深海海底光缆敷设施工余量控制的原理和控制软件的应用

为解决深海有中继海底光缆项目技术难题,中国海底电缆建设有限公司开发了海底光缆敷设施工控制软件,填补我国相关领域的空白。文章介绍海底光缆敷设施工余量控制的原理和控制软件的操作流程:海底光缆余量包括区域余量、底部余量和释放余量,余量控制是海底光缆敷设施工中最关键的核心技术环节,应用控制软件可极大地降低计算量和提高计算精确度,并可通过在施工中不断调整计划,从而极大地提高施工质量,具有传统人工计算不可比拟的优势。     

泉州湾赤潮藻类优势种细胞密度回归方程研究

于2006年5-11月对东海泉州湾赤潮监控区四个监测站位开展赤潮常规监测。根据监测结果,分别以各站位23项水质理化生物环境因子指标为自变量,相应赤潮藻类优势种的细胞密度为因变量,进行多元逐步回归分析,建立了各站位优势种中肋骨条藻(Skeletonema costatum)、太平洋海链藻(Thalassiosira pacifica)、微小原甲藻(Prorocentrum minimum)、尖刺拟菱形藻(Pseudo-nitzschia pungens)、丹麦细柱藻(Leptocylindrus danicus)和旋链角毛藻(Chaetoceros curvisetus)等的细胞密度多参量回归方程。结果表明,所有回归方程的复相关系数都接近于1,方差分析的结果均为回归极显著,表明所建立的回归方程可作为相应赤潮优势种细胞密度预报方程的高度有效性,将对今后泉州湾的赤潮预报提供良好的指导作用。     

Pipe laying simulation with an active reel drive

Dynamic and quasi-static models for analysis of a pipe lay spread are presented in the paper. Depending on the type of a pipe, spooling it on a big drum (reel) may cause plastic deformations. When the pipe is reeled out at sea, again it undergoes plastic deformations. In order to model such a nonlinear behaviour, a model, which includes both elasto-plastic material characteristics and large deformations, is required. Discretisation of the pipe is performed by means of the Rigid Finite Element Method (RFEM), which allows us to study static and dynamic problems, taking into account required properties of the model. Different sea conditions are simulated and analysed. It is shown how operational conditions can limit the ability of laying operation by a dedicated vessel, due to an unstable system response. The second part of the paper presents an upgrade of a passive reel drive by application of an active drive. In the model used for control applications, the pipe is modelled by a neural network. This allows us to perform a real-time calculations. In this fast-response calculation model, nonlinear aspects of the system are taken into account. The improvements in a pipe laying vessel performance are presented by some results of numerical simulations. The conclusions are also formulated.     

Automated threshold selection methods for extreme wave analysis

The study of the extreme values of a variable such as wave height is very important in flood risk assessment and coastal design. Often values above a sufficiently large threshold can be modelled using the Generalized Pareto Distribution, the parameters of which are estimated using maximum likelihood. There are several popular empirical techniques for choosing a suitable threshold, but these require the subjective interpretation of plots by the user.In this paper we present a pragmatic automated, simple and computationally inexpensive threshold selection method based on the distribution of the difference of parameter estimates when the threshold is changed, and apply it to a published rainfall and a new wave height data set. We assess the effect of the uncertainty associated with our threshold selection technique on return level estimation by using the bootstrap procedure. We illustrate the effectiveness of our methodology by a simulation study and compare it with the approach used in the JOINSEA software. In addition, we present an extension that allows the threshold selected to depend on the value of a covariate such as the cosine of wave direction.     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

使用Python函数式编程进行空间回归分析

针对使用传统的编程方式进行空间回归分析过于繁杂的问题,本文提出将Python函数式编程引入空间回归分析方法,分析和探讨脚本语言＋函数式编程方式在GIS数据分析中的推广应用前景。编程实践证明,采用Python函数式编程,结合既有的开源软件包,可以有效提高编程效率,减少代码量,将实践人员的注意力集中在问题本身。     

Specifying a joint space- and time-lag using a bivariate Poisson distribution

Separate space- or time-lags have been considered regularly in data analyses; as space–time models are more recently being studied extensively in data analytic fashion, joint estimation of both lags has to be considered explicitly. This paper addresses this issue, taking into special consideration parametric parsimony together with specification richness; use of the bivariate Poisson frequency distribution is advocated and applied to an empirical case. The relation of this approach to random effects specifications is investigated. Data for Belgian regional products constitute the empirical case study.

人工神经网络在云南5月雨量预测中的应用

提出了一个基于３层ＰＢ神经网络的云南５月雨量的短期气候预测模型。由于神经网络本身是一个非线性动态系统,将其应用到短期气候变化预测这一非线性系统中,更能反映出气候系统状态量之间的物理关系和动力行为特性。并用该预测模型对云南５个区域１９９１～１９９８年的５月雨量距平趋势进行了共４０次的预测试验和检验,其预测的距平符号相关准确率可达到７７．５％。     

井冈山森林火灾与气象条件的相关分析

根据江西井冈山森林火灾资料,利用一元回归和逐步回归分析方法,对井冈山1991—2000年森林火灾发生期间的连旱日数、降水量、相对湿度、气温、风速5个主要气象指标进行分析,将各指标分别与火灾发生次数、森林受害面积进行回归计算,建立了利用连旱日数和降水量预测森林火灾受灾面积及发生次数的数学模型。模式运算结果表明,当火灾发生前15 d连旱日数增加时,火灾次数增加;当火灾发生前15 d连旱日数增加、降水量减少时,火灾面积增加。