A Linear Programming Model for Seabed Oil-Gas Pipe Network

This paper analyzes the pipe network system of oil-gas collection and transportation foroffshore oilfield development.A"0-1"integer linear programming model is constructed to optimize theinvestment of seabed pipe network.The mathematical model is solved by the spanning tree method ofgraph theory and network analysis.All spanning trees of a network graph compose all the feasible solu-tions of the mathematical model.The optimal solution of the model is the spanning tree with the minimumcost among all spanning trees.This method can be used to optimize the seabed pipe network system andgive a minimum cost plan for the development of offshore marginal oilfield groups.     

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     

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.     

海底油气集输管网可靠性评估

对海底油气集输管网进行了可靠性评估，提出了一种适用于树状海底油气集输管网可靠性评价的方法，将流可靠度和单元重要度的概念引入到海底油气集输管网可靠性的分析中，该方法能够有效地评价海底油气集输管网的可靠性。     

一种基于异步串行口的IBM-PC局部网

在综合的科学考察船上以异步通讯口实现的IBM-PC局部网具有较好的性能价格比。本文介绍了该网的联网环境、网络系统的组成及其拓扑结构、网络的服务功能以及网络软、硬件的安装和配置。     

可靠性约束下的海底油气集输管网系统优化

在管网系统满足一定可靠度的基础上，以费用最小为优化目标，对系统进行了优化，求出了满足一定可靠度指标的费用最小的管网布置方案。对树状网络可靠性进行了评估，提出了一个0-l规划模型，并运用图论和网络理论将管网系统的优化问题转变为求可靠度至少为α的网络固的最小生成树问题。     

Prediction of Load Carrying Capacity of Corroded Reinforced Concrete Beam

A novel method for prediction of the load carrying capacity of a corroded reinforced concrete beam (CRCB) is presented in the paper. Nine reinforced concrete beams, which had been working in an aggressive environment for more than 10 years, were tested in the laboratory. Comprehensive tests, including flexural test, strength test for corroded concrete and rusty rebar, and pullout test for bond strength between concrete and rebar, were condueted. The flexural test results of CRCBs reveal that the distribution of surface cracks on the beams shows a fractal behavior. The relationship between the fractal dimensions and mechanical properties of CRCBs is then studied. A prediction model based on artificial neural network (ANN) is established by the use of the fractal dimension as the corrosion index, together with the basic intbrmation of the beam. The validity of the prediction model is demonstrated through the experimental data, and satisfactory resuits are achieved.     

沉降监测多项式回归分析与神经网络预测

使用多项式和切比雪夫(Tchebyshev)多项式分别对沉降监测数据进行回归分析以预测未来沉降值,其中切比雪夫多项式的外推效果较好;应用前向BP神经网络对两种不同的单因子输入模式进行非线性函数逼近,并进行了不同采样步长的比较,实例表明将时间点作为网络的输入对沉降进行预测效果较好。     

人工神经网络方法反演东中国海悬移质浓度

海中悬移质是决定海洋光学性质、海洋水质,河口海岸带演变动力过程的重要环境参数。本文利用模拟遥感反射比数据集建立人工神经网络反演悬移质浓度,并利用东中国海现场同步数据对该算法进行验证。