Study of haze emission efficiency based on new co-opetition data envelopment analysis

As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co-opetition matrices of Chinese provinces, then builds the co-opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co-opetition DEA cross-efficiency model that integrates haze's feature of cross-border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision-making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co-opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co-opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results.     

Display reflectance: Basics,measurement, and rating

Abstract— In this paper, methods and instruments for measurement and evaluation of reflection characteristics are reviewed as needed for research and development of electronic displays and for material and surface modeling with ray‐tracing and rendering software packages. Contrast under ambient illumination and recognizability under daylight illumination are prime development targets in the electronic‐display field, while computation and synthesis of realistic scenes and objects are pushing the need for physical data in computer graphics applications. Three categories of instruments are available for detailed reflection analysis. They are based on (1) gonioscopic (mechanical) and conoscopic (optical) directional scanning, (2) imaging approaches, and (3) on arrangements with variable source or receiver aperture. The capabilities, advantages, and limitations of these methods are introduced and discussed in order to facilitate appropriate selection of methods and instruments. For illustration purposes we present typical results obtained from commercial electronic display screens. A basis for continued widespread implementation and standardization of reflection metrology as required for objective rating and comparison of electronic‐display screen performance under ambient illumination is provided.     

基于多尺度特征多对抗网络的雾天图像识别

目的 当前的大型数据集,例如ImageNet,以及一些主流的网络模型,如ResNet等能直接高效地应用于正常场景的分类,但在雾天场景下则会出现较大的精度损失。雾天场景复杂多样,大量标注雾天数据成本过高,在现有条件下,高效地利用大量已有场景的标注数据和网络模型完成雾天场景下的分类识别任务至关重要。方法 本文使用了一种低成本的数据增强方法,有效减小图像在像素域上的差异。基于特征多样性和特征对抗的思想,提出多尺度特征多对抗网络,通过提取数据的多尺度特征,增强特征在特征域分布的代表性,利用对抗机制,在多个特征上减少特征域上的分布差异。通过缩小像素域和特征域分布差异,进一步减小领域偏移,提升雾天场景的分类识别精度。结果 在真实的多样性雾天场景数据上,通过消融实验,使用像素域数据增强方法后,带有标签的清晰图像数据在风格上更趋向于带雾图像,总的分类精度提升了8.2%,相比其他的数据增强方法,至少提升了6.3%,同时在特征域上使用多尺度特征多对抗网络,相比其他的网络,准确率至少提升了8.0%。结论 像素域数据增强以及多尺度特征多对抗网络结合的雾天图像识别方法,综合考虑了像素域和特征域的领域分布差异,结合了多尺度的丰富特征信息,同时使用多对抗来缩小雾天数据的领域偏移,在真实多样性雾天数据集上获得了更好的图像分类识别效果。     

雾霾成因及严寒地区乙醇汽油对雾霾影响

本文通过分析雾霾的组成成分及其形成机理,分别研究了乙醇汽油燃烧产物气溶胶粒子、颗粒物、附加产物(醛酮等)和水分对严寒地带区域性雾霾形成的影响.研究结果表明,乙醇汽油燃烧产物对雾霾的影响也是比较严重的,在严寒地区冬季不建议过量使用乙醇汽油.     

明亮区域的暗原色先验算法

针对暗原色先验算法在处理含有大面积明亮区域的有雾图像时,复原图像会产生严重色彩偏移的不足,提出了一种基于像素点的透射率修正方案.在分析暗原色先验去雾原理以及复原图像色彩偏移成因的基础上,对去雾模型进行推导,使用暗通道亮度与大气光数值接近度及像素通道间数值接近度作为明亮区域判定机制,得出更具普适性的透射率求取方法.明亮区域采用容差机制纠正错误估计的透射率,非明亮区域仍采用原透射率求取方式,并首次将改进算法应用到雪天模糊图像清晰化中.实验结果表明,改进算法使去雾图像清晰自然,可有效修正色彩失真.     

关于雾霾污染的现状及治理方案的探讨＊

雾霾是雾与霾的混合物,雾霾湿度相对较大时,雾所占的成分居多;而在相对湿度为80%～90%时,霾所占的比例较大。在雾霾天气下,人们在室外的能见度特别低,给行车出行带来很大的不便,同时,还会给人们的健康带来严重的威胁。主要对雾霾污染的现状进行了分析,并重点提出治理雾霾污染的有效方案。     

纳米TiO_2/PC-PP光扩散复合材料的结构与性能

采用熔融复合的方法制备了纳米TiO_2/聚碳酸酯-聚丙烯(TiO_2/PC-PP)光扩散复合材料。分别采用SEM和DSC研究了纳米TiO_2/PC-PP复合材料的微观结构和等温结晶性能,并研究了复合材料的热稳定性与光学性能。结果表明:纳米TiO_2的加入使得光扩散体系中分散相的平均尺寸变小,形态更加均一,并且随着复合材料中纳米TiO_2质量分数的增加,复合材料的半结晶时间逐渐延长,总体结晶速率变慢。纳米TiO_2的加入使复合材料的初始热降解温度以及最终残余量升高,当PP中纳米TiO_2质量分数为5%时,复合材料的透光率达到80%,雾度为88%,此时纳米TiO_2/PC-PP复合材料的光扩散性能较好。     

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针对国内外尚无对霾的遥感监测,霾在NASA的MODIS产品中作为薄云处理的情况,基于MODIS数据,采用.NET分布式架构,结合MatlabCOM, ArcEngine等组件,建立一个区域霾监测系统,论述系统的设计思想及改进的霾反演算法,对华北地区部分霾天进行监测,结果显示该系统能够准确地模拟大气污染状况。     

基于雾气浓度估计的图像去雾算法

根据雾气浓度的视觉特征,提出一种雾气浓度估计模型.在此基础上,结合大气散射模型,提出一种新的图像去雾算法.首先,基于雾气浓度估计模型计算出雾气浓度量化图,利用模糊聚类算法在量化图中识别出雾气最浓区域并估计出全球光; 然后,对量化图中的“非雾气最浓”区域再次进行聚类处理,根据文中所提最优透射率评价指标估计出每个聚类单元的透射率,将全球光与透射图以及有雾图像导入散射模型,便可达到去雾的目的; 最后,针对去雾后图像较实际场景偏暗,提出一种基于小波域的多尺度锐化算法进行增强处理,以改善其主观视觉质量.实验结果表明,本文算法与现有主流算法相比,具有更好的去雾效果,并且其计算速度也相对较快.     

基于STM32大气动态雾霾检测系统的设计

大气的质量直接关系到人们的生存环境,衡量大气污染的程度和对身体健康造成的伤害,都离不开对大气的检测;如何进行检测,用什么方法来评判,文章提出一种方法来实时检测大气雾霾的状况,尤其是PM2.5(空气动力学当量直径小于等于2.5微米的颗粒物)被认为是造成雾霾天气的“元凶”;大气雾霾监测系统利用雾霾检测器和GPRS传导,将所在地雾霾情况实时监测传输给手机APP终端,手机接收之后将数据整合显示给使用者,达到雾霾监测和提前应对恶劣天气的作用。