Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Analysis of Cognitive Radio Enabled Flooding in Opportunistic Networks

Epidemic routing (Flooding) is considered as a simple routing protocol for opportunistic networks where the participants attempt to transmit whatever information they have to everyone who does not already have that information. However, it is plagued with disadvantages of resource scarcity as it exerts stress on available bandwidth as well as storage capacity of the devices in the network. Cognitive radio (CR) is one of the emerging technologies that can improve the bandwidth utilization by smart allocation of spectrum radio bands. Ideally speaking, a spectrum-aware cognitive radio is able to sense the local spectrum usage and adapt its own radio parameters accordingly. In this study, we have performed experiments to analyze the gains achieved by flooding protocol using cognitive radios of varying capabilities in opportunistic networks. We have performed experiments on three opportunistic networks obtained from real-life traces from different environments and presented results showing variance in delivery efficiency as well as cost incurred on those scenarios. Our results show that performance of flooding can be significantly improved using CRs in bandwidth-scarce environments;however, the improvement is not uniform with the increase in a number of available bands.

Analyzing History Quality for Routing Purposes in Opportunistic Network Using Max-Flow

Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

计算机网络实验课程教学改革研究

在计算机网络技术高速发展和大学生科学能力培养目标的新形势下,针对当前计算机网络实践教学存在的问题,提出“三位一体”的计算机网络实验课改革方案。从通信、协议和应用三个方面,构建计算机网络实验模块内容体系,综合运用现场实验、线上虚拟环境和虚拟仿真教学手段,充分激发学生的学习兴趣,有效发挥其学习专长,在教学设施投入受限的情况下,积极开展计算机网络实践教学。

潜在区域水下威胁评估仿真实验系统设计实现

为提升作战人员对水下战场态势的理解能力及其水下威胁评估分析水平,综合典型作战仿真实验评估方法特点,从信息输入、数据驱动及评估计算三方面分析了水下平台潜在区域威胁评估仿真实验需求和威胁评估实验方法选择依据,给出了由作战实验仿真控制、作战实验数据管理、作战实验数据评估以及海洋环境效应仿真等四部分组成的系统总体架构及分系统功能设计实现思路,并从评估方案生成与推演控制、信号级声学传感器环境效应仿真以及仿真实验可视化评估等方面总结了系统实现的关键仿真技术。通过系统设计工程化实现及迭代升级,对于探索水下平台任务评估保障机制、提高训练效益具有支撑作用。

基于街区小气候环境冬夏协同优化的城市绿地生态网络精准修复

越来越多城市绿地生态网络建设进入存量更新和结构性优化并存发展的阶段,生态安全优先基础上如何进一步提升城市绿地生态网络的多种生态系统服务功能是当前风景园林实践研究的重要议题。应对全球气候变化、极端天气多发导致的人居环境宜居性下降问题,目前基于气候适应的城市绿地生态网络规划关注促进自然通风以缓解夏季热岛效应,尚忽视了其冬季可能带来的负效应。从气候调节视角切入,探讨城市绿地生态网络在协同冬夏季小气候环境中的低效空间识别与精准修复技术方法。以江苏省昆山市密集城区的189个街区单元为研究对象,通过遥感反演的方法对其冬、夏季小气候环境进行分析,聚类获得7类小气候特征街区。之后,叠加现状绿地生态网络与计算流体力学模拟风环境结果,提取现状适于通风的生态廊道,从宽度、方向、类型等方面分析其潜在通风效能,耦合街区冬夏小气候环境特征,识别研判现状绿地生态网络在冬夏温湿环境调节上存在的低效问题,并提出冬夏协同的精准修复策略,为优化人居环境宜居性、提升城市绿地生态网络生态系统服务综合效能提供技术参考。

