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.     

Field measurements and monitoring of wastewater discharge in sea water

This paper reports a study of the field measurements and monitoring of wastewater discharge in sea water at Bari East (Italy). A wastewater sea outfall system is an integral and fundamental part of each wastewater treatment with ultimate sink in the sea water. The design of a water treatment plant and wastewater outfall must take into account the use of the environmental water, the values of physicochemical parameters to be respected in order to safeguard the use itself and the quality of the environmental water where wastewater is issued. In the present study measurements of sea current velocity components were carried out with a VM-ADP (Vessel-Mounted Acoustic Doppler Profiler). Salinity, wind directions and velocities were assessed with, for one survey, the total faecal coliforms and other biochemical parameters. It was emphasized that the measurements necessary for monitoring cannot be concentrated in the wastewater outfall pipe zone only, but should be extended to a neighbouring area of the outfall pipe, with an extension depending on the wastewater discharge, the polluting charge and the magnitude of the sea currents and the winds typical of the zone of interest. The analyses presented in this paper confirm that the sea zones close to the wastewater outfall pipe are particularly sensitive and vulnerable. Such results must be considered in the planning of a wastewater outfall pipe.     

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。     

动态基准实时测量系统的开发与应用

介绍了动态基准实时测量系统的硬件组成、站点上全站仪棱镜组合安装与检测方法、系统软件的开发及特点，最后对系统在广州地铁某地铁站结构变形自动监测中的应用情况进行了阐述。通过实际应用表明该系统能应用在自动化、高精度的变形监测领域。     

船舶机舱自动集中监测报警系统结构与功能分析

船舶机舱集中监测与报警系统目前已在船舶设计制造中得以广泛应用。对其组成、工作流程及功能进行了较为系统的阐述 ,为船舶使用者提供借鉴     

水下自航式海洋观测平台技术发展研究

水下自航式海洋观测平台是一种新型海洋观测平台，主要用于无人、大范围、长时间水下环境监测，包括物理学参数、海洋地质学和地球物理学参数，海洋化学参数、海洋生物学参数及海洋工程方面的现场接近观测。其特点是：成本低，环境适应性强，可冲破人工潜水极限而进入现场进行接近观测，免除了ROV需要水面支援母船的累赘，减少作业经费，体积小，使用方便，便于布放回收；可根据水声信号摇控或预置程序控制，按要求进行相关项目观测；有自主动力，水下运行时间相对较长，有源噪声低，可进行隐弊观测，正是基于以上的优点，近几年颇得海洋环境监测和海军方面的青睐，本文介绍了国际上水下自动观测平台的发展历史和现状，提出了我国开展研究水下自动观测平台的重要性及研究的面容。     

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。