两类双结构矩阵的标准型及其应用

给出2类双结构矩阵的标准型。作为应用,还利用新得出的标准型对这2类双结构矩阵特征值问题发展了结构算法,其中对第一类双结构矩阵特征值问题发展了只用实运算的结构算法。     

Jason-1: Assessment of the System Performances

On 7 December 2001, Jason-1 was successfully launched by a Boeing Delta II rocket from the Vandenberg Air Force Base, California. The Jason-1 satellite will maintain the high accuracy altimeter service provided since 1992 by TOPEX/Poseidon (T/P), ensuring the continuity in observing and monitoring the Ocean Dynamics (intraseasonal to interannual changes, mean sea level, tides, etc.). Despite one-fourth the mass and power, the Jason-1 system has been designed to have basically the same performance as T/P, measuring sea surface topography at a centimetric level. This new CNES/NASA mission also provides near real-time data for sea state and ocean forecast. The first two months of the Jason-1 mission have been dedicated to the assessment of the overall system. The goals of this assessment phase were:

1. To assess the behavior of the spacecraft at the platform and payload levels (Jason-1 being the first program to call on the PROTEUS versatile multimission platform for Low and Medium Earth Orbit Missions developed in partnership between Alcatel Space and CNES);

2. To verify that platform performance requirements are met with respect to Jason-1 requirements;

3. To verify that payload instruments performance requirements evaluated at instrument level are met;

4. To assess the performance of the Jason-1 Ground System.

This article will display the main outputs of the assessment of the system. It will demonstrate that all the elements of the onboard and ground systems are within the specifications. Provision of data to the Jason-1 Science Working Team started at the end of March 2002. This is the goal of a six-month phase after closure of the initial assessment phase to derive the error budget of the system in terms of altimetry user products.     

基于ArcEngine的数字高程模型更新方法研究

采用COM技术,以ARCENGINE所提供的类库和凸包算法,实现了数字高程模型(DEM)数据更新的边界检测和利用矢量地形数据采集更新数字高程模型数据的方法。保证了数字高程模型数据的精度,可以满足数字高程模型的现势性需求。     

海洋调查数据的可视化建模及实现方法

科学计算可视化作为新一代的分析工具 ,正在进入物理海洋学领域。文中介绍对海洋调查数据进行可视化的关键问题——数据建模 ,并从分析海洋调查数据的特点出发提出针对海洋研究中可视化建模的原则和有效途径 ,给出相关的实现方法。其中 ,重点讨论局部体样条建模中必须面对的区域分割优化问题 ,引入遗传算法并给出较好的解决方案。     

基于模拟退火算法的饲料配方设计

本文在饲料配方设计中首次引入新颖、高效的组合优化算法--模拟退火算法，并获得理想的数值结果，说明了在传统的饲料配方设计中高技术的应用可以获得更高的效益.     

应用遗传神经网格方法分析赤潮监测数据

简要介绍了遗传神经网络方法的原理 ,探讨了应用遗传神经网络方法研究辽东湾海域丹麦细柱藻 (Leptocylindrusdanicus)赤潮与其环境因子之间的关系 ,计算了各环境因子对丹麦细柱藻赤潮的贡献。结果表明 ,温度、盐度、DIN的变化对研究海域丹麦细柱藻种群密度的增长有比较重要的影响 ,DIN是营养限制因子。遗传神经网络是分析赤潮监测数据的有效方法 ,值得进一步探索     

一种新型混沌遗传优化算法的研究

在对遗传算法交叉、变异后结果的处理方法进行改进并在将进化过程分段的基础上,提出1种新的混沌遗传算法。该算法利用混沌运动的遍历性择优产生初始群体,对每一轮遗传操作所得到的部分当前最优个体进行变尺度混沌搜索。仿真结果表明,该算法具有较高的优化效率,并能求得全局最优解。     

Neural network for wave forecasting among multi-stations

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data.     

GPS网络RTK内插算法分析与比较

在GPS网络RTK中,利用不同的内插算法,生成的流动站误差改正数不同,它们对定位结果的影响也不同。通过算例分析表明,选择合理的内插算法,不仅可以生成与流动站真实误差较为接近的误差改正数,还可以提高整周模糊度的解算成功率和导航定位的精度。     

The DNA de-methylating agent 5-azacytidine does not restore CYP1A induction in PCB resistant Newark Bay killifish (Fundulus heteroclitus)

Newark Bay (NB) killifish (Fundulus heteroclitus) have been chronically exposed to environmental contaminants that activate the aryl hydrocarbon receptor (AHR) and are tolerant to toxic effects and CYP1A induction provoked by AHR ligands. Resistance to CYP1A induction could be due to an epigenetic mechanism such as DNA methylation. We measured in-ovo CYP1A catalytic activity (ethoxyresorufin-O-deethylase, EROD) in NB and reference site killifish embryos aqueously exposed to various concentrations of the de-methylating agent 5-azacytidine, 5-AC (5, 50 and 500 μ(micro)M) with or without 0.2 μ(micro)g/l of the CYP1A inducer 3,3^′,4,4^′,5 pentachlorobiphenyl (IUPAC PCB126). Neither PCB126 alone, nor PCB126 plus 5-AC, induced EROD above levels in vehicle treated Newark Bay fish. In reference site fish, the same PCB126 dose provoked a 7.4-fold EROD induction relative to controls. We conclude that Newark Bay killifish are resistant to CYP1A induction by co-planar PCBs during early embryological development and our data suggests that DNA methylation does not play a critical role in resistance to CYP1A induction in this model.