J2EE架构下分层结构的研究和中间件的设计

J2EE是一种利用Java 2平台来简化多级企业解决方案的开发、部署和管理的体系结构.对实际应用的J2EE分层结构进行了详细分析,给出了一种J2EE架构下的多层应用模式,并详细介绍了该模式下的具体开发方案,重点论述了中间件的设计对于系统开发的意义.     

基于Spring与Hibernate的J2EE持久层解决方案

通过Spring与Hibernate的整合,探讨如何有效地应用Spring的核心技术IOC和AOP实现J2EE应用的持久层解决方案.并从实用角度阐明用Spring IOC动态注入Hibernate SessionFactory和数据源,用Spring DAO简化Hibernate的持久化操作;用Spring AOP实现声明式事务管理.     

医学图像自适应阈值边缘检测的研究

介绍一种基于小波变换的自适应医学图像边缘检测方法.实验结果表明:该方法可对图像边界进行敏锐检测,从而得到图像的细微特征.这些特点使得它特别适合于医学图像边缘的检测.     

计算机联锁系统二乘二取二容错结构分析

针对计算机联锁系统的二乘二取二容错结构,基于马尔可夫模型,将各单元的失效率划分为几个独立的分量,根据每部分失效率对结构的影响,利用计算机对系统的可靠性和安全性进行分析和仿真计算.结果表明,二乘二取二容错结构满足铁路联锁系统高可靠性和高安全性要求.     

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.