动态交通流量预测方法研究

随着智能运输系统的广泛应用,实时交通流量预测的重要性也日益显著。本文介绍了预测模型发展过程中比较重要的几个模型,并由此引出人工神经网络。介绍误差逆传播(BP)模型的相关理论。指出传统BP神经网络的缺陷,并提出提高预测精度的措施引进高阶神经网络。建立普通BP神经网络的预测模型,利用误差反传播算法实现这些影响因素到输出变量的复杂映射,再用高阶神经网络构建另一预测模型。利用交叉口实测数据进行预测,并用实际数据进行比较验证。     

小流量下短时交通量预测最佳窗口长度与时间间隔

探讨了小流量情况下，路段短时交通量预测中的可变时间间隔及预测窗口长度对预测精度的影响，分析了不同间隔序列在反映交通流特性方面的差别和最适合的预测窗口长度，并建立神经网络模型对预测效果进行了定量比较，得到序列用于预测的最佳窗口长度和一组最优时间间隔，有助于预测算法的改进和预测精度的提高。     

Sensing of chlorpheniramine in pharmaceutical applications by sequential injector coupled with potentiometer

This paper reports on development of a system consisting of a portable sequential injector coupled with potentiometric unit for sensing of chlorpheniramine （CPA）, based on the reaction of CPA with pota...     

Data Fusion-Based Traffic Density Estimation and Prediction

Traffic congestion has become a major challenge in recent years in many countries of the world. One way to alleviate congestion is to manage the traffic efficiently by applying intelligent transportation systems (ITS). One set of ITS technologies helps in diverting vehicles from congested parts of the network to alternate routes having less congestion. Congestion is often measured by traffic density, which is the number of vehicles per unit stretch of the roadway. Density, being a spatial characteristic, is difficult to measure in the field. Also, the general approach of estimating density from location-based measures may not capture the spatial variation in density. To capture the spatial variation better, density can be estimated using both location-based and spatial data sources using a data fusion approach. The present study uses a Kalman filter to fuse spatial and location-based data for the estimation of traffic density. Subsequently, the estimated data are utilized for predicting density to future time intervals using a time-series regression model. The models were estimated and validated using both field and simulated data. Both estimation and prediction models performed well, despite the challenges arising from heterogeneous traffic flow conditions prevalent in India.     

Variations of the abundance and nucleic acid content of heterotrophic bacteria in Beaufort Shelf waters during winter and spring

Depth profiles of heterotrophic bacteria abundance were measured weekly over a 6-month period from December to May in Franklin Bay, a 230 m-deep coastal Arctic Ocean site of the southeastern Beaufort Sea. Total bacteria, low nucleic acid (LNA) and high nucleic acid (HNA) bacteria abundances were measured using flow cytometry after SYBR Green I staining. The HNA bacteria abundance in surface waters started to increase 5–6 weeks after phytoplankton growth resumed in spring, increasing from 1 × 10⁵ to 3 × 10⁵ cells mL^− 1 over an 8-week period, with a net growth rate of 0.018 d^− 1. LNA bacteria response was delayed by more than two months relative to the beginning of the phytoplankton biomass accumulation and had a lower net growth rate of 0.013 d^− 1. The marked increase in bacterial abundance occurred before any significant increase in organic matter input from river discharge (as indicated by the unchanged surface water salinity and DOC concentrations), and in the absence of water temperature increase. The abundance of bacteria below the halocline was relatively high until January (up to 5 × 10⁵ cells mL^− 1) but then decreased to values close to 2 × 10⁵ cells mL^− 1. The three-fold bacterial abundance increase observed in surface waters in spring was mostly due to HNA bacteria, supporting the idea that these cells are the most active.     

公路隧道交通量的预测及分析

公路隧道交通量的预测对隧道通风系统的节能以及降低隧道运营成本有很重要的意义。分别利用多元统计分析法和BP神经网络两种方法对公路隧道交通量进行了预测,并对两类预测数据进行分析比较,得出了多元统计分析法适用于车流量少而且稳定的公路隧道的预测,而BP神经网络法则适用于车流量大而且不稳定的公路隧道的预测的结论。     

新型救生装置流场性能数值模拟研究

海洋事故的频发和传统的救生装置存在不能进行及时救援和易受环境影响的缺陷，使得救援十分困难，因此新型救生装置应运而生。本文通过对三体叉式和整体式两种不同线型的新型救生装置进行阻力性能的数值仿真，分析其在空载、搭载儿童、搭载成人和极限四种载重工况下不同速度（0.5m/s，1m/s，1.5m/s，2m/s）时的流场性能，对比两种救生装置的阻力性能，得出相较于A型三体叉式救生装置，B型整体式救生装置的侧体对装置的阻力性能影响更大，且搭载重量越大，其阻力先增后减；救生装置表面高压的分布区域会随速度变大而发生变化，由前体向侧体后移，高低压差是影响阻力大小的关键因素。本文的研究对新型救生装置的优化提供了一定的数据支撑。     

On a mean field game approach modeling congestion and aversion in pedestrian crowds

In this paper we present a new class of pedestrian crowd models based on the mean field games theory introduced by Lasry and Lions in 2006. This macroscopic approach is based on a microscopic model, that considers smart pedestrians who rationally interact and anticipate the future. This leads to a forward-backward structure in time. We focus on two-population interactions and validate the modeling with simple examples. Two complementary classes of problems are addressed, namely the case of crowd aversion and the one of congestion. In both cases we describe the model and present numerical solvers (based on the optimization formulation and the partial differential equations respectively). Finally we present numerical tests involving anticipation phenomena and complex group behaviors such as lane formation.     

基于VOF界面追踪的气缸流场分析

发动机作为汽车的动力来源,研究发动机的混合气混合特性对于提高汽车的使用性能具有至关重要的作用。文章通过基于VOF界面追踪的算法,得到了进气过程中可燃混合气流动过程的流体相图、缸内压力和速度云图,帮助理解了气缸内流动、涡旋、喷雾过程以及混合气形成过程。     

液压转向系统中转阀的研究

本文主要研究了液压转向系统中流量转阀的稳态特性,设计了测试实验台,验证了该阀的理论模型,分析确定了阀的压力灵敏度,并通过测试某品牌汽车转向系统的流量转阀获得实验过程和结果,可以供厂商借鉴使用。