多尺度图像边缘检测技术在车辆识别中的应用

指出了图像边缘检测的定义及现有算法的不足,并对Canny边缘检测离散准则及该准则下的最优线性滤波器进行了论述。根据边缘检测离散准则,利用数值方法求出该准则下边缘检测的最优线性滤波器及对应的平滑算子,将其与Canny边缘检测技术相结合进行边缘检测,检测出不同尺度下多幅边缘图像,并根据小尺度下图像边缘细节信息丰富、边缘定位精度高,大尺度下图像边缘稳定、抗噪性好等特点,将检测到的多尺度边缘进行融合,得到抗噪性好的精确的单像素宽边缘;同时将该方法应用于汽车特征提取,检测到完整连续的汽车边缘图像,为车型识别提供条件。试验结果表明:该方法不仅能准确检测出图像边缘,而且能有效地抑制噪声,是边缘检测的实用方法。     

COE段开发技术

COE是软件即插即用的开放标准，在COE中将软件包装成段可以获得相当大的灵活性。段开发技术是COE中的关键技术，包括段注册，开发，提交，集成，安装几个阶段。成功的应用COE，将能达到C^4I系统的互联、互通、互操作。     

对舰载信息系统数据融合攻击分析

针对对舰载信息系统的攻击，分析了系统的易损性，提出对数据融合系统的攻击类型，给出了攻击矩阵，并对攻击效果作了简单定性分析。     

视频图像压缩编码标准分析

模拟视频数字化后,数据量巨大。在对其进行实时处理时,对存储容量、传输带宽及计算机的处理能力提出了不切实际的要求,从而使数字化成为不可能。因此,可采用数据压缩技术,将那些对人眼感知图像不太重要的东西和图像中的冗余成分抛弃,从而缩减存储、传输和处理的数据量。这样就出现一系列的视频压缩编码标准。就视频压缩标准,以及图像压缩中所采用的相关技术进行介绍,并重点对MPEG4的原理进行讲解。     

智能新能源船舶的概念及关键技术

绿色和智能是当前船舶技术的发展趋势。文章首先提出了智能新能源船舶的概念,并介绍了智能新能源船舶的内涵、特点和系统构成;然后,从船舶航行、动力和推进3个方面分析了智能新能源船舶的关键技术;最后,对智能新能源船舶的未来发展进行展望。研究结果可为船舶的创新研究提供技术支持,引领船舶技术转型升级的发展方向。     

环氧沥青洒布车的研制与应用

文章基于环氧沥青洒布车研制历程,针对环氧沥青桥面防水粘结层施工的特殊性,介绍了环氧沥青洒布车的功能设计、结构特点及工作原理,分析了其技术优越性,为桥面防水粘结层施工机械的选用提供参考。     

军队信息化建设关键技术及发展趋势

分析了现代军队发展的三个特点，重点论述了分布协同作战技术，人性化系统技术，军用智能系统，军用决策支持系统，军用软件工程，信息战技术等军队信息化建设关键技术及发展趋势。     

CKD4C型电传动内燃机车

CKD4c型电传动内燃机车用于委内瑞拉国家铁路自治局货物运输业务。介绍了该型机车的主要技术特点．总体布置、技术参数等。CKD4c型机车性能满足用户的要求，综合体现了“先进、成熟、经济、适用、可靠”的技术原则。     

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.