Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.     

Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.     

Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem

In this paper, we study two closely related airline planning problems: the robust weekly aircraft maintenance routing problem (RWAMRP) and the tail assignment problem (TAP). In real life operations, the RWAMRP solution is used in tactical planning whereas the TAP solution is implemented in operational planning. The main objective of these two problems is to minimize the total expected propagated delay (EPD) of the aircraft routes. To formulate the RWAMRP, we propose a novel weekly line-of-flights (LOF) network model that can handle complex and nonlinear cost functions of EPD. Because the number of LOFs grows exponentially with the number of flights to be scheduled, we propose a two-stage column generation approach to efficiently solve large-scale real-life RWAMRPs. Because the EPD of an LOF is highly nonlinear and can be very time-consuming to accurately compute, we propose three lower bounds on the EPD to solve the pricing subproblem of the column generation. Our approach is tested on eight real-life test instances. The computational results show that the proposed approach provides very tight LP relaxation (within 0.6% of optimal solutions) and solves the test case with more than 6000 flights per week in less than three hours. We also investigate the solutions obtained by our approach over 500 simulated realizations. The simulation results demonstrate that, in all eight test instances, our solutions result in less EPDs than those obtained from traditional methods. We then extend our model and solution approach to solve realistically simulated TAP instances.     

多元评价体系组合模型在铁路隧道变形预测中的应用

为克服传统预测模型结构单一、预测精度及稳定性不足等缺陷,提出多元体系组合预测模型的建模思路。首先,基于支持向量机、BP神经网络及ARMA模型3种单一预测模型,构建铁路隧道变形预测体系;再以均方根误差、误差平方和及平均绝对误差等为评价准则或指标,构建各预测结果的误差评价体系,求解各单项预测模型的权值贡献指数,得到最优组合权值;然后利用后验差检验、残差检验和关联度检验构建预测精度校验体系,对组合预测结果进行检验,评价预测模型的有效性;最后,结合工程实例,对多元体系组合预测模型在特大断面隧道中的变形预测效果进行检验。结果表明:多元评价体系组合模型预测相对误差值均小于2%,具有较高的预测精度,且较单一预测模型具有更高的预测精度,也一致通过相关检验,验证了多元体系组合预测模型的有效性。     

构建网络安全体系,保障电子政务畅通

构建安全的电子政务网络体系对于推动政府工作信息化、促进政务公开、提高政府办公效率、构建和谐社会是非常重要的.在详细介绍目前网络安全一些认识误区的基础上,结合实际工作经验,从技术实施、制度建设方面阐述了如何构建安全的网络体系的问题.     

陆战场 ISR 无人机威胁分析及其评估模型研究

分析陆战场中情报、监视和侦察（IS R ）无人机的威胁影响因素及其特点,构建了IS R无人机威胁评估指标体系,利用遗传算法优化了BP神经网络评估模型,针对ISR无人机的威胁进行评估分级,仿真结果实际可行,为陆战场中无人机的编队模式和航路优化提供了可靠依据。     

基于便携式排放测试系统与BP神经网络的大型客车排放预测

以大型客车为研究对象,在长沙市的不同道路工况下进行了车载排放测试,借助道路测试得到的数据,利用BP神经网络,以逐秒的速度、加速度、比功率和油耗数据为输入,建立CO2、CO和NOx的排放预测模型,并用部分试验数据进行了验证.结果表明,CO2、CO和NOx预测结果的总体相关系数R为0.9167,线性高度相关,在整体误差水平...     

克隆选择算法在基坑支护位移反分析中的应用

在基坑工程中,传统的位移反分析方法计算量较大,不易掌握,且求得的岩土体力学性能参数的精度较低。为此,提出应用克隆选择算法求解位移反分析问题的新方法,其核心是采用BP网络为正演工具代替有限元方法,描述土体力学性能参数和位移的非线性映射,可减少计算量,并利用克隆选择算法为反演工具,通过反演分析确定土体力学性能参数,具有较高的精度。依据该反分析方法获得的岩土体力学性能参数又可以估算下一工况的基坑位移。仿真试验结果表明了新方法的可行性和有效性。     

海底混响非瑞利特性研究及神经网络应用

基于单元散射理论介绍了瑞利分布模型和K分布模型,通过计算混响偏度和峰度判断出海底混响偏离瑞利分布模型,并利用CW信号、LFM信号的试验混响数据进行阵元域、波束域上的PDF曲线拟合。结果表明,海底混响的统计特性更趋向于K分布模型。利用BP网络方法和海底混响、点目标仿真信号的PDF特性进行了目标识别验证,其正确识别率达到了92%以上,且计算量大大降低。