小波变换在管道泄漏点定位中的应用

当管道发生泄漏时，泄漏诱发的负压波传播到管道上下游监测点时会使该点压力信号幅值产生瞬态下降，利用小波变换模极大值检测压力信号的突变点，从而计算出管道泄漏诱发的负压波传播到上下游监测点的时间差。在MATLAB6．1软件环境下，以某次实验数据为例进行管道泄漏点定位研究。在计算过程中小波基函数选取为Haar小波，尺度水平为6，计算结果表明该方法可以计算出管道泄漏点处诱发的负压波传播到管道上游监测点的时间差，从而为准确定位泄漏点提供了基础。     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

基于改进BP-GA方法的FPSO舷侧结构耐撞性能优化设计

基于遗传算法和ABAQUS参数化有限元仿真技术,对传统的BP-GA优化方法进行改进,并采用改进的BP-GA方法对浮式生产储油卸油装置(FPSO)舷侧结构的耐撞性能进行优化,以验证其可行性和准确性。结果表明,与传统的BP神经网络相比,经遗传算法优化的BP神经网络具有更高的预测精度和更强的泛化能力;改进的BP-GA优化方法可在结构减重的基础上进一步提高结构的耐撞性能,能较好地适用于复杂的FPSO舷侧结构耐撞性优化设计。采用的优化方法具有通用性,可为抗爆性能的优化设计提供参考。     

Highway voting system: Embracing a possible paradigm shift in traffic data acquisition

The integration of internet and mobile phones has opened the door to a new wave of utilizing private vehicles as probes not only for performance evaluation but for traffic control as well, gradually replacing the role of traffic surveillance systems as the dominant source of traffic data. To prepare for such a paradigm shift, one needs to overcome some key institutional barriers, in particular, the privacy issue. A Highway Voting System (HVS) is proposed to address this issue in which drivers provide link- and/or path-based vehicle data to the traffic management system in the form of “votes” in order to receive favorable service from traffic control. The proposed HVS offers a platform that links data from individual vehicles directly with traffic control. In the system, traffic control responds to voting vehicles in a way similar to the current system responding to prioritized vehicles and providing the requested services accordingly. We show in the paper that the proposed “voting” system can effectively resolve the privacy issue which often hampers traffic engineers from getting detailed data from drivers. Strategies to entice drivers into “voting” so as to increase the market penetration level under all traffic conditions are discussed. Though the focus of the paper is on addressing the institutional issues associated with data acquisition from individual vehicles, other research topics associated with the proposed system are identified. Two examples are given to demonstrate the impact of the proposed system on algorithm development and traffic control.     

基于模糊PID控制的ABS仿真研究

以车轮滑移率为控制对象,分析了模糊控制的理论基础,将传统的模糊控制与经典的PID控制相结合,提出了白适应模糊PID控制算法,并进行了仿真研究.     

NT4-Ant-Shepherdin[79-87]融合基因重组载体的构建

目的设计构建携带NT4-Ant-Shepherdin[79-87]的cDNA融合基因的重组载体,为针对Survivin的靶向治疗奠定基础。方法应用非对称引物/模板法、PCR技术制备NT4-Ant-Shepherdin[79-87]cDNA片断,连接于pGEM-T-Easy载体,经克隆测序、酶切后与PBV220/NT4质粒连接;转化感受态细胞E.coliDH5α,亚克隆获得NT4-Ant-Shepherdin[79-87]融合基因。结果克隆出Ant-Shepherdin[79-87]基因,经酶切及测序证实结果正确;连接PBV220/NT4,经克隆、酶切,琼脂糖凝胶电泳证实获得321bp的NT4-Ant-Shepherdin[79-87]目的基因片断。结论通过分子生物学技术成功构建了携带NT4-Ant-Shepherdin[79-87]融合基因的重组载体,为进一步研究针对Survivin的靶向抗肿瘤作用奠定了基础。     

铁路空车调整优化模型及其蚁群算法

以理论研究为主,通过对空车调整问题的特点分析,明确了空车数量调配和网络配流是空车调整的两个核心问题;在分析已有模型及对问题进行抽象描述的基础上,建立了空车调整协同优化（EWDCO）模型,并设计了相应的蚁群算法。分析发现,共同径路约束与EWDCO模型是不协调的;通过对定理的证明得出,对流约束不会对EWDCO模型的最优解产生影响,模型可以描述为线性整数规划模型,其实质是带容量约束的最小费用流模型。同时,研究表明ACO对求解空车调整问题具有一定的优势,是一种较为有效的算法。     

Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand

To improve the efficiency of large-scale evacuations, a network aggregation method and a bi-level optimization control method are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial sub-network and balances accuracy and efficiency by refining local road sub-networks. The bi-level optimization control method is developed to reconfigure the aggregated network from both supply and demand sides with contraflow and conflict elimination. The main purpose of this control method is to make the arterial sub-network to be served without congestion and interruption. Then, a corresponding bi-objective network flow model is presented in a static manner for an oversaturated network, and a Genetic Algorithm-based solution method is used to solve the evacuation problem. The numerical results from optimizing a city-scale evacuation network for a super typhoon justify the validity and usefulness of the network aggregation and optimization control methods.     

基于蚁群算法和群决策理论的动态路径优化研究

文章针对城市交通信号控制中的动态路径优化问题,综合考虑相邻交叉口间距和路段交通饱和度两个参数的影响,提出了一种基于蚁群算法和群决策理论的动态路径优化算法模型,并通过仿真实验,对比分析了该算法模型的有效性。     

基于遗传算法的舰船航速模糊控制研究

研究并建立了舰船航速控制系统的数学模型,并将模糊控制理论应用到该系统,提出了一种基于遗传算法的舰船航速模糊控制新方法。该方法较常规的模糊控制具有更优的控制性能,仿真实验结果表明该方法可行、有效。