潜水器载人舱的不确定性测度计算方法研究

本文研究状态函数中存在概率数和证据数的混合状态下的系统不确定性分析方法问题,用于解决在产品实现过程中部分信息的认知不足。传统概率分析方法无法建立认知不确定信息模型,无法反映产品在实现过程中不确定性影响的问题。本文将证据理论不确定度分析方法融入传统JC算法,在计算随机变量迭代时开展认知变量极值计算,进而可以处理随机和认知不确定性变量同时存在时的混合不确定性问题。本文建立了4500 m载人潜水器载人舱不确定度分析模型,基于改进JC算法计算得到载人舱失效似然性测度和失效可信性测度值,同时采用实验设计法对影响载人舱不确定性测度的因素进行分析,分析表明测度影响较大的因素是真球度和极限拉伸强度,测度对真球度和厚度两个因素的单位数量变化比较敏感。     

基于遗传算法的盾构隧道管片接头刚度反演研究北大核心CSCD

梁-弹簧模型在管片衬砌结构计算中应用较广泛,目前接头刚度取值方法主要有模型试验法及经验取值法,模型试验法耗时长、成本高昂,而依据经验取值难免存在一定主观性。鉴于此,利用C++编程语言,基于遗传算法开发了盾构隧道管片结构位移反分析程序,首先通过弹性模量值反演算例,得出反演值与理论值误差为1.4%,验证了算法和程序的可行性。利用程序将反演接头刚度值与实际设计案例管片接头刚度值进行对比,反演计算结果表明:正弯矩下管片环向接头转动刚度值反演误差为2.6%,负弯矩下管片环向接头转动刚度值反演误差为4.3%。     

数据采集算法的研究

介绍利用C#编程制作数据采集软件的方法,讨论该软件在工程中的应用情况.数据采集软件利用以太网协议读取PLC中的数据,并将现场设备信息存储到数据库中,以实现对PLC采集数据的自动计算,节省人力,缩短项目开发周期.     

基于SOC自适应分阶的动力锂电池两步优化快速充电策略

快速高效的充电方式对于推动汽车电动化,加快以石油为主导的传统交通能源向绿色低碳能源转型,实现中国"双碳战略"的目标具有重要意义。针对充电时间和充电损失的平衡优化问题,提出了一种基于SOC自适应分阶的两步优化多阶恒流充电策略。为实现充电过程的优化分阶,利用改进的二分K-means算法对基于内阻曲线的采样点集进行聚类,实现了充电区间关于内阻变化和分布特征的自适应划分。基于分阶优化结果,采用改进的非支配排序哈里斯鹰优化算法(INSHHO)求解优化电流对应帕累托前沿。利用Logistic混沌初始化及自适应t分布突变算子对哈里斯鹰模型(HHO)进行改进,进一步提升算法的全局寻优能力。最后通过充电对比试验,将优化多阶恒流充电策略与恒流恒压策略(CC-CV)和均分多阶恒流充电策略在不同充电时间条件下进行充电性能对比。结果表明:在充电时间保持一致的条件下,提出的优化多阶恒流充电策略较恒流恒压策略和均分多阶恒流充电策略的充电欧姆损失最大分别减少1.03%和0.3%;在温升表现上,优化多阶恒流充电策略较均分多阶恒流充电策略的充电温升最多降低了0.82℃。     

基于梯度关联规则的老年行人交通事故风险识别

为构建老年行人交通事故严重程度风险关联因素识别方法体系，本文应用极限梯度提升关联规则挖掘算法(Extreme Gradient Boost-Apriori，XGB-Apriori)识别城市道路老年行人交通事故风险因子。运用机器学习优化关联规则算法结构，通过机器学习库 scikit-learn 中 XGBoost (Extreme Gradient Boost)算法与SFM(Select From Model)特征选择类功能实现变量特征值的选择。进而，对Apriori算法设置有序定向约束，得到适用于交通事故致因分析的数据挖掘技术。通过逐层迭代识别关联项，选取频繁项集，总结高置信度、高提升度的关联规则。关联因素模型评估结果表明，本文采用的SFM功能准确度可达78.31%，关联规则XGB-Apriori算法较传统算法精度提升了91%。挖掘结果显示，驾驶员与行人的自身特征、车辆特征、碰撞状态以及道路特征均对老年行人交通事故的严重程度具有重要影响。其中，男性驾驶员造成的行人死亡事故频次较高，女性驾驶员造成的受伤事故频次较高；大型、重型车辆(SUV、卡车、施工车)发生死亡事故频次相对小轿车更高；位于匝道等道路线型弯曲的坡道中，老年行人发生致死交通事故的频次相对线型缓和路段更高。本文对老年行人交通事故耦合因素全面识别并针对性提出风险防控精准预判方法，为有效保护道路弱势群体提供必要的理论支持。     

