基于GM-SVR的小样本条件下化工设备可靠性预测

为了准确预测化工设备可靠性趋势，针对化工设备失效寿命数据为小样本的情形，基于灰色估计法与支持向量回归机在小样本数据处理中的优势，建立了失效寿命时间服从三参数威布尔分布的化工设备可靠性模型；结合GM（1，1）和SVR对模型进行参数估计，在压缩机可靠性分析中进行了实例应用，对比分析了最小二乘法、灰色估计法和GM-SVR的估计效果。研究结果表明:GM-SVR对威布尔分布参数的估计精度明显优于最小二乘法和灰色估计法，可以有效地应用于化工设备失效数据为小样本时的可靠性预测。     

区域环境--经济协调发展定量分析方法研究

本文对目前常用的环境与经济协调发展分析方法的优劣和特点进行了具体分析，指出现在常用的进行可持续发展水平定量分析的层次分析法存在明显的缺陷，以及现有的几种评价环境与经济之间协调程度的方法的局限性。提出了较为客观实用的评估方法，即依据系统分析和数理统计的原理利用主成分分析进行环境——经济协调度分析，并对环境——经济协调度定量评估的步骤、方法包括测算方法的选择、数学模型的建构进行深入的探讨和研究，以南阳市为例对该方法进行了验证。     

基于二值逻辑的复合绝缘子状态判别策略研究

复合绝缘子是架空输电线路上广泛使用的设备,它的老化会给电力系统的正常运行带来威胁。电力相关工作人员需要一种能够对复合绝缘子的运行状态进行判别的方法,帮助他们确定复合绝缘子是否满足运行要求,而对复合绝缘子的定性表征就成为一种优选的表征策略。将运行后的复合绝缘子分成"满足运行要求"和"不满足运行要求",使用二值逻辑回归模型,选取合适的特征量,建立了复合绝缘子状态表征体系。     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

我国区域空气污染治理效率及其影响机制——基于超效率SBM模型和门槛回归模型

随着人民对美好生活需求的日益增长,政府空气污染治理的效率问题日益得到重视。本文从地方政府空气污染治理效率及其影响因素的视角入手,以全国30个省份为研究对象,运用超效率SBM模型测算2003—2015年各省级区域的空气污染治理效率。在此基础上,运用门槛回归模型分析政府空气污染治理效率的影响机制。研究表明:①地区间空气污染治理效率差异性较为显著。中部、西部空气污染治理效率整体呈下降趋势。东部和东北地区空气污染治理效率则上升比较平稳。②政策规划、碳源、污染物排放和环境治理投入表现出双重门槛特征,其中碳源对空气污染治理效率影响显著,而以碳为首的能源消费结构则主要对中部和西部空气污染治理效率产生影响。落后地区政府应注重产业升级,财政上应对碳税等税目进行征收,产业上应鼓励企业创新,并对落后企业进行倒逼升级。     

三江平原湿地植物的土壤环境因子分析

以三江平原毛果苔草(Carex lasiocarpa)湿地和小叶章(Deyeuxia angustifolia)湿地为研究对象,选取土壤全氮(TN)、全磷(TP)、有效氮、有效磷、有机质、pH值和土壤含水量为主要环境控制因子,通过主成分分析(PCA)和逐步多元回归分析,来确定环境因子对物种相对密度和地上生物量的贡献值.结果表明,2个样地中土壤全氮、全磷、有效氮和有效磷的贡献率能达50%左右.毛果苔草湿地中主成分1能够解释88%的物种相对密度和84%的地上生物量;小叶章湿地中主成分1对相对密度和地上生物量的解释量则分别为75%和58%.湿地营养状况决定着植物的生产量;土壤含水量的变化影响着植物对营养物质的吸收和利用.     

利用多元线性回归方法评估气象条件和控制措施对APEC期间北京空气质量的影响

气象条件对大气污染物的扩散和传输有重要影响,准确分离和定量气象因素对空气质量的影响是评估大气污染控制政策有效性的前提.本研究利用APEC会议期间及前后(2014-10-15～2014-11-30)北京城区朝阳观测站点SO_2、NO、NO_2、NO_x、CO、PM_(2.5)、PM_1和PM_(10)以及气象因素的观测数据,采用多元线性回归分析方法,定量评估了气象条件和空气污染控制措施对APEC期间北京空气质量的影响.在假定排放条件不变的情况下,基于气象因素参数建立的预测污染物浓度的多元线性回归模型模拟效果较为理想,决定系数R~2在0. 494～0. 783之间.控制措施使得APEC控制期SO_2、NO、NO_2、NO_x、CO、PM_(2.5)、PM_1和PM_(10)浓度分别降低48. 3%、53. 5%、18. 7%、40. 6%、3. 6%、34. 8%、28. 8%和40. 6%,气象因素使得APEC控制期SO_2、NO、NO_2、NO_x、CO、PM_(2.5)、PM_1和PM_(10)浓度分别降低1. 7%、-2. 8%、18. 7%、4. 5%、18. 6%、27. 5%、30. 6%和35. 6%.气象因素和控制措施共同作用使得APEC控制期北京空气质量得到了明显改善.控制措施对SO_2和氮氧化物浓度的下降起主导作用,气象因素对CO浓度的下降起主导作用,气象因素和控制措施对颗粒物浓度降低的贡献相当.本研究还利用相对权重方法研究了气象因素对污染物浓度影响的贡献,结果表明影响不同污染物浓度的决定性气象因素不同.     

基于SVR的含缺陷管道剩余强度研究

针对采用标准预测含缺陷管道剩余强度误差较大这一问题,在Matlab中建立基于SVR的含缺陷管道剩余强度预测模型,并基于60组含缺陷管道爆破试验数据进行训练测试,以验证模型的实际性能.结果表明:SVR模型预测测试集结果的最小相对误差为0.55％,最大相对误差为10.35％,平均相对误差为2.63％,预测结果的R2高达0....     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

基于视频分析的空管员违规行为识别方法*

为利用视频数据对空管员违规行为进行智能化分析,降低不安全事件发生率,提出2阶段的违规行为识别模型（AR-ResNeXt）,基于实地调研构建空管员视频数据集,利用最小化动态多实例学习损失函数和中心损失函数,获得违规行为检测的判别特征表示,结合异常回归网络和ResNeXt网络,完成对空管员违规行为的时序区间检测与动作分类。研究结果表明:AR-ResNeXt模型在自制数据集中,其帧级AUC达到82.9%,分类准确率达到87.8%,可准确识别空管员发生违规行为的时序区间并进行分类,研究结果可为保障空中交通安全奠定基础。