Exploring the Role of Transportation in Fostering Social Exclusion: The Use of GIS to Support Qualitative Data

Travel behavior data collection methodologies have not captured the why of demand nor unsatisfied demand. This has left a gap in the development of methodology that can adequately address the role that transport plays in fostering social exclusion. This paper presents one innovative technique that utilizes GIS to organize and analyze data taken from focus groups and the self-mapping of individual space. Implications for transportation planning include redefining how networks are conceived to include not just road and bus networks, but also the spatio-temporal networks constructed by low-income people as they organize their activities.     

基于随机森林回归的清防垢加药量预测方法

复合驱由于强碱化学剂的注入易造成严重的结垢现象,而预测清防垢加药量是制定科学的清防垢方案的关键.为了获得一种具有较强实用价值和较高预测精度的复合驱清防垢加药量的预测方法,该文将随机森林回归算法应用到复合驱清防垢加药量预测.首先,分析并处理影响清防垢加药量的20维特征,利用随机森林回归的特征重要性评估功能对影响加药量的众...     

基于MCGS的货物分拣系统监控方案设计与实现

设计了一套货物分拣系统监控方案,用以实现对TVT-METS-3货物分拣系统的在线监控,完成对货物的分拣、加工、装配,以及出入库管理。用MCGS工控组态软件编写控制程序。实际运行结果表明,该系统运行效果良好,人机界面友好,达到了预期效果。     

基于微博扩展的用户兴趣主题挖掘算法

为解决基于微博的用户兴趣建模存在建模不全面的问题,提出一种基于微博扩展的用户兴趣主题挖掘算法。通过结合用户自身兴趣及用户关注人的兴趣来扩充微博用户兴趣,将兴趣分为长期兴趣、过期兴趣、近期兴趣,利用改进的TF． IDF算法分析相关微博内容,利用基于时间及文档频率加权的主题词重要度计算兴趣主题词得分,得出综合全面的用户兴趣。实验结果表明,综合分析用户及用户关注人的兴趣,可以有效得到真实、全面、包含潜在兴趣的用户个人兴趣。     

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face. Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’ identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session. However, divergent issues remain unaddressed. This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s (GWCK-CC). First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images. Cauchy Kriging Regression function is employed to reduce the dimensionality. Finally, Continuous Czekanowski’s Classification is utilized for proficient classification between the genuine user and attacker. By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time. Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset. The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods.     

双向通信无人机集群领航顶点选取方法

领航顶点选取是关系到领航-跟随模式无人机集群等多智能体系统可控性的重要问题.以具有几十架个体的无人机集群为研究对象,针对领航顶点选取问题,基于Laplace矩阵的特征向量,提出关键集概念.从理论上证明2关键集、3关键集以及独立关键集的图特征.在此基础上,给出求无向网络最小关键集的CSA算法,通过数值仿真实验获得了不同规...     

美国城市公共安全应急体系建设方法研究

全面紧急事态管理是美国公共安全管理中最基本的原则,包括全危险方法、综合紧急事态管理系统、紧急事态管理的生命周期、对各级政府及组织紧急救援资源的统一调配.美国国土安全部确定了了解、防止、保护、应对、恢复、服务和组织品质七大安全战略目标.美国联邦紧急事务管理局除制订全国防灾计划外,还制订可持续减灾计划和应急反应计划.社区应急反应小组是美国重要的市民组织队伍.     

基于层次分析法的城市反空袭防护目标选择

通过对防护目标的选定因素分析,城市反空袭防护目标指标体系包括目标层、准则层及措施层.利用层次分析法(AHP)法选定城市反空袭中的防护目标,形成数据表、建立并求解判断矩阵及一致性检验,以获得选择最优保护目标的排序权重.经实际运用,所建立的指标体系和方法更有利于合理、科学地选择防护目标.     

融合可变形卷积网络的鱼眼图像目标检测

环视鱼眼图像具有目标形变大和图像失真的缺点,导致传统网络结构在对鱼眼图像进行目标检测时效果不佳.为解决环视鱼眼图像中由于目标几何畸变而导致的目标检测难度大的问题,提出一种基于可变形卷积网络的鱼眼图像目标检测方法.将Cascade_RCNN中固定的卷积层和池化层分别替换为可变形卷积层和可变形池化层,使用Resnet50网...     

Fisher’s decision tree

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way.The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method.