基于深度Q网络的海上环境智能路径规划

深入研究了融合航海优先级(NP)和优先级经验回放(PER)策略的深度Q网络(DQN)算法在海上环境智能路径规划问题上的应用。不同于传统路径规划算法,本优化算法能够自主探索并学习海上环境的规律,无需依赖人工构建的海洋环境全局信息。本研究开发了基于Gym框架的海上仿真环境,用以模拟和验证改进的DQN模型。该模型融合了航海优先级和优先级经验回放机制,通过调整学习过程中经验样本的利用频率,提升了算法对重要决策的学习效率。此外,引入新的奖赏函数,进一步增强了模型对路径规划问题的适应能力和稳定性。仿真实验结果证明,该模型在避免障碍物及寻找最佳路径方面相较于基准方法有显著提升,展现了一定的泛化性和优秀的稳定性。

作战模拟中基于不规则三角网的路径规划研究

针对分队级作战模拟的路径规划问题,该文采用了一种基于不规则三角网的路径规划方法,通过将作战地图上的面状区域进行三角网剖分,区分地貌、地物设置三角面的属性,以描述不同三角网之间的可通行性,并在此基础上利用A*算法寻路,可以快速实现路径规划。非路网的路径规划将使作战模拟中实体的行为更加逼真,达到了野战环境下运动规划的目的。

Particle dispersion modeling in ventilated room using artificial neural network

Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor environment.This study was performed for a stationary flow regime.The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement.Multilayer perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the outputs.Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach.The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model.The average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.

Network simulation task partition method in heterogeneous computing environment

To reduce the running time of network simulation in heterogeneous computing environment,a network simulation task partition method,named LBPHCE,is put forward.In this method,the network simulation task is partitioned in comprehensive consideration of the load balance of both routing computing simulation and packet forwarding simulation.First,through benchmark experiments,the computation ability and routing simulation ability of each simulation machine are measured in the heterogeneous computing environment.Second,based on the computation ability of each simulation machine,the network simulation task is initially partitioned to meet the load balance of packet forwarding simulation in the heterogeneous computing environment,and then according to the routing computation ability,the scale of each partition is fine-tuned to satisfy the balance of the routing computing simulation,meanwhile the load balance of packet forwarding simulation is guaranteed.Experiments based on PDNS indicate that,compared to traditional uniform partition method,the LBPHCE method can reduce the total simulation running time by 26.3%in average,and compared to the liner partition method,it can reduce the running time by 18.3%in average.

考虑能见度影响的公路隧道照明动态优化与智能控制

为解决不同能见度影响下公路隧道实际路面亮度变化过大以及由此引起的行车安全与能源虚耗问题,本文提出了一种能够改善公路隧道照明环境的动态优化与智能控制方法.首先,通过对不同时空条件下的公路隧道进行现场试验和数据分析,得到了隧道内能见度的变化规律;其次,在公路隧道传统照明设计的基础上考虑能见度对照明环境的影响,建立了基于隧道内能见度、交通量、车速、路面亮度和照明亮度的按需照明与动态优化模型;随后,以不同地区公路隧道的实测数据为样本,结合划分出的公路隧道典型照明场景和模糊径向基神经网络算法构建了公路隧道照明智能控制模型,最后,通过仿真实验验证了所构建模型的有效性,其结果表明,本文所提出的优化控制方法能够在保证隧道照明安全性的前提下兼顾节能性.

针对机会网络资源消耗攻击的路由设计和影响分析

为解决资源消耗攻击对能量受限机会网络造成的安全问题,本文首先通过对机会网络通信方式和资源消耗攻击原理进行分析,在机会网络仿真平台ONE下搭建资源消耗攻击模型;其次,在经典机会路由基础上提出一种引入节点和数据包管理机制的安全机会路由;最后,选取不同类别机会路由协议与提出的路由协议进行对比仿真,使用数据包投递率、网络开销和平均传输时延作为机会路由性能评价指标。实验表明,资源消耗攻击模型下,基于能量判断的机会路由性能受到的影响较大,而改进后的路由协议有效抵御了资源消耗攻击带来的影响,总体性能表现较好,保障了对数据包的投递能力。

基于VR的采摘机器人仿真试验研究

针对采摘机器人精度和识别能力较差的问题,基于VR技术对采摘机器人进行试验研究。采摘机器人主要由PC控制中心、虚拟现实系统、传感器系统、通信系统和执行器系统等组成。为了实现快速、精准采摘,采用虚拟现实环境建模。首先,确定真实环境和虚拟环境的相对位置,然后采用神经网络模型进行非特定模型的参数辨识,最终确定虚拟环境模型。为了验证该采摘机器人的性能,对其进行定位精度试验和采摘试验,结果表明:采摘机器人的定位精度较高,采摘效果良好,采摘过程中可成功避障。

