波浪作用下潜标拖缆动力学分析

对三维潜标拖缆在规则波作用下的动力响应进行了研究,首先基于集中质量法给出了缆绳在规则波作用下的控制方程,然后提出了两种不同类型的自由端边界条件:质点型和艇型自由端边界条件,前者把拖体视为作三个自由度线运动的质点,而后者把拖体视为作六自由度运动的艇体,最后用有限差分法进行了数值研究。数值计算结果表明潜标拖缆在规则波的作用下,不仅作近似的简谐振动,还会在横向、垂向产生一个偏移量,该偏移量的大小随浪向角的变化而变化,另外受遭遇频率的影响,振动的幅值也随浪向角的变化而呈现不同的特征。     

Grey relational analysis on the relation between marine environmental factors and oxidation-reduction potential

The effects of marine environmental factors-temperature(T),dissolved oxygen(DO),salinity(S) and pH-on the oxidation-reduction potential(ORP) of natural seawater were studied in laboratory.The results show an indistinct relationship between these four factors and the ORP,but they did impact the ORP.Common mathematical methods were not applicable for describing the relationship.Therefore,a grey relational analysis(GRA) method was developed.The degrees of correlation were calculated according to GRA and the va...     

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□省厅01湖南发布国内首部《行政程序规则》5月31日,湖南省国土资源厅正式发布《湖南省国土资源厅行政程序规则》(以下简称《规则》),这是国内首部省直部门制定的行政行为和行政程序内部规则。该《规则》对制定规范性文件程序、行政执法程序、行政公开与查询     

一种基于TIN三角网数据的规则格网生成算法

格网地形模型和7TIN地形模型是数字高程模型的两个主要组成部分。结合实际工作,在实现多分辨率地形环境仿真需求下,充分利用已知TIN数据信息,通过划分格网点,判断格网点是否落在TIN三角网内,用三角形三个点高程内插得到格网点高程的方法,实现基于TIN数据生成格网数据。方法简单实用,在三维地形仿真应用中有较好的效果。     

基于灰关联分析方法卡尔曼滤波器的精度效能评估

针对综导系统卡尔曼滤波器的特点,深入研究了灰关联分析法在滤波器精度效能评估中的应用。给出卡尔曼滤波器进行灰关联分析的方法和步骤,并对某一类型的滤波器进行精度效能评估。仿真结果表明了该方法的可行性和有效性。     

规则格网上双方向性插值DEM精度研究

简要介绍双方向性插值方法的原理,并以线性插值和三次插值过程中的误差传播为基础,利用误差传播定律,推导出双方向性插值方法生成的DEM曲面平均高程误差值,并对线性插值和三次插值公式本身的精度进行分析。     

基于要素规则分类的城镇地籍数据建库方法研究

城镇地籍数据的有效分类与正确组织是地籍数据库规范建立和正常运行的前提条件。本文提出了基于地籍要素规则的分类方法,按照地籍数据的表达类型,描述了宗地背景图形数据、宗地图形数据、测量控制点数据、属性数据和档案数据的各个要素定义和编码规则。以成都市地籍数据库的建设为实例,根据各种来源的地籍数据分别开展了不同类型地籍数据的快速分类与入库工作。     

一种改进的Apriori挖掘算法

针对制约Apriori算法效率的瓶颈问题,提出了一种基于Apriori的改进算法,算法只需要遍历一次事务数据库,避免了Aprori算法多次扫描事务数据库和产生大量的候选项集,同时利用项集的有序性改进了判断连接的方式,优化了连接步骤,减少了判断时间.实验结果表明,改进的Apriori算法在运行效率上有一定的提高.     

多模式控制在关联系统中的应用研究

根据多模式控制器的设计理论,以偏差分段为基础,将控制过程分为几个阶段,用不同的模式加以控制,用加速储能控制来解决响应快速性的要求;用快速放能控制来解决响应平稳性要求;用稳态能量控制来解决系统的准确性要求。把控制模式用于双输入双输出关联系统的解耦系统中进行了仿真研究,与常规控制方法做了比较,多模式控制在关联系统中具有良好的动态响应性能。     

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.