弹用电连接器湿热环境下寿命预测

目的预测某型电连接器的剩余寿命。方法以某弹用电连接器为研究对象,在分析导弹服役环境和电连接器失效机理的基础上,基于电连接器接触电阻性能退化特点,利用Wiener过程拟合电连接性能退化轨迹。然后,以Wiener过程的漂移参数为中间变量,采用广义Eyring模型,对温度、湿度应力进行建模,进而根据试验数据对性能退化数据似然函数的参数进行估计。最后,结合加速模型,推导得到正常应力水平下的漂移参数值,从而确定出电连接器的可靠度模型。结果计算出样品电连接器的平均寿命,可进一步推断该型电连接器的其他寿命信息。结论Wiener过程可以拟合湿热环境下电连接器的性能退化轨迹,温湿应力是开展弹上设备试验需重点关注的因素,考虑进一步提高类似环境载荷下的加速退化试验效率,可在一定程度上增大温度应力水平。     

基于航班运行规律的典型机场NOx浓度临近预测方法研究

针对天津机场区域考虑航班运行影响,应用广义加性模型（Generalized Additive Model,GAM）建立污染物浓度预测模型,对因子间的共线性问题和交互作用进行改进,得到最优的NO_x浓度预测模型.选取天津机场区域2019年11月—2020年3月环境、气象及航班数据,建立改进的GAM.结果显示：（1）改进的GAM预测效果优,可以更加准确地预测浓度峰值及变化趋势;（2）样本量会影响模型选择的因子数量及模型性能;（3）改进模型adj-R²为0.940,实测和预测NO_x浓度的相关系数为0.975,预测效果好;（4）航班活动对机场区域污染物浓度影响较大.改进的GAM考虑污染物浓度与影响因子之间的复杂非线性关系及影响因子之间交互作用对污染物浓度变化的影响,使模型精度进一步提升. GAM对污染物浓度的准确预测可为机场区域污染防治提供依据.     

我国土壤受试植物筛选与毒性预测

生态毒性数据缺乏是我国土壤基准与生态风险评估研究中一直存在的问题,开展本土受试植物的筛选可提供更多的生态毒理试验选材,从而获得不同物种的生态毒性数据.鉴于植物对土壤污染物的敏感性,从被子植物中依据分布范围、代表性和易于获得性等原则,对我国潜在的受试植物进行筛选,结果发现,13科53种被子植物分布广泛且易于获取,可作为本土受试植物;结合生态毒性数据的搜集与分析,其中12种受试植物的生态毒性数据相对丰富,并对部分典型污染物表现高敏感.此外在受试植物生态毒性预测模型研究方面,对12种受试植物两两进行建模预测,共得到132个物种种间关系估算模型(Interspecies Correlation Estimation,ICE),其中88个为显著性模型(F检验P<0.05);此处,回归分析了已构建ICE模型的评价参数,得出预测效果较好的ICE模型应满足交叉验证成功率≥ 80.00%、MSE ≤ 0.62、R² ≥ 0.76和分类学距离≤ 4的标准.最终筛选出25个符合上述标准的ICE模型,涉及禾本科-禾本科、十字花科-十字花科的相互预测,其中当燕麦Avena sativa、芜青Brassica rapa、普通小麦Triticum aestivum、玉蜀黍Zea mays和黑麦草Lolium perenne等作为替代物种时,预测物种的实际生态毒性值与预测值较为接近.受试植物的筛选与生态毒性预测模型的建立有助于生态毒性数据的产生,并为土壤污染管理和生态风险评估提供科学依据.     

基于粗集理论算法-脊波神经网络的深基坑变形预测与应用

基坑开挖变形具有非线性特性,在脊波神经网络的基础上,采用粗集理论算法优化初始权值和阈值,建立了基于粗集理论算法-脊波神经网络的深基坑变形预测模型,应用该模型对西南地区某市火车站综合交通换乘中心南广场的基坑开挖过程进行了变形预测。结果表明:粗集理论算法能够对脊波神经网络进行优化,提高了脊波神经网络基坑变形预测结果的收敛速度和泛化能力;脊波神经网络能逼近基坑变形的非线性部分,避免了模型误差影响基坑开挖变形预测精度,提高了系统整体抗干扰性能。模型的预测值与实测值之间的误差在5%以内,满足实际工程的要求。     

Prediction of Crop Production using Drought indices at Different Time Scales and Climatic Factors to Manage Drought Risk1

Sadat Noori, S.M., A.M. Liaghat, and K. Ebrahimi, 2011. Prediction of Crop Production Using Drought Indices at Different Time Scales and Climatic Factors to Manage Drought Risk. Journal of the American Water Resources Association (JAWRA) 48(1): 1‐9. DOI: 10.1111/j.1752‐1688.2011.00586.x Abstract: Drought causes great damage to rainfed and irrigated farming. Therefore, prediction of crop production during the drought period is essential in order to manage drought risk. Thus, proceeding to agricultural drought risk management can be very useful. This study shows the results of early crop prediction using the combination of climate factors and drought indices at different time scales. The study region was Hamadan, a semiarid region in Iran. The methodology demonstrated here has allowed the prediction of production several months before harvest. Moreover, the predictive models constructed have explained 89% of the temporal variability of wheat production. This method could be very efficient for managing crop production. Moreover, having clear prediction, decision makers can plan better for overcoming drought impacts to reduce crop uncertainty for farmers in insurance companies.     

