Improvement of rainwater infiltration and storage capacity by an enhanced seepage well: From laboratory investigation to HYDRUS-2D numerical analysis

Seepage well is an emerging Low Impact Development (LID) technology that can effectively control the storm runoff. However, its rainwater infiltration rate and storage capacity still require further enhancement. By setting a horizontal infiltration structure at the bottom of conventional rainwater seepage well (CSW), an enhanced seepage well (ESW) was proposed in this study, and its infiltration performances compared with the permeable pavement (PP) and the CSW were systemically investigated using static infiltration experiment and HYDRUS-2D simulation. The results showed that the infiltration efficiency of ESW was significantly higher than that of PP and CSW, and the process of water infiltrated through soil mainly controlled the macroscopic infiltration rate. The Nash-Sutcliff Efficient (NSE) index was used to evaluate the accuracy and reliability of the HYDRUS-2D model, and the results of NSE values greater than 0.75 (varied between 0.75 and 0.91) confirmed the applicability of HYDRUS-2D to describe correctly the hydraulic behavior of the ESW system. Simulation infiltration tests showed that the ESW performed a higher average infiltration rate and fewer total runoff volume than the CSW, indicating the effectively enhancement of the infiltration and water retention capacity of ESW, especially under heavy rainfall intensities. Additionally, the ESW system exhibited an excellent runoff-control and rainwater retention capacity in an actual rainfall scenario.     

基于深度神经网络的高位滑坡范围预测

以贵州尖山营滑坡为工程背景,通过对深度学习的总结与分析,建立多层感知器模型以对该滑坡危险区范围进行非线性预测研究。通过对深度神经网络算法的优化,构建64-128-32-1四层多层感知器模型,并以滑坡最大高差、滑坡体积、滑源区坡度、坡脚坡度、地层倾角作为输入量,以滑坡最大水平运动距离作为输出量对该模型进行训练,实现影响因素与运动距离的非线性映射。根据对贵州省尖山营滑坡调查和研究,尖山营滑坡区域面积约648 700 m2,体积约1 200万 m3,属于特大型滑坡。依据最优模型对该滑坡进行滑距预测,滑坡平面直线距离1 769 m区域内为危险区域。     

Phase equilibria of the Al2O3–CaO–SiO2-(0%, 5%, 10%) MgO slag system for non-metallic inclusions control

Al₂O₃–CaO-(MgO–SiO₂) inclusions are one of the dominant inclusions in Al-deoxidized spring steel, the compositions changes of which are closely related to refining slags and deoxidization process. The Al₂O₃–CaO–SiO₂–MgO system can represent the primary ingredients of the Al₂O₃–CaO inclusions. According to analyzed compositions and predicted liquidus temperature ranges of inclusions and refining slag, equilibra experiments under high temperature, water quenching technique and subsquent electron probe X-ray microanalysis (EPMA) has been conducted to ascertain detailed thermodynamic database for inclusions control. Liquidus temperatures within the dominant phase fields of Ca₃SiO₅, Ca₂SiO₄, CaAl₂O₄, Ca₃Al₄O₉, spinel and MgO with the intervals of 20 °C from 1350 to 1560°C were identified. To further promote inclusions control, the influences of mass ratios of Mass(Al2O3)Mass(Al2O3+SiO2+CaO) and MgO contents on equilibrated phases and liquidus temperature changes have been explored. To further enhance modification levels of Al₂O₃–CaO-(MgO–SiO₂) system inclusions, it is suggested that refining time could be suitably prolonged.     

避雷器接地电阻的测量与预测方法

介绍了避雷器三极法测量接地电阻的接线、测量原理及测量电压的选取,根据测得的历史数据建立了基于回归分析的接地电阻变化趋势预测模型,并通过标准离差检验证明了该预测模型可用于实际接地电阻的预测。实践表明,该测量与预测方法切实可行,能在一定程度上避免因接地电阻超标而引发的雷击事故。     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

基于变精度粗糙集的煤与瓦斯突出预测

煤与瓦斯突出是受诸多因素影响的复杂问题,为了提高其预测的准确性,本文以变精度粗糙集理论中的决策表为主要工具,首先将影响煤与瓦斯突出的检测信号作为预测的条件属性集,煤与瓦斯突出量作为对预测的决策属性,建立决策表,然后利用小生境遗传算法适合于进行多峰值函数优化的特点,提出了一种基于小生境遗传算法的粗糙集属性约简方法,用于求解决策表的多个约简,进而进行值约简后抽取出预测规则。算例结果说明了本算法的正确性和可行性。     

太阳能帆板输出功率短期预测研究

在卫星观测中,经常由于各种原因导致短时间内无法提取目标卫星数据,使控制系统无法继续对其保持高精度跟踪和控制。文章主要研究ARIMA模型在卫星遥测数据短期预测中的应用。使用Matlab软件进行ARIMA模型参数计算,再使用SPSS软件执行数据预测,并进行残差分析,通过实例验证,短时期内的预测数据精度较高。     

Two new characteristic parameters for runoff computation

A method capable of estimating the hydrograph from a prescribed storm for a practical mild slope upstream catchment is proposed. This method makes use of two new characteristic parameters, andS, in conjunction with the kinematic wave equation to compute lateral inflows of the main stream of the catchment. The depth profile of overland flow at any instant within the catchment and hydrograph at any location can be easily found. Lag times for individual lateral inflows are then considered and are linearly combined to obtain the hydrograph at the outlet of the catchment or depth profile of the main stream at any instant. The validity of the excess rainfall-surface runoff linear relationship in this study has also been verified with Tatsunokuchiyama catchment, and it shows good results for this computed runoff.     

多型号装备系统维修资源需求预计建模

传统的按装备型号进行保障的模式已不适应未来战争需要。多型号装备联合保障是未来装备维修保障的主要方式。针对多型号装备系统维修资源的需求预计问题,分析了影响维修资源需求的主要因素,按照维修资源的不同特点,提出了一种维修资源分类方法;根据装备保障的实际过程,勾画出了维修资源需求预计的一般流程,并构建了各类维修资源的需求预计模型;最后给出了具体算例,证明已构建维修资源需求预计模型的适用性和可行性。     

一种新的红外图像快速分割方法研究

研究红外图像目标分割快速优化问题。在高分辨率红外图像的分割中,红外图像存在数据量大、目标的边缘模糊和噪声较大等导致分辨率低和实时性差。为了快速准确分割,提出基于低尺度分割阈值预测的快速红外图像分割方法(LFIRS)。首先建立尺度阶数计算模型以确定保留原始红外图像目标基本信息所需的最小尺度,在分析多尺度过程对具有目标/背景强相关特性的红外图像进行分割阈值,建立多尺度红外图像分割阈值的相关模型(CLM),结合经典二维阈值法得到的最低两个尺度的阈值CLM模型参数,可以通过CLM模型与最小尺度的快速获取原始尺度的分割阈值,实现红外图像的快速分割。实验结果表明,改进方法提高了图像分辨和分割速度,且改善了分割效果。