生态足迹动态预测模型构建与分析——以武汉市为例

生态足迹的动态预测能够为区域可持续发展政策的制定提供科学的理论依据,但其研究仍处于探索阶段.以武汉市为例,选取表征社会经济发展的重要指标建立了社会经济系统指标体系,在对武汉市1978～2004年生态足迹时间序列计量的基础上,应用偏最小二乘算法构建了生态足迹动态预测模型,并根据VIP值的大小分析了各指标对生态足迹的影响机制.结果表明,武汉市"十一五"规划期间生态足迹呈上升趋势,其中规划期末2010年的人均生态足迹为2.810 5 hm2,高于全球生态阈值(人均生态足迹为2.200 0 hm2);根据VIP值得出人口、能源消费量和第三产业所占比重为最重要的影响因子,并就如何实现武汉市"十一五"规划目标提出了政策性建议.将科技进步贡献率、能源消费量等重要因子纳入社会经济系统指标体系,弥补了在生态足迹定量测度中忽略社会、经济、科技因素对其影响的不足;另一方面,引入偏最小二乘算法中的VIP值更准确地评价了各指标对生态足迹变化的影响,为生态足迹动态预测研究作了进一步地完善与改进.     

湿法脱硫入口SO_2浓度对脱硫效率影响的非线性回归分析

通过对太原二电厂11号FGD现场168运行数据采用一元非线性回归分析和基于距离判别法的多元非线性回归分析两种不同的分析方法,利用最小二乘曲线拟合法建立了不同出口SO2浓度范围内的入口SO2浓度与脱硫效率的非线性回归方程,为实际运行过程中脱硫效率的预测和分析提供理论依据。此外,分析了入口SO2浓度对脱硫效率的影响,为运行人员在实际运行过程中提高脱硫效率而提供控制思路。     

正方形沉淀池内部水流运动模拟及设计应用

针对江苏新纪元环保有限公司自主研发的上下全截面正方形沉淀澄清池进行水流运动模拟及分析。正方形沉淀澄清池的水力特性对其沉淀效果有很大影响,在沉淀池水力设计中应通过对池型以及各种几何参数的优化,使其池内回流区范围尽可能减小,并且尽量使池内垂直断面上流速均匀平稳,研究中主要利用模型模拟正方形沉淀澄清池在不同进水流速和不同挡板布置形式下的水流流场,经过分析比较出较为合理的沉淀池运行工况以及较为合适的挡板布置形式,进而提高沉淀池的运行效率。     

安全文化建设原理研究

为夯实安全文化建设的理论基础,进一步丰富安全文化学原理,以组织安全文化建设为着眼点,提炼并分析组织安全文化建设的2条核心原理,即组织安全文化方格理论和杠杆原理,并构建其“轮形”结构体系。结果表明:2条组织安全文化建设原理之间彼此影响、相互促进,其中,方格理论系统阐明了组织安全文化建设方案的设计方法和要求,杠杆原理指明了减弱组织安全文化建设阻力的阻碍作用的具体思路和途径,它们可显著提升组织安全文化建设的效率和效果。     

钢筋混凝土板弯曲问题的各向同性化最小平方误差法

利用合适的坐标变换将钢筋混凝土板的弯曲问题变换为各向同性板弯曲问题。由弹性薄板的虚功原理及最小平方误差法计算各向同性板弯曲变形,得到原正交各向异性薄板的挠度方程。算例表明,在近似函数选取合理的情况下,该方法简单,计算时间短,具有较高的精确度。     

酸雨pH预测的偏最小二乘回归模型

酸雨pH值受酸性离子[SO4^2-]、[NO3^-]和碱性离子[Ca^2 ]、[NH4^ ]等的影响。这些影响因素之间存在多重相关性。用一般最小二乘回归法建模预测pH值，估计参数存在着很大的误差且物理意义明显不足。应用偏最小二乘回归技术建立pH值预测模型，克服了自变量之间的多重相关性的问题，因而更具有先进性，计算结果更为可靠。以我国17个城市pH值预测为例，探讨偏最小二乘法的优势，并与最小二乘回归法进行了比较。     

某机载雷达多自由度振动试验控制方法比较

根据机载雷达实测振动数据,在三轴振动台上,采用不同振动控制方法,对机载雷达单元进行了多自由度振动试验。比较了雷达单元在不同振动控制方法下的安装点响应与参考谱的偏差。试验结果表明:相比于方阵控制,长方阵控制中的坐标变换控制(Input/Output控制)方法在进行多自由度振动试验时,可实现真正意义上的平均控制,振动的传递更为均匀。     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

Network inclusion probabilities and Horvitz-Thompson estimation for adaptive simple Latin square sampling

Consider a survey of a plant or animal species in which abundance or presence/absence will be recorded. Further assume that the presence of the plant or animal is rare and tends to cluster. A sampling design will be implemented to determine which units to sample within the study region. Adaptive cluster sampling designs Thompson (1990) are sampling designs that are implemented by first selecting a sample of units according to some conventional probability sampling design. Then, whenever a specified criterion is satisfied upon measuring the variable of interest, additional units are adaptively sampled in neighborhoods of those units satisfying the criterion. The success of these adaptive designs depends on the probabilities of finding the rare clustered events, called networks. This research uses combinatorial generating functions to calculate network inclusion probabilities associated with a simple Latin square sample. It will be shown that, in general, adaptive simple Latin square sampling when compared to adaptive simple random sampling will (i) yield higher network inclusion probabilities and (ii) provide Horvitz-Thompson estimators with smaller variability.