农作物面积空间抽样调查研究进展

农作物面积信息对于准确估计农作物产量、制定农业政策和确保国家粮食安全具有重要意义。该文主要总结了国内外农作物面积空间抽样调查方法,主要回顾了美国、欧盟和中国的农作物面积空间抽样调查的发展历程。该文还预测了农作物面积空间抽样调查的发展趋势,希望能对农作物面积调查的研究和应用提供指导。     

流域综合管理认知度的量化调查和层级差异分析——以长江流域为例

长江流域自然条件复杂,经济发展不均衡,长期的资源消耗和环境污染,使流域管理面临体制、机制、形式、方法、手段等种种挑战,流域综合管理是公认的改革方向。从法律法规、协调机制、信息共享、公众参与4个角度观察长江流域的管理现状,收集原始信息,采用定量分析,剖析国家、省、流域3个层面的认知差异,分析结果可为辨识流域管理的薄弱环节及选择改革的突破点提供量化支撑。     

离散采样偏差造成的信噪比损失分析与改善

分析了离散采样偏差造成的目标信噪比损失,提出采用幅度最大原则搜索获得采样位置偏差值,利用该偏差值在频域完成对采样偏差的补偿,从而改善信噪比的损失。讨论了该算法的应用条件,分别对强散射点目标和弱散射点目标情况进行了仿真,分析了随着信噪比变化该算法对信噪比损失改善的效果,验证了算法的有效性。算法具有较低的运算复杂度,因而在实际系统中易于实现。     

Application of bootstrap re-sampling method in statistical measurement of sustainability

Sustainability is of essential interest for many organizations and is defined as the ability to maintain existing resources at a certain rate or level when encountered with barriers. Factors affecting sustainability are categorized as enablers (capacities) and barriers (challenges) that have positive and negative effects on sustainability, respectively. To evaluate the status of sustainability, organizations need a measurement method to account for all the aspects of the sustainability classified into social, economic, and environmental tiers. Previously many researchers have provided indexes and measure specific to the studied field, which is not applicable to other areas. Furthermore, the proposed methods fail to cover all the aspects of sustainability. This study investigates a statistical method to measure the sustainability and the application of the bootstrap re-sampling method in order to overcome the problem with normality assumption when the sample size is not large enough and thus develop a more realistic stochastic model. The Bootstrap re-sampling method enables the unbiased estimation of population parameters such as mean and standard deviation. The proposed method is evaluated by comparing its results with those found in the literature.     

农作物面积抽样单元空间变异性及其对抽样外推效率的影响

［目的］联合遥感和传统抽样方法的空间抽样技术是进行大区域农作物面积监测的有效手段。但传统抽样要求抽样单元间相互独立，并未考虑到区域农作物空间变异性的存在对农作物面积空间抽样效率是否有影响及其影响程度如何，从而限制了农作物面积监测的效率和精度。［方法］文章以吉林省德惠市为研究区，利用变异函数分别对10种抽样单元尺度内玉米和水稻种植面积的空间变异性进行定量评价。选择3种空间抽样方案（简单随机抽样、系统抽样、分层抽样），分别计算不同空间变异程度下的样本容量（n）、抽样相对误差（Re）和总体总值估计量的变异系数（CV（Y︿））作为空间抽样效率的评价指标，定量分析空间变异性对农作物面积空间抽样效率的影响，提出适宜空间变异农作物的空间抽样优化方案。［结果］(1)玉米和水稻面积的空间变异性（反映为基台值C0+C）均随着抽样单元尺度的增加逐渐而减小，其中500m×500m和2 500m×2 500m抽样单元尺度下农作物面积的空间自相关程度相对最小。(2)相同抽样比下，农作物面积在3种抽样方法下的相对误差和变异系数均随着空间变异性的减小（抽样单元尺度增加）逐渐增大，其中玉米和水稻在分层抽样条件下的相对误差和变异系数具有相对最小值，分别限制在10%和20%以内，其次为系统抽样（Re<40%，CV（Y︿）<88%）和简单随机抽样（Re<50%，CV（Y︿）<75%）。 在抽样方法被选定的情况下，两种农作物的分层抽样相对误差变化率随着抽样比的增加迅速降低，当抽样比增至5%时，其相对误差和变异系数的变化率基本趋于稳定，继续增加抽样比来提高抽样精度的效果甚微。 在3 500m×3 500m抽样单元尺度内，按5%抽样比确定的样本容量对玉米和水稻面积进行分层抽样时，可达到95%的抽样精度（Re<5%）。 综合农作物在不同抽样单元尺度下的空间变异特征和空间抽样效率，基于2 500m×2 500m的抽样单元尺度和5%抽样比进行分层抽样的空间变异性农作物空间抽样方案是一种高精度、低成本、代表性强的空间抽样方案。［结论］该研究为区域农作物面积抽样调查方法的改善和抽样效率的提高提供技术支持和参考依据。     

An effective hybrid variance reduction method for pricing the Asian options and its variants

In this paper, we propose a variance reduction method that combines importance sampling and control variates to price European Arithmetic Asian options and its variants (i.e., Asian options plus knock-in or knock-out options) under the Black-Scholes model. The numerical results show that the proposed methods are especially efficient under the following scenarios: in the money, low volatility, more sampling dates, and higher barrier thresholds.     

A proposed configurable approach for recommendation systems via data mining techniques

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.