东湾背斜构造特征与勘探目标选择

东湾背斜位于准噶尔盆地南缘,采用多种地球物理及地质方法,通过多次论证,证明构造是落实的,且其烃源岩、储集层以及盖层条件都较好,但2003年完钻的东湾1井钻探结果却无任何油气显示。在深入了解准噶尔盆地南缘区域构造背景的基础上,探讨了东湾背斜的构造特征、构造演化过程及油气成藏条件,总结出东湾背斜的油气聚集规律,指出在东湾地区可能存在的两种油气藏模式,最后提出下步有利勘探目标区。     

基于数据挖掘探析针刺治疗支气管哮喘的选穴规律

目的：通过数据挖掘分析针刺治疗支气管哮喘的选穴规律。方法：检索中国知网(CNKI)、维普中文科技期刊数据库(VIP)、万方数据(WANFANG DATA)、中国生物医学文献服务系统(SinoMed)自建库至2022年9月20日收录的针刺治疗支气管哮喘的临床研究文献，通过中医传承辅助平台系统V2.5进行频次、关联规则、聚类、核心处方分析。结果：共纳入42篇文献，包含52个腧穴，总使用频次为272次，使用频次排前10位的腧穴依次为：肺俞、定喘、足三里、丰隆、膻中、尺泽、列缺、天突、风门、大椎;常用腧穴主要分布于足太阳膀胱经、手太阴肺经、任脉。特定穴多选用背俞穴、交会穴及五输穴。腧穴配伍以肺俞-定喘最为常见。核心腧穴处方：肺俞、定喘、足三里、丰隆、膻中、风门。结论：特定穴的选用是针刺治疗哮喘组方的重要组成部分，临床应重视“治脏者，治其俞”之选穴思想，突出“局部配穴”“远近配穴”“前后配穴”之配穴特点，从而体现“扶正祛邪，标本兼顾”之治疗原则。     

基于特征选取及广义傅立叶分形的SAR图像海洋溢油检测算法

针对海上溢油SAR图像中油膜与类油膜的识别问题,提出了一种结合傅立叶分形与特征提取的检测算法。由于分形特征可以具有无穷多的细节,并在不同的研究尺度存在自仿射特性。这与油膜及类油膜表面的几何形貌特征非常吻合。该算法通过计算样本的傅立叶分形特征,组成油膜与类油膜的特征空间。然后,应用基于差分进化的特征选取方法将利于分类的重要特征值筛选出来。再利用重要特征值对原有样本进行分类。实验表明,经特征选取的分形特征向量能够以100%的准确率将两类样本区分开。该算法在选取重要特征的同时实现了对高维特征空间降维的目的,该思想可以应用于其他的基于高维特征的识别系统中,具有普遍的适用性。     

多模式智能路灯控制系统

使用AT89S52单片机作为主控芯片,设计了一种多模式智能路灯控制系统,该系统能够根据道路、巷口或定时应用3种情况进行工作模式选择,并根据环境光亮度决定是否启动所设定的工作模式.巷口工作模式时,系统通过红外传感器检测道路上有汽车或行人通过时,将识别信号送入单片机,由单片机控制交通灯开启;道路工作模式时,路灯处于开启状态,以保障行人畅行;定时模式时,通过DS1302定时模块,可以设定路灯的开关时间.此外,该系统还具有自动故障检测功能,功能全面,有较强的实用价值.     

某深埋特长隧洞线路比选方案研究

滇中引水工程香炉山隧洞为特长深埋隧洞,起点位于冲江河右岸石鼓镇,终点位于洱海边的长育村,此前已对该段进行东、中、西等多方向的较大范围线路比选[1]。通过已有地质资料进行线路对比,提出了8条比选线路,从各线路长度和形态,存在的主要工程地质问题,隧洞地质、施工条件等进行比较、分析、研究,最终选定中4线为推荐方案。论文简要介绍了隧洞线路选线方案、比选的思路和方法,以期为相关工程的规划设计提供参考。     

The FAST/SKA site selection in Guizhou province

Many karst depressions with diameters of 300 m to 500 m, suitable for constructing Arecibo-style radio telescopes, were identified in the south of Guizhou Province by Remote Sensing (RS) and Geographic Information System (GIS) technologies together with field investigations. Fundamental topography and landform databases were established for 391candidate depressions, and using GIS the3-dimensional images of depressions, at a scale of 1:10000, were then simulated to fit a spherical antenna. This revised version was published online in July 2006 with corrections to the Cover Date.     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Infrastructure owners and operators, or governmental agencies, need rapid screening tools to prioritize detailed risk assessment and retrofit resources allocation. This paper provides one such tool, for use by highway administrations, based on Bayesian belief network (BBN) and aimed at replacing so‐called generic or typological seismic fragility functions for reinforced concrete girder bridges. Resources for detailed assessments should be allocated to bridges with highest consequence of damage, for which site hazard, bridge fragility, and traffic data are needed. The proposed BBN based model is used to quantify seismic fragility of bridges based on data that can be obtained by visual inspection and engineering drawings. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking and prioritizing. While the actual data and seismic hazard employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment can easily be adopted for bridges in different geographical locations. Copyright © 2015 John Wiley & Sons, Ltd.     

应用选权拟合法建立单历元伪距和载波相位双差模型

本文根据选权拟合法的一个重要性质:只要被约束的那部分参数估值无偏,其余的参数估值也无偏;该性质说明利用选权拟合进行参数估计的结果是条件无偏的。这个不同于一般正则化解的重要特性可以建立单历元伪距和载波相位双差模型,并通过实例验证了该方法的可行性。     

MULTIVARIATE SELECTION OF VARIABLES IN INDUSTRIAL QUALITY CONTROL:OPTIMIZING AVIATION FUEL FINAL CONTROL

This paper deals with a typical question encountered in all industrial analytical laboratories:are all thetests performed in the laboratory to characterize the final product really necessary?In this work the cross-validation technique,Procrustes rotation techniques and statistical variable selection have been used toreduce the 26 initial British Petroleum and ASTM kerosene specification tests to ten'essential'ones.Statistical as well as chemical considerations were used to select the optimum subset of original variablesto be retained from all the possible ones.