基于遗传算法的集输管网井组划分

通过对油田集输管网的分析研究,建立了基于产量距离之和最小的井组最优划分的数学模型,并采用基于整数编码的遗传算法对模型进行了求解.实例计算表明:建立的以产量距离之和最小为目标的数学模型更能反映集油管网投资的经济性,采用基于整数编码的遗传算法进行求解,其寻优效益高,且收敛性和稳定性也较好.最后求得的产量距离和为18 937.93 km·t/d,比文献[2]中的划分结果小1 381.07 km·t/d.     

基于遗传算法的机车二系支承载荷调整优化方法

为了改善机车轮轴重量分配的均匀性,从分析机车二系支承结构出发,建立二系载荷分布弹簧高度调整模型,提出应用遗传算法实现机车二系支承点加垫高度值的快速优化计算。针对机车二系支承载荷分布多维参数优化问题的特点,给出基于实数编码的改进遗传算法模型。对国产SS9,SS3b型机车实际应用,可使二系支承载荷分布最大误差进一步减小8%～15%,平均计算过程时间减少约20%。结果表明这种方法克服了基于误差梯度信息迭代搜索求解支承点加垫最佳值算法的不足,在获得一致的全局优化(近似)解,以及在减小运算时间、提高调簧算法的可靠性和实时性等方面具有明显的效果。     

空间多万向节传动布置的优化设计

本文从控制振源降低万向节传动引起的传动系与整车振动噪声出发,提出了空间多万向节传动最优布置的数学模型,选择了适用的优化算法,编出了相应的程序。实例计算表明,所提优化设计方法实用、可行,具有足够精度,所编程序计算速度快,使用方便,便于推广。     

CA4DC1柴油机配气机构设计

CA4DC1型柴油机是在CA498柴油机基础上开发的能够满足欧Ⅲ排放法规要求的柴油机.介绍了该柴油机在配气机构总体布置、气门组零件设计、气门传动机构设计、凸轮型线设计等方面所作的改进.     

船舶抗沉计算与决策模型研究

船舶抗沉计算和决策是及时、准确地进行损管行动的基础。依据不沉性理论、数值分析、专家系统和多目标决策理论,建立了船舶的抗沉计算模型和抗沉方案优选决策模型,并以某型船舶为实例进行了数值仿真,结果表明了模型的可行性和有效性。     

Simultaneous optimization of transit line configuration and passenger line assignment

Passenger transportation in most large cities relies on an efficient mass transit system, whose line configuration has direct impacts on the system operating cost, passenger travel time and line transfers. Unfortunately, the interplay between transit line configuration and passenger line assignment has been largely ignored in the literature. This paper presents a model for simultaneous optimization of transit line configuration and passenger line assignment in a general network. The model is formulated as a linear binary integer program and can be solved by the standard branch and bound method. The model is illustrated with a couple of minimum spanning tree networks and a simplified version of the general Hong Kong mass transit railway network.     

GIS中基于栅格转换的缓冲区生成算法

缓冲区生成是GIS中非常重要的一种分析方法,现有的算法数据量大、运算效率低.在"DEComGIS的研究与开发"课题中我们运用栅格转换来实现缓冲区生成,实践证明该算法是有效的.     

Dynamic change of aircraft seat condition for fast boarding

Aircraft boarding is a process mainly impacted by the boarding sequence, individual passenger behavior and the amount of hand luggage. Whereas these aspects are widely addressed in scientific research and considered in operational improvements, the influence of infrastructural changes is only focused upon in the context of future aircraft design. The paper provides a comprehensive analysis of the innovative approach of a Side-Slip Seat, which allows passengers to pass each other during boarding. The seat holds the potential to reduce the boarding time by approx. 20%, even considering operational constraints, such as passenger conformance to the proposed boarding strategy. A validated stochastic boarding model is extended to analyze the impact of the Side-Slip Seat. The implementation of such fundamental change inside the aircraft cabin demands for adapted boarding strategies, in order to cover all the benefits that accompany this new dynamic seating approach. To reasonably identify efficient strategies, an evolutionary algorithm is used to systematically optimize boarding sequences. As a result, the evolutionary algorithm depicts that operationally relevant boarding strategies implementing the Side-Slip Seat should differentiate between the left and the right side of the aisle, instead of the current operationally preferred boarding from the back to the front.     

基于蚁群算法的维修保障资源应急调度研究

在保障资源损毁后,将现有保障资源整合成机动保障小组,按照一定的机动路线巡回实施支援保障,可以最大限度的节省现存保障资源,提高保障效能。实施机动巡回的资源调度,需要确定机动保障小组的数量以及机动的路线,这类问题可归结为带约束条件的组合优化问题。应用蚁群算法建立了机动巡回保障优化数学模型,给出了算法的实现步骤。案例结果表明,该算法可以快速、有效求得巡回保障的优化解,得到巡回保障过程中的较优方案。     

Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor

This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.