C76系列敞车撑杆优化改进探讨

针对C₇₆系列敞车在运用过程中出现的撑杆脱落和折断故障进行了现场调研,分析了故障原因,并提出了将原焊接结构调整为螺栓连接结构的改进方案,实践证明该方案效果明显。     

A hybrid heuristic strategy for liquefied natural gas inventory routing

As liquefied natural gas (LNG) steadily grows to be a common mode for commercializing natural gas, LNG supply chain optimization is becoming a key technology for gas companies to maintain competitiveness. This paper develops methods for improving the solutions for a previously stated form of an LNG inventory routing problem (LNG-IRP). Motivated by the poor performance of a Dantzig-Wolfe-based decomposition approach for exact solutions, we develop a suite of advanced heuristic techniques and propose a hybrid heuristic strategy aiming to achieve improved solutions in shorter computational time. The heuristics include two phases: the advanced construction phase is based on a rolling time algorithm and a greedy randomized adaptive search procedure (GRASP); and the solution improvement phase is a series of novel MIP-based neighborhood search techniques. The proposed algorithms are evaluated based on a set of realistic large-scale instances seen in recent literature. Extensive computational results indicate that the hybrid heuristic strategy is able to obtain optimal or near optimal feasible solutions substantially faster than commercial optimization software and also the previously proposed heuristic methods.     

Dynamic airspace sectorisation for flight-centric operations

Today’s air traffic operations follow the paradigm of ‘flow follows structure’, which already limits the operational efficiency and punctuality of current air traffic movements. Therefore, we introduce the dynamic airspace sectorisation and consequently change this paradigm to the more appropriate approach of ‘structure follows flow’. The dynamic airspace sectorisation allows an efficient allocation of scarce resources considering operational, economic and ecological constraints in both nominal and variable air traffic conditions. Our approach clusters traffic patterns and uses evolutionary algorithms for optimisation of the airspace, focusing on high capacity utilisation through flexible use of airspace, appropriate distribution of task load for air traffic controllers and fast adaptation to changed operational constraints. We thereby offer a solution for handling non-convex airspace boundaries and provide a proof of concept using current operational airspace structures and enabling a flight-centric air traffic management. We are confident that our developed dynamic airspace sectorisation significantly contributes to the challenges of future airspace by providing appropriate structures for future 4D aircraft trajectories taking into account various operational aspects of air traffic such as temporally restricted areas, limited capacities, zones of convective weather or urban air mobility. Dynamic sectorisation is a key enabling technology in the achievement of the ambitious goals of Single European Sky and Flightpath 2050 through a reduction in coordination efforts, efficient resource allocation, reduced aircraft emissions, fewer detours, and minimisation of air traffic delays.     

人机混驾环境下混行车辆雾模型研究

目前搭载高级驾驶辅助系统和车联网（Vehicular Ad Hoc Network，VANET）技术的智能网联车（Intelligent Connected Vehicles，ICV）正大量涌入人工驾驶车（Manual Vehicle，MV）流之中，ICV与MV共存的异构车辆混行交通态势逐步形成，异构车辆之间的交互产生壁垒。混行之下单个ICV虽可依托单车硬件传感与单车计算单元实现与MV的交互意图识别，但其受有限算力与有限传感的影响，资源负载增大，时效性与安全性方面存在一定的误差与风险，而混行之下的VANET技术也不能够提供全局性车路资源用以高度匹配ICV与MV的交互场景，而且越来越多的ICV计算需求也在激增VANET的负载压力。对此，结合边缘计算概念中的雾计算理论，提出混行车辆雾模型（Mixed Vehicle Fog，MVF），充分发挥车联网络边缘节点能力，通过合理整合调度ICV资源的方法，解决对MV正常交互意图计算的时效性与安全性问题。该模型首先通过各感知单元响应混行交通环境下ICV与MV的正常交互事件，然后利用基于容错节点分簇的资源调度算法（Fault-tolerant Node Clustering Resource Scheduling Algorithm，FNC-RSA），动态划分局部路段内对交互事件具有相关意图感知与计算需求的ICV为一组"协同雾群"，再评估雾内ICV节点自身资源与路由代价，定向定量调度资源，最终实现雾群内部MV交互信息共享与驾驶意图协同计算。试验借助Prescan和MATLAB搭建联合仿真平台，与低能耗自适应分簇型路由算法（Low Energy Adaptive Clustering Hierarchy，LEACH）模型对比，验证MVF模型的运行效率与模型鲁棒性。研究结果表明：MVF模型通过交互事件细分协同雾群，保证了计算负载均衡，提高了ICV定向资源计算与传输效率，比LEACH模型降低了55.17%的平均跳数，缩短了45.40%的平均任务完成时间，抗时延干扰能力强，鲁棒性能优异。该模型对于打破混行环境异构车辆交互壁垒，提高混行道路交通行车安全，创造车联网络良性发展空间具有积极作用。     

Solving a discrete multimodal transportation network design problem

This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level mathematical program with complementarity constraints (MPCC). The decision variables, including the expanded capacity of auto links, the layout of bus routes, the fare levels and the route frequencies, are transformed into multiple sets of binary variables. The layout of transit routes is explicitly modeled using an alternative approach by introducing a set of complementarity constraints. The congestion interaction among different travel modes is captured by an asymmetric multimodal user equilibrium problem (MUE). An active-set algorithm is employed to deal with the MPCC, by sequentially solving a relaxed MMNDP and a scheme updating problem. Numerical tests on nine-node and Sioux Falls networks are performed to demonstrate the proposed model and algorithm.