规范化安全评估与港口船舶交通安全

港口船舶交通是海上交通运输系统的一个子系统。该系统所具有的危险性，可能造成的损失，人们可接受的风险值究竟如何，所有这些都依赖于有效和合理的安全评价。规范化安全评估(FSA)是近年来国外已在不断推广应用的一种安全评估方法。在简要介绍FSA特点与实施步骤的基础上，文中根据港口船舶交通安全评估的实际情况与需要，采用人工神经网络的方法对港口船舶交通系统的模型和技术路线进行了论述，并就相应人为因素的研究提出一些建议，以改进港口船舶交通安全工作和提高其安全水平。     

铁路轨道不平顺预测模型研究与应用

在保障列车行车安全的前提下提高维修效率和减少经济开支具有重要意义。为此,利用综合轨道检测车检测的历史轨道不平顺动态检测数据TQI值进行科学合理的分析,建立一种基于数据选择向量的非等时距灰色模型和神经网络理论相结合的预测方法,对实际线路轨道不平顺值进行预测,相对误差分别为2.63%、2.516%和2.025%。将预测模型应用在年度轨道状态最优综合维修计划的编排中,以养护维修时间和维修地点为决策变量,以年度轨道不平顺平均值最小为目标函数,在考虑了一系列约束函数的情况下,建立了利用遗传算法求解最优解的辅助决策模型。实验结果表明,该方法提高了预测精度,具有较好的实用性,能够快速地编排出线路的年度养护计划。     

Changes in the design and operational wind due to climate change at the Indian offshore sites

The increasing global warming is most likely to affect the magnitude and pattern of wind at a regional level and such an effect may or not follow the trend predicted at the global scale. Regional level exercises are therefore necessary while making decisions related to engineering infrastructure. In this paper an attempt is made to know the extent of change in design as well as operational wind conditions at two offshore locations along the west coast of India. The design wind speeds with return periods of 10, 50 and 100 years derived for two 30-year time slices in the past and future are compared. In two separate exercises the past and future wind at the local level is simulated by empirical downscaling as well as by interpolation of the general circulation model (GCM) output. Both sets of past and future data are fitted to the Generalized Pareto as well as Weibull distributions using the peak over threshold method to extract long term wind speeds with a specified return. It is noticed that at the given locations the operational and design wind may undergo an increase of around 11%–14% when no downscaling is adopted and 14%–17% when the GCM data are downscaled. Although these figures may suffer from a certain level of statistical uncertainty the study points out to take a relook into the safety margins kept in the design and operation of ocean structures in the light of global warming.     

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

We propose Time–Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection between mathematical form of error correction model and traffic flow theory is revealed through the transformation of the well-known Fundamental Traffic Diagrams. A threshold regime switching framework is implemented to overcome any unknown structural changes in traffic time series. Spatial cross correlated information is incorporated with a piecewise linear vector error correction model. A Neural Network model is also constructed in parallel to comparatively test the effectiveness and robustness of the new statistical model. Our empirical study shows that the TS-TVEC model is an effective tool that is capable of modeling the complexity of stochastic traffic flow processes and potentially applicable to real time traffic state prediction.     

求解多目标跟踪中数据互联问题的数字网络结构研究

在多目标跟踪情况下，求解数据互联问题要求计算把第i个量测分配给第t个目标的概率βi^t。该文阐述了求解数据互联概率βi^t的神经网络方法，介绍了具有神经元网络结构的数字实现，并分析了该神经元网络结构的稳定性和收敛性。     

混凝土梁式桥梁应力释放神经网络模型研究

通过对模型梁实验数据的分析处理,结合BP神经网络技术,建立了基于神经网络的混凝土梁预应力释放模型,开发了预应力释放仿真界面,取得了基本符合模型实验情况的效果。     

神经网络在舰载米波搜索雷达方向估计中的应用研究

阐述了舰载米波搜索雷达天线阵转动时多的目标回波信号的模型及最大似然法实现舰载米波搜索雷达天线阵转动时多目标回波信号的方向估计，提出了方向估计的神经网络方法，仿真结果表明了此方法的有效性。     

Filling up gaps in wave data with genetic programming

A given time series of significant wave heights invariably contains smaller or larger gaps or missing values due to a variety of reasons ranging from instrument failures to loss of recorders following human interference. In-filling of missing information is widely reported and well documented for variables like rainfall and river flow, but not for the wave height observations made by rider buoys. This paper attempts to tackle this problem through one of the latest soft computing tools, namely, genetic programming (GP). The missing information in hourly significant wave height observations at one of the data buoy stations maintained by the US National Data Buoy Center is filled up by developing GP models through spatial correlations. The gap lengths of different orders are artificially created and filled up by appropriate GP programs. The results are also compared with those derived using artificial neural networks (ANN). In general, it is found that the in-filling done by GP rivals that by ANN and many times becomes more satisfactory, especially when the gap lengths are smaller. Although the accuracy involved reduces as the amount of gap increases, the missing values for a long duration of a month or so can be filled up with a maximum average error up to 0.21 m in the high seas.     

基于误差反传神经网络的船型要素建模分析方法及应用

探讨用ＢＰ神经网络建立船型要素建模分析理论和方法。讨论了建模的处理技巧。结合算例对基于ＢＰ网络的数学模型的表达方式、网络结构模型预报精度的影响及模型中变量的重要度分析方法等基本问题作了初步分析。