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。     

一种新的地球物理反演方法——模拟原子跃迁反演法

详细研究了一般地球物理反问题的迭代优化求解过程与物理学中原子跃迁过程的对应关系,建立了反演问题中模型空间、初始模型、局部极值模型、最优化模型等与原子的态空间、定态、激发态、基态等的对应关系. 在此基础上,模拟了物理学中原子从激发态向基态跃迁的物理过程,建立了一种与原子跃迁过程相对应的非线性随机跃迁数学模型和模型解跃迁搜索准则,导出了适用于一般地球物理资料的模拟原子跃迁的非线性反演算法. 用理论测试函数对这种新的反演方法进行了数值试验,结果表明该方法具有解不依赖于初始模型、收敛速度快等优点.     

面向海上目标搜索任务的多无人机协同航路优化

提出了一种面向多无人机(unmanned aerial vehicle,UAV)协同搜索海上目标任务的航路优化方法。首先,分析多无人机协同航路优化问题的基本要素模型。然后,在对各UAV独立维护的目标概率图信息进行探测更新的基础上,采用状态预测一致性算法实现目标概率图信息的快速融合。最后,同时考虑局部搜索收益与未来搜索收益,采用分布式模型预测控制(distributed model predictive control,DMPC)方法优化各UAV的搜索航路。仿真结果表明,本研究提出的方法具有较高的搜索效率,可有效应用于海上目标的快速搜索任务,具有重要的应用价值。     

基于分区间泛化Kriging近似模型的船舶局部结构形状优化

为提高Kriging近似模型在船舶结构性能多维度响应预测方面的适用性,对常规Kriging近似模型进行分区间泛化改进:一是对设计样本点的各个维度(分量)进行划区,并在每一个分区间内采用最优拉丁超立方(OLhd)取样;二是引入比例系数w1组合高斯与指数型相关函数提高模型对数据的泛化能力,从而在每个划分的区间内建立泛化的Kriging近似模型。通过SCH测试函数,验证了构造的分区间泛化Kriging模型有效性。结合参数化建模和改进Kriging近似模型对某过渡肘板和舱口角隅边界进行形状优化,优化第一阶段由改进的近似模型通过多岛遗传算法得到全局初步的最优解,第二阶段在初步解的基础上缩小优化变量范围,由少量的FEM计算即可在小范围内搜寻到精确的最优设计变量。结果表明:分区间泛化Kriging近似模型在预测多维度响应时较常规Kriging模型预测精度更高;分阶段的形状优化流程在保证极小误差和缩小计算成本的情况下能够得到理想的应力分布和重量优化结果,有助于船舶结构的轻量化研究。     

GNSS控制网方案优化与数据处理分析

GNSS布设平面控制网已在工程项目中得到广泛应用.获得满意的平差结果的前提条件为:①高质量的基线数据;②约束GNSS控制网的已知点精度足够高,使得GNSS控制网的整体精度不受已知点约束而有明显改变;③优化的平差方案;④科学严密的平差模型.通过对某项目GNSS控制网的布设进行优化设计,并利用不同软件对观测数据进行处理分析...     

Selection of sand models and identification of parameters using an enhanced genetic algorithm

Numerous constitutive models of granular soils have been developed during the last few decades. As a consequence, how to select an appropriate model with the necessary features based on conventional tests and with an easy way of identifying parameters for geotechnical applications has become a major issue. This paper aims to discuss the selection of sand models and parameters identification by using genetic algorithm. A real‐coded genetic algorithm is enhanced for the optimization with high efficiency. Models with gradually varying features (elastic‐perfectly plastic modelling, nonlinear stress–strain hardening, critical state concept and two‐surface concept) are selected from numerous sand models as examples for optimization. Conventional triaxial tests on Hostun sand are selected as the objectives in the optimization. Four key points are then discussed in turn: (i) which features are necessary to be accounted for in constitutive modelling of sand; (ii) which type of tests (drained and/or undrained) should be selected for an optimal identification of parameters; (iii) what is the minimum number of tests that should be selected for parameter identification; and (iv) what is the suitable and least strain level of objective tests to obtain reliable and reasonable parameters. Finally, a useful guide, based on all comparisons, is provided at the end of the discussion. Copyright © 2015 John Wiley & Sons, Ltd.     

Fuzzy Optimization Neural Network Approach for Ice Forecast in the Inner Mongolia Reach of the Yellow River/Approche d'Optimisation Floue de Réseau de Neurones pour la Prévision de la Glace Dans le Tronçon de Mongolie Intérieure du Fleuve Jaune

Abstract

In ice forecasting, a key problem is the forecast of freeze-up and break-up dates. Ice-water mechanics and the principle of heat-exchange were mainly adopted in previous research. However, the mathematical models in these studies are complex and many parameters are required in relation to upstream and/or downstream gauging stations. Moreover, too many assumptions or simplifications for these parameters and constraints directly lead to low accuracy of the models and limitations as to their practical applications. This paper develops a fuzzy optimization neural network approach for the forecast of freeze-up date and break-up date. The Inner Mongolia reach lies in the top north of the Yellow River, China. Almost every year ice floods occur because of its special geographical location, hydrometeorological conditions and river course characteristics. Therefore, it is of particular importance for ice flood prevention to forecast freeze-up date and break-up date accurately. A case study in this region shows that the proposed methodology may allow obtaining useful results.     

基于梯度分析法的长江上游水库群供水-发电-环境互馈关系解析

长江上游水资源耦合系统优化调控涉及供水、发电和生态需水等相互竞争、不可公度的调控目标。其供水-发电-环境互馈协变机制难以数学解析和刻画，多维目标效益均衡优化调控难以实施。为此，以多目标优化、运筹学理论及方法为基础，提出了基于梯度分析法的供水-发电-环境两两互馈关系研究方法。通过多目标约束优化方法求解长江上游水库群联合优化调度在供水-发电-环境目标空间的最优解集，并进行插值构造了供水-发电-环境互馈关系多维空间曲面，以此为基础，采用一阶差分近似求解供水对环境和发电对环境偏导函数值，以量化环境对供水变化和发电变化响应的梯度，进而解析环境-供水和环境-发电间互馈协变关系。该方法为水库群优化调度多目标互馈关系研究提供了一种新的思路。     

蔚县矿区岩溶水供排结合研究

本文从系统观点出发,分析了矿区岩溶水供排结合的水文地质条件,提出了以供代排,合理的规划蔚县矿区地下水资源的供排结合方案。应用有限元数值模拟与最优化技术建立供排结合水量规划的教学模型,求出了蔚县矿区最佳水源地位置和最佳的供水开采量与疏干量,实现了较好的经济效益。     

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.