面向对抗仿真的通信系统多粒度建模方法研究

为了在体系级对抗仿真中有效平衡通信系统建模的精细度和实时性的问题,提出一种面向对抗仿真的通信系统多粒度建模方法,构建了军事通信系统工程级、功能级和任务级模型。对于精细度要求较高的场景,利用专业电磁仿真软件离线解算通信设备的天线数据,并在仿真运行中在线插值从而实时得到准确的数据。对于仿真实体较多的应用场景,采用典型参数来简化描述天线的方向性。对于大规模的军事通信网络,模拟实体运动带来的通信覆盖范围变化情况。在建模过程中考虑了战场通信干扰、大气环境干扰以及地形通视性的计算情况,建立一套从天线解算、链路仿真到军事通信网络的多粒度模型,来支撑体系级对抗仿真。

智能网联环境下无信号控制交叉口可穿越间隙分析与预测

无信号交叉口环境下,可穿越间隙会随着车速、流量以及智能网联车辆渗透率的变化而波动。利用驾驶模拟器搭建无信号交叉口环境下的智能网联车辆混行场景,对不同交通流状态下驾驶员的驾驶行为、心理特性进行实验;选取速度、加速度、瞳孔直径以及瞳孔直径变化率作为驾驶员的生/心理指标,结合主成分分析和k-means聚类算法对其进行定性与定量分析;仿真结果表明:在不同交通流状态下,驾驶员的驾驶行为发生了明显的改变,可划分为三个等级:“无感无行为间隙”、“有感无行为间隙”、“有感有行为间隙”。实验结果为智能网联环境下无信号交叉口的控制方法提供了数据及理论基础。

机会网络模拟器ONE及其扩展研究

网络仿真是DTN网络研究的重要方法和手段。通过对ONE模拟器的开发背景、结构、功能及其应用的介绍,探讨与分析了基于Java环境的DTN网络模拟器ONE的扩展方法,进一步对ONE的不足之处进行了完善和改进,并通过仿真实验证实了改进的ONE对路由性能评价的影响。

作战仿真技术综述

作战系统是社会复杂巨系统,仿真可以在实验室研究与学习战争,是对军事学科发展和实验研究的补充。对作战仿真的几个主要共性技术问题进行了简要论述,包括:任务空间功能模型、综合自然环境建模、多分辨率建模、基于多智能体的计算机生成兵力建模、复杂大系统建模与仿真的VV&A、仿真支持环境、基于网格技术的网络中心战仿真等。在作战仿真研究过程中,应建立一个综合虚拟战场支撑环境,将作战仿真所有共用部分建成一个平台,在此基础上开发各用户为某种目的的仿真应用。

基于ONE平台的机会网络路由协议仿真分析

从传输成功率、平均传输延迟和路由开销比率三个路由性能指标入手,利用ONE仿真平台仿真并分析了不同网络环境因素对机会网络几种典型路由协议的影响,为不同机会网络环境下路由协议的选取提供依据.仿真结果表明:各路由协议性能差异明显,其中Spray and Wait和MaxProp算法在各种仿真环境下都具有较高的传输成功率,且Spray and Wait算法路由开销比率较低.

Wait and Spray:一种改进的机会网络路由算法

灾难场景是机会网络重要的应用场景之一,在灾难场景下能量成为稀缺资源,高效转发数据包的同时尽可能减少节点能量消耗成为路由算法追求的目标之一,为此提出了Waitand Spray路由算法,该算法将数据包的转发过程分为Wait和Spray两个阶段,在不同阶段采用不同的路由策略。该算法具有W、K、M参数,恰当的参数设置可控制Spray阶段的泛洪程度,大幅提高算法性能。仿真结果表明,该算法可在获得高传输成功率的同时,大幅降低路由开销,是一种适合在能量稀缺场景下应用的路由算法。