基于仿真分析的复杂电子装备测试性预计技术研究

测试性是产品的一种设计特性,是伴随功能原理设计时赋予产品的一种固有属性。随着电子产品及设备的复杂和多样化,导致其故障诊断越来越困难,产品的测试性也逐渐得到生产和使用单位的重视。为尽早在研制阶段对产品的测试性水平进行评估,发现测试性设计的薄弱环节,首先对测试性仿真技术进行了简单介绍,之后详细阐述了基于TEAMS的测试性仿真方法,最后以某型柴油机系统为例,对其测试性指标进行预计,同时发现产品测试性设计上的薄弱环节,为产品的设计改进提供建议。     

A beetle antennae search improved BP neural network model for predicting multi-factor-based gas explosion pressures

A gas explosion in an underground structure may cause serious damage to the human body and ground buildings and may result in huge economic losses. The pressure of the gas explosion is an important parameter in determining its severity and designating an emergency plan. However, existing empirical and computational fluid dynamics (CFD) methods for pressure prediction are either inaccurate or inefficient when considering multiple influencing factors and their interrelationships. Therefore, for a more efficient and reliable prediction, the present study developed a multifactorial prediction model based on a beetle antennae search (BAS) algorithm improved back propagation (BP) neural network. A total of 317 sets of data which considered factors of geometry, gas, obstacle, vent, and ignition were collected from previous studies. The results showed that the established model can predict pressures accurately by low RMSE (43.4542 and 50.7176) and MAPE (3.9666% and 4.9605%) values and high R² (0.7696 and 0.7388) values for training and testing datasets, respectively. Meanwhile, the BAS algorithm was applied to improve both the calculation efficiency and the accuracy of the proposed model by enabling a more intelligent hyperparameter tuning method. Furthermore, the permutation importance of input variables was investigated, and the length (L) and the ratio of length and diameter (L/D) of geometry were found to be the most critical factors that affect the explosion pressure level.     

Prediction of global solar radiation using support vector machines

This article utilizes Support Vector Machines (SVM) for predicting global solar radiation (GSR) for Sharurha, a city in the southwest of Saudi Arabia. The SVM model was trained using measured air temperature and relative humidity. Measured data of 1812 values for the period from 1998–2002 were obtained. The measurement data of 1600 were used for training the SVM, and the remaining 212 were used for comparison between the measured and predicted values of GSR. The GSR values were predicted using the following four combinations of data sets: (i) Daily mean air temperature and day of the year as inputs, and global solar radiation as output; (ii) daily maximum air temperature and day of the year as inputs, and GSR as output; (iii) daily mean air temperature and relative humidity and day of the year as inputs, and GSR as output; and (iv) daily mean air temperature, day of the year, relative humidity, and previous day’s GSR as inputs, and GSR as output. The mean square error was found to be 0.0027, 0.0023, 0.0021, and 7.65 × 10^?4 for case (i), (ii,), (iii), and (iv) respectively, while the corresponding absolute mean percentage errors were 5.64, 5.08, 4.48, and 2.8%. Obtained results show that the SVM method is capable of predicting GSR from measured values of temperature and relative humidity.     

高分辨率遥感图像在城市快速震害预测中的应用研究

以河南省某县城为例,通过分析高分辨率遥感图像,获取建筑物的属性信息,结合相关调查数据,建立震害矩阵,得到震害预测结果,计算综合地震危险性指数,评价该区域综合抗震能力。与实际调研结果对比后发现,基于高分表率遥感图像的建筑物信息提取技术具有较高精度,能够满足大范围快速震害预测的需要。     

基于集对分析的降水酸度及水质相似预测模型研究

应用集对分析法刻画历史样本之间的相似性,用多个最相似的历史样本的加权平均值作为当前样本预测值,建立了基于集对分析的降水酸度及水质相似预测模型(SFM-SPA),并运用该模型进行了降水酸度及水质预测的实例验证。结果表明,在主要影响因子选择适当、历史样本的代表性和相似性较好等限定条件下,利用模型进行环境预测是可行的,它较直观、计算简便,为环境污染预测提供了新的途径。