基于深度强化学习的智能船舶航迹跟踪控制

[目的]智能船舶的航迹跟踪控制问题往往面临着控制环境复杂、控制器稳定性不高以及大量的算法计算等问题。为实现对航迹跟踪的精准控制,提出一种引入深度强化学习技术的航向控制器。[方法]首先,结合视线(LOS)算法制导,以船舶的操纵特性和控制要求为基础,将航迹跟踪问题建模成马尔可夫决策过程,设计其状态空间、动作空间、奖励函数;然后,使用深度确定性策略梯度(DDPG)算法作为控制器的实现,采用离线学习方法对控制器进行训练;最后,将训练完成的控制器与BP-PID控制器进行对比研究,分析控制效果。[结果]仿真结果表明,设计的深度强化学习控制器可以从训练学习过程中快速收敛达到控制要求,训练后的网络与BP-PID控制器相比跟踪迅速,具有偏航误差小、舵角变化频率小等优点。[结论]研究成果可为智能船舶航迹跟踪控制提供参考。     

考虑空车调配协调的货物列车开行方案优化研究

为实现空车调配与货物列车开行方案协调优化，结合基本运行图架构与车流径路，构建货运时空服务拓展网络。考虑配空与装卸取送、集编发等环节的时间接续要求，节点与区段不对流空车要求，以重车流全程运送与空车配送等广义总费用最少为目标，建立整数规划弧路模型。针对既有算法设计局限性，结合重车或空车配空的时间接续要求，提出将不同的 k 短路重车流方案与空车配空方案相关联的改进可行解构造方法，设计混合差分进化求解算法。实例研究表明，考虑空车调配进行重车、空车流组织协调优化，能够减少空车走行费用，及时满足装车需求，有效保证作业车流配合中转车流集结编组及时挂线，提高方案可实施性。     

基于完全耦合算法的绕水翼流固耦合特性研究

基于完全耦合算法对绕二维NACA0009水翼流固耦合特性进行了数值模拟研究。采用Theodorsen模型和Munch模型对刚性和弹性水翼的水弹性响应进行了数值计算,分析了流体与结构的相互作用关系,研究了影响结构水弹性响应和流固耦合特性的因素。研究结果表明:考虑了流体黏性的Munch模型与基于势流理论的Theodorsen模型对气动弹性响应的数值计算结果基本一致,而Theodorsen模型由于没有考虑流体黏性在一定程度上低估了结构的水弹性响应。结构的惯性、阻尼和刚度力矩与流体的相应附加载荷均处于同一数量级,故流体与结构的相互作用不可忽略,尤其对于弹性水翼,流体的惯性、附加阻尼作用增大,流固耦合算法的数值稳定性对流固耦合特性的计算结果影响将更大。外部激励频率为非共振频率时,结构的刚度作用是影响水弹性响应的主要因素,外部激励频率为共振频率时,流体的附加阻尼和附加刚度作用减弱,除结构的刚度作用外,流体与结构的惯性作用对水弹性响应和流固耦合特性的影响也较大。     

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.     

基于主线拥堵场景的快速路级联信号控制方法

城市快速路高峰时段已经呈现出常态性拥堵，对快速路主线拥堵进行疏导尤为必要.本文通过在主线和入口匝道进行三级交通检测判别，设计了多级联动的信号控制流程与实现方式，构建了不同拥堵程度下的分级响应控制策略，基于瓶颈点通行能力最大化下的实时信号控制算法及最大排队长度限制下的实时信号控制算法，建立了主线及匝道车道开关闭、汇合处信号灯控制、主线动态限速、交通信息诱导等于一体的快速路级联信号控制方法.仿真分析结果表明，在主线不同拥堵程度下采取的级联信号控制方法，可以有效提升快速路主线平均车速，并未导致匝道排队的恶化，局部路网平均延误均有明显降低.     

非完美信息下京津冀区域物流设施选址研究

针对大规模物流设施选址问题,本文构建了非完美信息下设施可靠性选址的连续逼近模型.该模型不仅考虑了设施存在损坏的可能性,而且考虑了由于信息缺失顾客无法获得设施状态且存在返回出发地的行为.由于该模型的结果无法直接确定设施选址点,因此采用Disk算法对该模型的结果进行离散化,获得物流设施选址方案.以京津冀区域物流设施选址为例,验证了该模型有效性并对设施布局特性进行了分析.最后,通过灵敏度分析探讨了不同参数对京津冀区域物流设施选址的影响.     

Direct formulation and algorithms for the probit-based stochastic user equilibrium traffic assignment problem

This paper proposes simple and direct formulation and algorithms for the probit-based stochastic user equilibrium traffic assignment problem. It is only necessary to account for random variables independent of link flows by performing a simple transformation of the perceived link travel time with a normal distribution. At every iteration of a Monte-Carlo simulation procedure, the values of the random variables are sampled based on their probability distributions, and then a regular deterministic user equilibrium assignment is carried out to produce link flows. The link flows produced at each iteration of the Monte-Carlo simulation are averaged to yield the final flow pattern. Two test networks demonstrate that the proposed algorithms and the traditional algorithm (the Method of Successive Averages) produce similar results and that the proposed algorithms can be extended to the computation of the case in which the random error term depends on measured travel time.     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

A two-phase model for dry port location with an application to the port of Dalian in China

This paper provides guidance for an optimal and reasonable dry port layout for the port of Dalian in China. We present a two-phase framework on the location of dry ports, which solves the selection of candidate inland cities and optimal dry port location choice, respectively. Fuzzy C-Means Clustering is applied to select alternative cities in the vast hinterland of the seaport of Dalian, with a view to identify evaluation factors that affect the location selection decision. A cost-minimisation linear programming solution is proposed, with the aid of a genetic algorithm, to choose the optimal location as well as capacity level among the candidate inland cities.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.