Boosting算法及其在动态视频图像中的应用

Boosting是一种有效的分类器组合方法,它用某个分类算法生成一系列的基分类器,每个基分类器的训练依赖于在其之前产生的分类器的分类结果,基分类器在训练集上的错误率用于调整训练样本的概率分布,最终分类器通过单个基分类器的加权投票建立起来。将Boosting算法应用在动态车型图像检测中,大大提高了对运动过程中车辆的识别能力,对智能交通系统的发展起着推动作用。     

一种基于遗传算法的区域物流网络选优方法

本文利用区域物流的有关理论,探讨了物流网络建设的框架,结合区域物流服务的需要,从第三方物流企业的角度出发,侧重于对区域现有的物流中心进行整合和选择,利用改进的遗传算法对现有物流节点进行最优化选择,从而在根本上提高区域物流网络建模的效率和运行参数的可靠性。     

The determinants of technology adoption by UK farmers using Bayesian model averaging: the cases of organic production and computer usage

We review and implement a reversible jump approach to Bayesian model averaging for the Probit model with uncertain regressors. Two applications are investigated. The first is the adoption of organic systems in UK farming, and the second is the influence of farm and farmer characteristics on the use of a computer on the farm. While there is a correspondence between the conclusions we would obtain with and without model averaging results, we find important differences, particularly in smaller samples. Concerning the adoption of an organic system, we find that attitudes to the sustainability of the current system along with the ability of organic farms alone to satisfy society’s needs for food are influential. Additionally, the source of management information used by the farmer has a significant impact. Regarding the adoption of computers, we confirm the findings of previous work that the level of education affects uptake and that age is a factor determining adoption. We also find that dairy and organic farms are more likely to use a computer. The physical size of the farm is positively associated with the probability of computer use while net farm income has a limited impact.     

改进Adaboost算法对县域粮食作物产量的预测

Adaboost算法是一种典型的机器学习算法,但其算法在训练过程中用时较长.针对这个问题,采用改进的机器学习DWTAdaboost算法与Adaboost算法对1950—2012年内蒙古自治区扎兰屯地区的各项经济统计数据进行分析,比较两种算法模型对数据分析的优劣.结果表明DWTAdaboost算法在保证准确率不变的前提下效率更高.     

Citizen-centered big data analysis-driven governance intelligence framework for smart cities

Sensors and systems within rapidly expanding smart cities produce citizen-centered big data which have potential value to support citizen-centered urban governance decision-making. There exists a wealth of extant conceptual studies, however, further operational studies are needed to establish a specific path towards implementation of such data to governance decision-making with analytical algorithms that are appropriate for each step of the path. This paper proposes a framework for the use of citizen-centered big data analysis to drive governance intelligence in smart cities from two perspectives: urban governance issues and data-analysis algorithms. The framework consists of three layers: 1) A data-merging layer, which builds a citizen-centered panoramic data set for each citizen by merging citizen-related big data from multiple sources in collaborative urban governance via similarity calculation and conflict resolution; 2) a knowledge-discovery layer, which plots the citizen profile and citizen persona at both individual and group levels in terms of urban public service delivery and citizen participation via simple statistical analysis techniques, machine learning, and econometrics methods; and 3) a decision-making layer, which uses ontology models to standardize urban governance-related attributes, personas, and associations to support governance decision-making via data mining and Bayesian Net techniques. Finally, the proposed framework is validated in a case study on blood donation governance in China. This research highlights the value of citizen-centered big data, pushes data-to-decision research from conceptual to operational, synthesizes previously published frameworks for citizen-centered big data analysis in smart cities, and enhances the mutual supplement cross multiple disciplinaries.     

访问控制策略一致性和完备性检测方法研究

为实现对复杂访问控制策略的统一管理,检测并排除冲突和不完备的访问控制策略,提出了利用访问控制策略集合中的偏序信息构造融合主体、客体层级结构的有向无环图模型。首先从理论上证明了构建有向无环图模型的可行性,并提出利用偏序关系构建有向无环图模型的实施算法,然后利用有向无环图模型的节点集合设计了访问控制策略一致性和完备性检测算法,最后对模型构建算法及访问控制策略一致性和完备性检测算法进行实验验证。实验结果表明,融合主体、客体层级结构的有向无环图模型可以有效管理复杂系统中访问控制策略中出现的冲突与策略不完备情况。相关算法可以有效地实现对复杂访问控制策略的统一管理。     

基于改进谱聚类算法在图像分割中的应用

为解决谱聚类算法应用于图像分割时,相似矩阵内存占用较大甚至满溢以及后续计算量大的问题,利用Nystrom方法随机获取一部分样本点,根据样本点和样本点、样本点和非样本点2种相似关系近似表征所有像素点的相似性,得到原图像的近似相似矩阵。在构建上述所需2种相似关系的相似矩阵时,距离度量采用余弦函数。结果表明,采用近邻传播聚类算法代替k-means算法对得到的低维向量子空间聚类,克服了聚类过程对初始值的敏感性,得到的分割结果较稳定,4幅真实图片也验证了研究算法的优越性。改进的谱聚类算法为图像分割的稳定性研究提供了依据。     

A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance

PurposeNowadays, Supply Chain Finance (SCF) has been developing rapidly since the emergence of credit risk. Therefore, this paper used SVM optimized by the firefly algorithm, which is called firefly algorithm support vector machine (FA-SVM), and applied it to SCF evaluation with a different indicator selection.Design/methodology/approachIn this paper, we used FA-SVM to assess the credit risk of supply chain finance with extracted index through correlation and appraisal analysis, and finally determined 3 first-level indicators and 15 third-level indicators. Through the application analysis, 39 SMEs (117 sample data) were selected from the Computer and Electronic Communications Manufacturing Industry as the characteristics for the input variables, to verify the improvement effect of the method relative to the LIBSVM and the classification pretest effect in the credit risk assessment of the SCF.FindingsThe results showed that FA-SVM could improve the accuracy of classification prediction compared with LIBSVM, and decrease the error rate of falseness recognize credible enterprise to untrusted enterprise.Originality/valueThis paper appliedthe firefly support vector machine in the supply chain financial evaluation for the first time. The output variable was described in a more detailed manner during the index define, and the random selection set in the process of FA-SVM data training.     

Is it China that leads the Asian stock market contagion in 2015?

This article examines the influence of China B-share aggregates on major Asian stock market aggregates during 2015 China crash. Results indicate the China market shows strong negative impact and is highly responsible for leading most markets down during the crash.     

基于压缩感知的红外遥感图像重构算法分析

随着航天遥感技术的飞速发展，遥感图像采集数据耗时长、图像数据量大等问题的出现对采样设备和存储设备提出了更高的性能要求。为了解决以上问题，在气象卫星的红外遥感图像的处理中采用了压缩感知理论。通过Matlab建模和仿真，分析了正交匹配追踪算法、梯度投影算法、子空间追踪算法、平滑l0范数算法的性能，并对大量红外图像以不同的采样率进行采样压缩，然后使用多种重构算法重构图像。对比试验显示，几种算法都能以较低的采样率得到完整的红外图像，但平滑l0范数算法在重构精度和运行时间方面都优于其余几种算法，证明了压缩感知在红外遥感图像的处理中具有较大的实用价值。