverage of the high-quality VIIRS AOD is inferior to that of the MODIS AOD. We attempted to include medium-quality VIIRS AOD data to eliminate this, while exploring its influence on the performance of the VIIRS model. The results show that it improves the spatiotemporal coverage of the VIIRS AOD dramatically especially in winter, although a decline in model accuracy occurred. Compared to the MODIS model, the VIIRS model with both high-quality and medium-quality AOD data performs comparably or even better with respect to some model accuracy evaluation indexes (e.g., the model overfitting degree of the VIIRS and MODIS models are 7.46% and 5.82%, respectively), the potential for capturing high PM2.5 concentrations, and the precision of annual and seasonal PM2.5 estimates. Nevertheless, the VIIRS models did not perform as well as the MODIS model in summer. This study reveals the advantages and disadvantages of the MODIS and VIIRS AOD in simulating ground-level PM2.5 concentrations, promoting research on satellite-based PM2.5 estimates.

Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China

Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has suffered widespread and significant landslide events in recent years. In our study, an object-oriented landslide mapping (OOLM) framework was proposed for reliable and accurate LM from ‘ZY-3’ high spatial resolution (HSR) satellite images. The framework was based on random forests (RF) and mathematical morphology (MM). RF was first applied as an object feature information reduction tool to identify the significant features for describing landslides, and it was then combined with MM to map the landslides. Three object-feature domains were extracted from the ‘ZY-3’ HSR data: layer information, texture, and geometric features. A total group of 124 features and 24 landslides were used as inputs to determine the landslide boundaries and evaluate the landslide classification accuracy. The results showed that: (1) the feature selection (FS) method had a positive influence on effective landslide mapping; (2) by dividing the data into two sets, training sets which consisted of 20% of the landslide objects (OLS) and non-landslide objects (ONLS), and test sets which consisted of the remaining 80% of the OLS and ONLS, the selected feature subsets were combined for training to obtain an overall classification accuracy of 93.3% ± 0.12% of the test sets; (3) four MM operations based on closing and opening were used to improve the performance of the RF classification. Seven accuracy evaluation indices were used to compare the accuracies of these landslide mapping methods. Finally, the landslide inventory maps were obtained. Based on its efficiency and accuracy, the proposed approach can be employed for rapid response to natural hazards in the Three Gorges area.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional non-homogenous discrete grey forecasting model (NDGM), the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence (MAPEM) and mean percent of interval sequence simulating value set covered (MPSVSC), are defined and, the procedure of the interval grey number sequence based the NDGM (IG-NDGM) is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM(1,1) model and GM(1,1) model.

Analytical Modeling and its Validation for Data Acquisition of ADS-B System

Based on the accuracy and integrity quality evaluation indexes of ADS-B navigation data, this paper foucses on the analytical modeling of ADS-B navigation data source under the global positioning system (GPS) and Compass system. The factors related to accuracy and integrity of ADS-B are derived and simulated, which include the position estimated uncertainty (EPU) and horizontal position integrity containing radius (RC). It is the first to give the quality index levels of the ADS-B horizontal data source in Tianjin under GPS and Compass. The simulation results show that ADS-B navigation data availability under Compass not only meet the lowest requirements recommended by the civil aviation administration of China (CAAC), but also is better than under Compass. The results derived from this paper provide the theoretic support for the implementation of ADS-B in Tianjin Binhai international airport and other areas of China. At the same time, Compass being navigation source increases reliability and security for ADS-B implementation of our country.

A method for evaluating the accuracy of power system state estimation results based on correntropy

Power system state estimation (SE) is a crucial basic function in energy management systems (EMSs) which offers the basic load flow models. It is a critical problem to check whether the SE results are credible enough to be used for online decision making or close-loop control. However, there are few published works focus on this topic till now. The accuracy of SE results can be affected by various factors, many of them are very difficult to quantify. Hence a feasible method is to estimate the accuracy of SE results based on residuals. Due to the huge number of measurements in real power systems, it is necessary to establish a reasonable scalar index that represents the residuals of all measurements and directly indicates the accuracy of the SE results. This paper provides a systematic research on the SE accuracy evaluation problem and proposes a new SE accuracy evaluation index based on correntropy. Some conventional SE accuracy evaluation indices are also introduced and compared theoretically and also through extensive numerical tests.

The Effects of Spatial Resolution on the Maize acreage estimation by Remote Sensing

Crop acreage estimation is essential to forecast crop production using remote sensing. The different spatial resolution of remotely sensed data directly affects the accuracy of crop acreage estimation. It is necessary and valuable to study the effect of resolution on crop acreage estimation, from both qualitative and quantitative points of view. Therefore, this paper analysed the resolution effect on the accuracy of acreage estimation by using CBERS-02B imagery. Spatial statistics methods and manifold accuracy evaluation indices were used respectively to analyse the data with different spatial resolutions and crop proportion statistics. The study results indicate that decreased spatial resolution will lead to reduced regional accuracy in addition to increased standard deviation, RMSE and bias due to the augmentation of mixed pixels. A replacement of higher resolution data by lower resolution data will have an important impact on the derived crop proportions. The regional accuracy of crop statistics can remain higher than 88%, when the crop proportion is higher than 40%. In summary, the higher resolution of the imagery can lead to increased average regional accuracy. The results of this paper also provide academic and experimental reference to resolve the problem of data selection in crop acreage estimation by remote sensing.

Comprehensive Evaluation of CNC Machine Tools Accuracy Based on AHP

In order to evaluate the accuracy of a numerical control machine tool comprehensively, the accuracy evaluation index system of a numerical control machine tool was established, and the priorities of accuracy indexes were determined by analytic hierarchy process method. Taking a five-axis numerical control machine tool for example, each accuracy index of it was studied by using fuzzy comprehensive evaluation, and a membership function was constructed by the intermediate form trapezoidal distribution. The evaluated result indicated that the accuracy of this numerical control machine tool was excellent. As a reference, the method presented in this paper could be used to evaluate the accuracy of other numerical control machine tools.

Application of Extenics Theory to Accuracy Evaluation of Machining Center

The matter-element model for the accuracy evaluation of the machining center is established based on extenics theory, the accuracy evaluation indexes of machining cente are made of multiple elements. The relational degree is calculated by introducing the relational function in the extension set theory, and the weights of evaluation indexes are defined with AHP (the analytic hierarchy process), so the awaiting evaluation machining center is determined though the accuracy grade. A type of machining center as an example is studied the practical application of the proposed method, the result is reasonable and shows that the method possesses a certain practicability.

Accuracy Evaluation Index for Running Transfer Path Analysis

In this study, an accuracy evaluation index for running transfer path analysis (TPA) employing principal component regression method was considered and the reliability was verified. To evaluate the TPA accuracy, the consistency of response signals in which the calculated response signal from the TPA model was compared with the measured response signal was used frequently until now. Also, the condition number that indicates the inverse matrix condition used in the calculation procedure of acceleration transfer function was employed in some cases. In addition to these accuracy evaluation methods, correlated principal component number (CPCN), that indicates the number of principal component correlating with the response signal generated at the running test, was proposed. The reliabilities of these accuracy evaluation methods were verified through simulation. As a result, the conventional two methods (consistency method and condition number) could not evaluate the accuracy well. However, CPCN index could indicate the frequency bands where the TPA accuracy was high or low. Consequently, the new index was found to be a suitable index for accuracy evaluation of the running TPA.

Consideration and Verification of Accuracy Evaluation Indices for Operational TPA

Consideration and Verification of Accuracy Evaluation Indices for Operational TPA