基于P2P技术的信息获取方案研究

当前互联网的体系结构是客户/服务器模式,服务器集中了所有功能和资源,因此负荷很重,维护其上的信息检索越来越困难.对等网(P2P)技术可以解决这个问题.P2P网络是一种分布式系统结构,它将互联网的集中管理模式引向分散管理模式,将内容从中央单一节点引向网络的边缘,从而充分利用互联网中众多终端节点所蕴涵的处理能力和潜在资源.但是已有的P2P检索机制并不成功,分析指出,主要是缺少为web设计的、定义严密的、基本的数据模型,即信息定义和结构的质量很差.因此提出了一个基于P2P技术的信息获取方案-ICover,它建立在P2P网络上,提供信息建模、信息发布、信息网关、虚拟组织、信息端到端加密等关键部分,从而有效完成信息获取和共享.     

Linux环境下基于策略路由实现多线路负载均衡

指出IP传统路由的不足,对Linux的策略路由进行了介绍,提出应用Linux操作系统上的IPROUTE2实现IP策略路由的方法.给出有效解决多出口网络的问题的一个实例,介绍如何对网络进行流量分割来实现负载均衡,并利用策略路由进行一定的访问控制.     

WebLogic和.NET协同开发Web Service中的安全问题解决方法

在实际项目开发过程中,结合.NET瘦客户端的小巧性和J2EE技术在安全性及事务控制上的优点,选择用Web Service技术进行.NET的瘦客户端和基于J2EE标准的WebLogic的服务器端的协同开发.将阐述的是上述开发过程中所遇到的WebLogic和.NET协同开发Web Service过程中的安全问题解决方法.     

An on-road evaluation of connected motorcycle crash warning interface with different motorcycle types

Crash warning systems have been deployed in the high-end vehicle market segment for some time and are trickling down to additional motor vehicle industry segments each year. The motorcycle segment, however, has no deployed crash warning system to date. With the active development of next generation crash warning systems based on connected vehicle technologies, this study explored possible interface designs for motorcycle crash warning systems and evaluated their rider acceptance and effectiveness in a connected vehicle context. Four prototype warning interface displays covering three warning mode alternatives (auditory, visual, and haptic) were designed and developed for motorcycles. They were tested on-road with three connected vehicle safety applications - intersection movement assist, forward collision warning, and lane departure warning - which were selected according to the most impactful crash types identified for motorcycles. Combined auditory and haptic displays showed considerable promise for implementation. Auditory display is easily implemented given the adoption rate of in-helmet auditory systems. Its weakness of presenting directional information in this study may be remedied by using simple speech or with the help of haptic design, which performed well at providing such information and was also found to be attractive to riders. The findings revealed both opportunities and challenges of visual displays for motorcycle crash warning systems. More importantly, differences among riders of three major motorcycle types (cruiser, sport, and touring) in terms of rider acceptance of a motorcycle crash warning system were revealed. Based on the results, recommendations were provided for an appropriate crash warning interface design for motorcycles and riders in a connected vehicle environment.     

Road traffic conditions in Kenya: Exploring the policies and traffic cultures from unstructured user-generated data using NLP

Road traffic accidents (RTA) are a prevalent cause of fatality with African countries having the highest fatality index (25–34 per quota). The World Health Organization estimates Kenya's fatality rate due to RTA at 28 per quota. From literature, the country's fatality and injuries have increased by 26% and 46.5%, respectively, since the year 2015. The country is faced with incomplete RTA data capturing, hindering effective planning and policy adjustments to curb the menace. In this paper, we scrapped user-generated data (Twitter) and national transport and safety authority's (NTSA) reports to shed light on traffic safety, practices, and cultures in the country. To this end, we gathered 1,000,000 tweets and 8000 speeding entries between 2015 and 2021 and performed natural language processing (NLP) and quantitative study of the data. We applied NLP and n-gram search of keywords to categorize data into 8 topics: traffic, public service vehicle (PSVs), policing, accident, infrastructure, recklessness, robbery, and corruption. From the data, policing, which touches on all police and law-enforcement-related activity was found to be highly correlated with PSVs, recklessness, accidents, traffic congestion, robbery, infrastructure, and corruption with indices of r(76) = 0.92, 0. 91, 0.87, 0.82, 0.81, 0.76, and 0.70, respectively with p < 0.001. The topic modeling confirmed the identified topics to be the latent discussion issues affecting the public. From the study, PSVs, policing and traffic flow were isolated as key issues that ought to be addressed immediately. The research recommended the integration of driver monitoring systems to strengthen policing. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy.     

Study on the framework of hybrid collision warning system using loop detectors and vehicle information

Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

一种铁路信号安全控制平台的研究

以铁路信号控制系统中核心的安全控制平台为研究对象,立足于铁路信号计算机安全控制平台的功能及安全需求,借鉴融合失效-安全原则、冗余控制原理、计算机通信总线及编程技术,从总体设计原则、软/硬件结构设计及实现等方面研究设计一种铁路信号安全控制平台。信号安全控制平台测试、验证和确认结果表明,信号安全控制平台适应并满足新时期各种铁路信号安全控制系统兼容性、安全完整性要求,研究设计的信号安全控制平台能有效解决适配各种信号安全控制系统的通用性、平台软件与应用软件分离后的移植性等关键技术问题。     

嵌入式仿真测试系统设计与实现

根据嵌入式系统的特点，围绕ADS-2系统，提出了用ADS-2构成嵌入式仿真测试系统的技术框架，阐述了嵌入式仿真测试系统设计和测试方法。     

基于AutoSEA2的声呐自噪声水动力分量分析

利用基于统计能量分析法的声仿真软件AutoSEA2分析湍流边界层激励下水下航行器声呐腔自噪声水动力分量。采用一种新的回转体模型模拟声呐罩,重点讨论了空间分布不均匀的湍流边界层对声呐罩的输入功率的计算。利用Fluent软件计算边界层的分离点及一些重要参数。分析结果可作为空间不均匀湍流边界层激励下声呐腔自噪声工程估算的参考。