傅立叶变换红外光谱联用技术在化石燃料分析中的应用

对傅立叶变换红外光谱联用技术在化石燃料分析中的应用进行了较全面的总结评述,重点介绍了气相色谱／红外光谱联机、高压液相色谱／红外光谱联机、薄层色谱／红外光谱联机、超临界流体色谱／红外光谱联机、裂解色谱／红外光谱联机、热重分析／红外光谱联机、气相色谱／红外光谱／质谱三机联用技术等在化石燃料的结构组分分析、成因机理研究及其勘探等诸方面的重要应用,尤其是在芳香族化合物和几何异构体的分离鉴定方面发挥出的突出功效。引用文献５９篇。     

流域综合管理方法与技术

流域作为一个具有明确边界的地理单元,流域以水为纽带,将上、中、下游组成一个普遍具有因果联系的复合生态系统,是实现资源和环境管理的最佳单元.流域综合管理已经被认为是实现资源利用和环境保护相协调的最佳途径.然而,由于流域综合管理涉及多学科,多部门,内容非常广泛.近年的流域管理还主要停留在一个目标.本文在流域模拟管理研究及实践的相关经验基础上,系统地讨论了流域综合管理实施方法、关键技术以及主要技术问题.     

BHαBAR: big Hα kinematical sample of barred spiral galaxies – I. Fabry–Perot observations of 21 galaxies

We present the Hα gas kinematics of 21 representative barred spiral galaxies belonging to the BHαBAR sample. The galaxies were observed with FaNTOmM, a Fabry–Perot integral-field spectrometer, on three different telescopes. The three-dimensional data cubes were processed through a robust pipeline with the aim of providing the most homogeneous and accurate data set possible useful for further analysis. The data cubes were spatially binned to a constant signal-to-noise ratio, typically around 7. Maps of the monochromatic Hα emission line and of the velocity field were generated and the kinematical parameters were derived for the whole sample using tilted-ring models. The photometrical and kinematical parameters (position angle of the major axis, inclination, systemic velocity and kinematical centre) are in relative good agreement, except perhaps for the later-type spirals.     

基于EfficientNet的星系形态分类研究

星系的结构和形态能够反映星系自身的物理性质,其形态的分类是后续分析研究的一个重要环节.EfficientNet模型使用复合系数对深度网络模型的深度、宽度、输入图像分辨率进行更加结构化的统一缩放,是一种新的深度网络优化扩展方法.将该模型应用于星系数据形态的分类研究中,结果表明基于EfficientNetB5模型的平均准确率、精确率、召回率以及F1分数(精确率与召回率的调和平均数)都在96.6%以上,与残差网络(Residual network, ResNet)中ResNet-26模型的分类结果相比有较大的提升.实验结果证明EfficientNet的深度网络优化扩展方法可行且有效,可应用于星系的形态分类.     

大庆油田勘探50年：陆相生油理论的伟大实践

文章系统分析了陆相生油理论和大庆油田的勘探实践,总结了大庆油田的勘探历程并划分为三大阶段：构造油藏勘探阶段,岩性油藏勘探的探索阶段,岩性油藏勘探大发展阶段。详细阐述了“十五”以来,大庆油田依靠创新理论与技术,丰富完善了大型陆相坳陷盆地岩性油藏勘探理论与配套技术,深层火山岩气藏勘探理论与配套技术,复杂断陷盆地油气勘探理论与配套技术,从而带来了松辽盆地北部中一浅层、深层和海拉尔盆地油气勘探的大发展,大庆油田再次进入油气储量增长高峰期。在详细分析大庆探区的勘探实际和资源潜力的基础上,提出了“百年油田”勘探的工作思路,通过扎实推进科学勘探,努力为油田可持续发展提供有力资源保障的勘探战略。     

基于Stacking集成学习的恒星/星系分类研究

机器学习在当今诸多领域已经取得了巨大的成功,但是机器学习的预测效果往往依赖于具体问题.集成学习通过综合多个基分类器来预测结果,因此,其适应各种场景的能力较强,分类准确率较高.基于斯隆数字巡天(Sloan Digital Sky Survey,SDSS)计划恒星/星系中最暗源星等集分类正确率低的问题,提出一种基于Stacking集成学习的恒星/星系分类算法.从SDSS-DR7(SDSS Data Release 7)中获取完整的测光数据集,并根据星等值划分为亮源星等集、暗源星等集和最暗源星等集.仅针对分类较为复杂且困难的最暗源星等集展开分类研究.首先,对最暗源星等集使用10折嵌套交叉验证,然后使用支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、XGBoost(eXtreme Gradient Boosting)等算法建立基分类器模型;使用梯度提升树(Gradient Boosting Decision Tree,GBDT)作为元分类器模型.最后,使用基于星系的分类正确率等指标,与功能树(Function Tree,FT)、SVM、RF、GBDT、XGBoost、堆叠降噪自编码(Stacked Denoising AutoEncoders,SDAE)、深度置信网络(Deep Belief Network,DBN)、深度感知决策树(Deep Perception Decision Tree,DPDT)等模型进行分类结果对比分析.实验结果表明,Stacking集成学习模型在最暗源星等集分类中要比FT算法的星系分类正确率提高了将近10%.同其他传统的机器学习算法、较强的提升算法、深度学习算法相比,Stacking集成学习模型也有较大的提升.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Cosmic‐ray rejection for single spectroscopic CCD images by means of template matching

This article describes a novel algorithm for cosmic‐ray rejection in single spectroscopic CCD images. This algorithm is based on a variation of template matching. It focuses on identifying those pixels belong to spectra, while other conventional algorithms tried to locate the cosmic‐ray hits directly. The main principle is applying template matching to find suspicious blocks, which is followed by surface patching to locate the legitimate pixels accurately. Therefore, the rest pixels are the ones corrupted by cosmic‐ray hits. Meanwhile, almost all the parameters are automatically extracted from the images. Examples of its performance are given for both simulated and observed images. It shows an advantage of significantly low false alarm rate with relatively high detection rate (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)