环境温度对某固体推进剂贮存寿命影响研究

为了分析环境温度对固体推进剂贮存寿命的影响程度,根据某固体推进剂加速老化试验结果,采用传统阿伦尼乌斯法、线性活化能法、整体预测法和两步回归法,预测了该推进剂在20℃和25℃下的贮存寿命。通过对比分析,认为不同寿命预估方法的预测结果不尽相同,但推进剂贮存环境温度的小范围改变对贮存寿命的影响是显著的。     

基于SSA-XGBoost方法的降水变化趋势预测模型

全球变暖预期下不同区域的未来降水变化,是政府和公众都关心的重要问题,也是全球变化研究的前沿科学问题。预测模型的建立是预测/预估研究的重点和难点,现今不稳定的气候背景对预测模型的精准度提出了更高的要求。为了解决传统方法对长期时间序列预测效果欠佳的问题,本文以泰国南部洞穴石笋δ¹⁸O重建的过去270多年（公元1773—2004年）的降水记录为数据集,提出了SSA-XGBoost预测模型。对原始数据去趋势预处理后,采用奇异谱分析法（SSA）提取前部分数据（1773—1964年）的振荡成分以确定数据的最佳谐波个数,并进行准确的周期信号分量分解;之后用去趋势数据减去周期信号得到随机信号,再利用XGBoost模型对随机项进行预测;最后将预测的序列、趋势曲线和周期信号延拓结果相叠加得到最终的预测数据（1965—2004年）。与其他四种模型（XGBoost、ARIMA、SSA-ARIMA、LightGBM）的预测结果相比,SSA-XGBoost的预测结果与真实值最相近,且MAE和RMSE均最小,R²也更接近1,说明该模型具有更高的精度和稳定性。该研究对于泰国南部等热带地区未来的降水变化趋势预测具有较好的指导意义,也可为其他长时间序列的预估研究提供借鉴。     

粗糙集理论在震害预测中的应用

知识约简是粗糙集理论的核心问题。首次尝试将这一概念用于建筑物震害预测工作中，结合震害预测工作的实际，提出了一种基于属性重要性的近似相对约简算法，并通过实例验证了其可行性。所述研究具有理论和现实意义，对今后进行有关抗震防灾工作具有很好的应用价值，并为开展震害预测工作提供了新的研究思路和手段。     

公路交通噪声预测的神经网络模型

利用多条国道、省道公路交通噪声的实测数据,建立了一个公路交通噪声预测的神经网络模型,能够同时预测公路交通噪声的等效连续声级、统计声级和标准偏差,预测值和实测值基本相符,该模型为公路交通噪声的预测、治理和控制提供了良好的条件。     

基于HRS和GIS的城市污染场地动态监测原理分析

选择有代表性的城市场地,运用网格法土壤采样,并通过实验室化学分析得出土壤的重金属等污染物质含量,运用地物光谱仪在实验室设定条件下测定土壤反射光谱,应用偏最小二乘法（PLS）建立高光谱遥感（HRS）影像定量反演模型,预测场地土壤中各种污染物的含量,并在遥感影像中直观表示。结果表明,HRS技术能提供大量、连续性的光谱信息;PLS模型为大尺度城市场地污染快速评价与监测提供了可能性;并通过结合G IS技术可实现城市污染场地的动态监测,为城市公共管理提供决策支持。     

关于“十四五”生态环境监测支撑能力提升的思路与目标

生态环境监测是推进国家生态环境治理体系和治理能力现代化的重要支撑。党中央、国务院高度重视监测事业发展，将生态环境监测纳入生态文明改革的大局统筹推进。"十四五"时期，生态文明改革持续深入，生态环境治理将向精准治污、科学治污、依法治污转变，生态环境监测面临着全新的压力与挑战。因此，笔者以习近平生态文明思想为指导，按照"面向发展、服务公众、智慧监测、精准支撑"的原则，针对大气、地表水、土壤、地下水、生态等重点领域，提出了"十四五"时期生态环境监测的重点目标任务，为生态文明和美丽中国建设提供技术支撑。     

Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM_2.5) and coarse particle (PM₁₀) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM_2.5 concentration is 13%–144% and 58%–241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM₁₀ results also exceeded the AQI (54%–65%) and WHO (8%–395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM_2.5 and PM₁₀ exhaust emission correlation coefficient were predicted to be (R² = 0.775) and (R² = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM_2.5 (R² = 0.943) and PM₁₀ (R² = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.     

Highway-rail grade crossings accident prediction using Zero Inflated Negative Binomial and Empirical Bayes method

Introduction: Recently the Federal Railroad Administration (FRA) released a new model for accident prediction at railroad grade crossings using a Zero Inflated Negative Binomial (ZINB) model with Empirical Bayes (EB) adjustments for accident history (2). This new model is adopted from the work that was conducted by the authors (3–6). The unique feature of the new FRA model is that it has a single equation for all three warning devices (crossbuck, flashing light, and gates) and uses the same variables regardless of the warning devices at the crossing. Since the New FRA model incorporates the warning device category as one of the variables in its model equation, the predicted accident frequency is higher when a crossing has crossbucks than flashing lights, and higher when it has flashing lights than gates. While this model is significantly better than the old USDOT model (7), its shortcoming is that the single equation does not accurately represent the field condition. Method: This paper presents the ZINEBS model (Zero Inflated Negative binomial with Empirical Bayes adjustment System). The ZINEBS model gives three different equations depending on the type of warning device used at the crossings (gates, flashing lights, and crossbucks). The three equations use variables, some of which are common across all warning devices, while other variables are specific to a warning device. The predicted values for the ZINEBS model show a closer agreement with the field data than the new FRA model. This observation was true for all three warning device types analyzed. Practical Applications: Based on the results of this study, the ZINEBS compliments the new FRA model and should be used when the single equation is not adequately representing the role of traffic control device types and relevant variables associated with that device type.     

基于改进多目标粒子群算法的航空器改航研究

针对影响航行安全的动态危险天气,提出了一种基于改进多目标粒子群算法的改航路径规划方法。首先,获取实时动态气象数据,利用栅格法对改航环境进行建模并采集危险天气区域初始边界点的历史气象数据。然后采用灰色预测模型对上述初始边界点坐标进行位置预测,进而建立改进后的实时动态环境模型。最后利用改进环境模型的多目标粒子群算法对改航路径进行动态规划。在考虑改航路径角度和距离等约束条件的基础上,确定了改航路径危险系数和距离最优的双目标函数。对中东部沿海某次短时危险天气下的航空器改航进行仿真分析,仿真结果表明改进后算法具有一定的有效性和可行性。     

基于Winkler模型的沿空留巷底板变形力学分析

沿空留巷顶板岩层垮落到底板时,会影响底板的受力和变形,进而导致底臌,底臌的产生将直接影响采煤工作开展。为了对巷道底板的变形机理进行研究,根据空留巷底板受力特点,将巷道底板视为弹性地基梁,建立顶板岩层垮落在巷道底板上的不均匀荷载作用下的Winkler弹性地基梁力学模型,通过对高阶微分方程求解,分析巷道底板任一处的挠度、转角、弯矩及剪力的分布规律,并结合内力分布规律对底板破坏机理进行分析。结合工程实践,提出了防止底板变形的措施,为类似工程的设计及施工提供参考。