模拟退火粒子群算法在新交通控制模型中的应用

城市交通系统是个随机性很强、复杂的巨型系统,为了获得良好的通行效率,提出了一种基于模拟退火温度的自适应粒子群优化算法,同时给出了一种城市区域交通协调控制信号配时模型,然后将提出的方法应用于此模型。仿真结果表明：这种算法不仅能够克服基本粒子群算法陷入局部寻优的缺点,而且算法的收敛性和稳定性都很好,同时也表明该模型是可行的、有效的。     

基于遥感和GIS的西安市土地利用时空变化研究

利用2000和2007年两期TM遥感影像,通过最大似然法、地理信息系统空间分析方法以及数理统计方法,借助Erdas Imagine 9.0、ArcGIS 9.2和Matlab软件平台,计算出西安市土地利用类型的动态转移矩阵;构建了土地利用程度变化指数、土地利用变化类型的多度和重要度指数等区域土地利用变化的指数模型,定量分析西安市土地利用时空变化。揭示了西安市土地利用差异以及土地利用空间变化的主要类型、分布特征和区域方向,阐明了西安市土地利用变化的区域特点,为土地可持续利用提供有效的决策支持。     

ICBF：汽车Ad-Hoc网络中一种改进的CBF算法

汽车Ad-Hoc网络是一种特殊的Ad-Hoc网络,其路由层面临着许多挑战。本文提出汽车Ad-Hoc网络中一种改进的CBF算法--ICBF。通过速度、方向和距离三个重要的因素控制定时器,更准确地选择下一跳转发节点;利用城市场景下汽车与相邻汽车之间速度差较小的特点,减少了数据包的广播;提出一种新的锚点路径选择算法,使得ICBF协议能够主动地感知地理拓扑和交通密度,从而达到优化路由性能的目的。实验表明,在城市场景下,ICBF算法能提高数据转发成功率,减少网络的带宽负荷。     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

贪心与回溯算法在城市马拉松路线规划中的实践

针对目前城市马拉松路线人工规划效率低下的问题, 本文采用贪心和回溯算法进行城市马拉松路线智能规划, 具体方法是: 通过城市路网信息构建由经纬度坐标点拓扑关系连接而成的路网, 采用贪心和回溯算法对坐标点进行遍历搜索, 结合城市马拉松路线特殊要求, 运用直接逼近、启发式远离、启发式靠近和方向预估等策略实现路线的智能规划. 在此基础上, 提出一种综合POI热度值、道路宽度适宜度、路线畅通指数、过弯舒适度以及POI密集度5个维度的马拉松路线评估方法. 最后, 开展了北京、合肥马拉松人工和智能规划路线对比分析, 结果表明所采用的方法可快速高效实现马拉松路线规划.     

沿海城市暴雨-高潮位复合致涝区划研究北大核心CSCD

由于暴雨和高潮位的共同影响,沿海城市更容易遭受内涝灾害。暴雨和高潮位影响区域致灾因子划分是减灾措施布设的重要因素。本文提出了致灾因子区划方法,并进行了区划方法的效果分析。以海口市主城区为例,基于城市暴雨污水及流域雨洪管理建模软件构建一二维耦合城市内涝模型,计算不同组合工况下的淹没水深和淹没范围,将内涝区域划分为降雨影响区、潮汐影响区和共同影响区,并对三个区域进行了减灾措施优化分析。结果表明,潮汐影响区、共同影响区和降雨影响区距海岸线逐渐变远;降雨影响区布设蓄水池的效益要明显优于潮汐影响区,而潮汐影响区布设止回阀的效益要优于降雨影响区。因此,通过内涝区域划分可以更高效地布设防涝措施。     

宁夏城市形象宣传招贴设计应用研究

目的研究以《印象·宁夏》为主题的宁夏城市形象宣传招贴设计。方法分析国内关于城市形象宣传设计的发展现状,以《印象·宁夏》为主题案例分析城市形象招贴的设计方法。结论城市形象宣传招贴的设计应从城市的形象定位、城市文化的提炼、城市形象招贴的创意思路3个方面入手,结合招贴设计的基本原则,将提炼出的信息符号进行视觉化的创意设计实践。     

低影响开发(LID)透水砖铺装系统的研究进展

透水砖铺装系统是现阶段我国海绵城市建设中重要的透水铺装系统技术之一。为了充分认识该领域已有的应用和研究成果,开展更深入的透水铺装系统技术探索和应用研究,对透水砖铺装系统的国内外应用和研究现状进行了分析总结。简述了透水铺装材料的分类和特点,分析了国内外应用情况,分别对混凝土透水砖、陶瓷透水砖、砂基透水砖的国内外研究现状进行了评述。最后指出现阶段研究存在的问题,并提出了建议和展望。     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible, such as smart communications, smart grids, smart energy, and smart buildings. The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality, with energy resources taking precedence. To achieve optimal energy management in the multidimensional system of a city tribe, it is necessary not only to identify and study the vast majority of energy elements, but also to define their implicit interdependencies. This is because optimal energy management is required to reach this objective. The lighting index is an essential consideration when evaluating the comfort indicators. In order to realize the concept of a smart city, the primary objective of this research is to create a system for managing and monitoring the lighting index. It is possible to identify two distinct phases within the intelligent system. Once data collection concludes, the monitoring system will be activated. In the second step, the operation of the control system is analyzed and its effect on the performance of the numerical model is determined. This evaluation is based on the proposed methodology. The optimized results were deemed satisfactory because they maintained the brightness index value (79%) while consuming less energy. The intelligent implementation system generated satisfactory outcomes, which were observed 1.75 times on average.