基于深度学习的工程结构损伤识别研究进展

为避免或减轻工程结构在建造和运营期间因结构振动产生不同程度损伤,造成安全隐患危及人们生命财产安全,针对结构振动损伤识别技术展开研究,探讨不同深度学习方法发展情况及其利弊,寻找更具可行性的损伤识别方法,并对其最新研究及应用现状进行全面综述。研究结果表明:应用深度学习开发新的结构损伤识别技术,无需冗余的数据预处理以及手工提取损伤特征,实现以较高精度实现损伤识别任务;一维卷积神经网络（1D-CNN）以其独特的应用优势,在数据样本有限条件下较二维卷积神经网络（2D-CNN）表现更为出色。研究结果可为数据驱动的结构损伤识别问题提供新思路,进一步完善土木结构健康监测研究体系。     

基于一维卷积神经网络的燃气管道泄漏声发射信号识别*

为保障燃气管道系统安全运行,及时诊断管道故障,基于VGG-16模型提出基于一维卷积神经网络的燃气管道故障诊断模型,提取原始声发射信号特征参数,有效诊断燃气管道故障。结果表明:基于一维卷积神经网络的燃气管道故障诊断模型,能够有效解决燃气管道故障诊断过程中数据预处理复杂、特征提取困难以及识别准确率低等问题,可为燃气管道故障诊断提供技术支撑。     

面向通风智能化的风速传感器结构化数据降噪方法对比*

为实现风网实时解算、系统优化调节等关键技术,需要去除监测数据噪声,得到结构清晰、纯度较高的通风数据。利用FCM,Rloess和S-G等平滑降噪算法对300组实测风速数据进行分析处理。结果表明:FCM算法处理过程变量引起噪声较为优越,但需提前给定分类数目;窗宽参数选7时,Rloess算法去除由状态变量引起的风速异常数据最优;在窗宽选5、次数为2时,S-G算法降噪和保持数据特性最佳;结合使用FCM-Rloess或FCM-SG算法可有效处理过程变量和状态变量引起的风速异常数据。研究结果可为矿井通风的异常诊断、灾变识别等研究提供合理的基础数据。     

基于人工神经网络的烟气及温度实时预测模型*

为监测建筑火灾事故区域的危险程度,实现更加安全、高效的火灾应急救援,以通廊式建筑为研究对象,基于转置卷积神经网络及数值模拟方法开发1种可实时预测走廊位置处烟气扩散和温度分布的神经网络模型。首先,依托Python建立包含全连接、转置卷积、反池化等在内的19层神经网络模型的整体架构;其次,建立包含99个火灾场景,共7 920组图像数据的火场信息数据库用于模型训练;最后,使用测试集对模型进行可靠性验证。研究结果表明:烟气（温度）预测模型在不同火灾场景下的预测精度达到95%,训练完成后模型的预测时间一般为1~2 s。研究结果可为应急策略的快速制定提供数据参考。     

Identification of the largest individual sample value using composite sample data and certain modifications of the sweep-out method

Cost-effective hotspot identification is an important issue in hazardous waste site characterization and evaluation. Composite sampling techniques are known to be cost effective when the cost of measurement is substantially higher than the cost of sampling. Although compositing incurs no loss of information on the means, information on individual sample values is lost due to compositing. In particular, if the interest is in identifying the largest individual sample value, the composite sampling techniques are not able to do so. Under certain assumptions, it may be possible to satisfactorily predict individual sample values using the composite sample data, but it is not generally possible to identify the largest individual sample value. In this paper, we propose two methods of identifying the largest individual sample value with some additional measurement effort. Both methods are modifications of the simple sweep-out method proposed earlier. Since analytical results do not seem to be feasible, performance of the proposed methods is assessed via simulation. The simulation results show that both the proposed methods, namely the locally sequential sweep-out and the globally sequential sweep-out, are better than the simple sweep-out method.Prepared with partial support from the Statistical Analysis and Computing Branch, Environmental Statistics and Information Division, Office of Policy, Planning, and Evaluation, United States Environmental Protection Agency, Washington, DC under a Cooperative Agreement Number CR-821531. The contents have not been subjected to Agency review and therefore do not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Absorption cross-sections of atmospheric constituents: NO2, O2, and H2O

Absorption spectroscopy, which is widely used for concentration measurements of tropospheric and stratospheric compounds, requires precise values of the absorption cross-sections of the measured species. NO₂, O₂ and its collision-induced absorption spectrum, and H₂O absorption cross-sections have been measured at temperature and pressure conditions prevailing in the Earth’s atmosphere. Corrections to the generally accepted analysis procedures used to resolve the convolution problem are also proposed.     

核事故沾染的放射性区域所产生的地表活度分布与空中辐射场之间的换算规律--反卷积定理在核事故辐射场航测中的应用

本文应用卷积定理和反卷积定理解决了长期以来存在的任意面状放射源产生的辐射场求解问题.在核事故产生的放射性沾染区,根据本文介绍的方法,利用航空测量的某一高度平面上的照射量分布值,应用反卷积定理可求得地面上对应的放射性活度的分布,利用卷积定理可求得该活度分布所对应的任一高度平面上的照射量分布值.因该方法采用了FFT算法,故计算速度很快,可用于实时测量处理.