一种多/高光谱遥感图像端元提取的凸锥分析算法

凸锥分析方法常用于多光谱和高光谱遥感图像的端元提取。遥感图像中的每个像元都可以看作一个多维向量,整幅影像看作由离散的非负向量构成的凸锥,通过寻找凸锥的角点来自动获取图像的端元。本文提出了一种自动选择最佳凸锥角点的方法,应用到传统的凸锥分析方法中,提高了凸锥分析方法的效率。利用模拟数据和真实数据实验验证了算法的可行性。     

地震前兆复杂性成因机理研究的讨论（一）：地震前兆复杂性的表现形式

通过国内外成功与失败的震例分析,介绍了地震前兆共性的一面,如在台网具有一定监测能力的地区,如果发生６级以上地震,震前总能或多或少地观测到异常现象。地震越大震前异常越多,分布越广,持续时间越长,异常幅度也较大,同时章着重对前兆复杂性的表现形式进行了较系统的归纳,整理,主要从以下几方面进行了分析,异常与地震关系的不确定性,主要表现在除了“有异常有地震”和“无异常无地震”的理论情况外,还存在“有异常无     

The regional dynamical model of the atmospheric ozonosphere

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Retrieval single-doppler radar wind with variational assimilation method-part i: objective selection of functional weighting factors

In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward in optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.     

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。     

G-凸空间内的连续选择定理和重合点定理

在G-凸空间内得到了一些新的连续选择定理,并利用连续选择定理证明了非紧G-凸空间内及乘积G-凸空间内的重合点定理.这些定理改进并推广了最近文献中的一些已知结果.     

FPGA在时间测量中的应用

利用现场可编程门阵列(FPGA)的内部资源，在FPGA中实现了高精度时间间隔测量系统的设计。     

城市创伤中心空间选址的层次分析模型

针对城市创伤中心空间选址问题,建立包含四大类、八小类影响因素的层次分析模型,邀请不同领域的专家对构造出的判断矩阵综合打分,得出各因素的相应权重后运用到深圳市创伤中心空间选址的实例研究上。模型中所涉及的空间数据均基于网络分析,更贴合实际情况。同时,考虑到深圳市创伤中心的依托医院相对集中这一特殊情况,通过聚类分析划分区域后再使用层次分析模型选址。优化后的结果将改善深圳市创伤医疗救治资源配置、提高创伤救治效率,为创伤救治机构规划部门提供参考意见。     

结合引力场理论的道路自动选取方法

针对复杂网络模型被广泛应用于道路选取的研究中,少有方法顾及道路网对偶图中多级邻居节点的影响,导致对道路的重要性评价缺乏准确性和可靠性的问题。该文将道路网对偶图节点的结构特征值视作质量,将节点间的最短距离视作距离,结合引力场方程实现道路自动选取,较好地将道路网对偶图中多级邻居节点的影响纳入到道路重要性的计算中,从而实现对道路重要性的准确评估。通过对兰州市城关区的路网进行实验,结果表明本文方法所选取的路网较好地保持了原始道路网的整体结构、覆盖范围、密度分布、拓扑特征和连通性。     

Data selection using support vector regression

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection(thinning)methods to preserve essential information. Satellites, such as Wind Sat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression(SVR), is developed and tested. To manage large on-line satellite data streams, observations from Wind Sat are formed into subsets by Voronoi tessellation and then each is thinned by SVR(TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods(random selection, averaging and Barnes filtering), producing a 10% thinning rate(90% data reduction), low mean absolute errors(MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1and correlations 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.