TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

基于融合时空数据的车辆加油行为多视图深度异常检测框架

车辆加油时空数据多源异构、关系复杂,现有成熟的异常检测方法难以对时空离散的加油活动数据进行分析,因此提出基于融合时空数据的车辆加油行为多视图深度异常检测框架。首先基于统一概念模型（UCM）对静态信息和动态活动数据进行关联融合管理,然后从空间视图、时间视图和语义视图角度对时空数据进行编码和转换,最后基于三种视图构建深度时空异常分析检测框架。车辆加油时空数据集上的实验结果表明,多种异常检测方法在融合时空数据上均可取得更低均方根误差（RMSE）,平均降低10.73%,所提方法比现有主流方法中结果最好的长短时记忆网络（LSTM）的RMSE降低19.36%。在信用卡欺诈公开数据集上的实验结果表明,所提方法较之逻辑回归模型,马修斯系数（MCC）提高了32.78%。以上实验验证了所提方法的有效性。     

基于压电和线圈的交通参数检测系统设计

根据ITS对交通参数采集的需求,提出一种交通参数检测系统的设计方案。详细介绍了利用环形线圈和压电传感器进行交通参数采集的原理和计算方法。设计了用DSP和单片机实现的交通参数采集系统。该系统可以同时实现车型分类和动态称重。     

基于监控视频的运动车辆检测优化方法

为满足实时性处理需要,提出了一种基于监控视频的运动车辆检测优化方法.运用自适应ROI(region of interest)提取算法,在获取可能出现运动车辆的区域后,基于帧间差分法与分块处理思想,提出了一种改进的背景提取算法,有效地提取运动目标区城.对提取的多目标运动区域进行分离,分别提取可能是车辆的区域后,提出了一种简单、快捷的阴影去除算法,有效地去除阴影,获得准确的运动目标区城.实验结果表明,该方法速度快、准确率高,能很好地满足实时性要求.     

Intelligent traffic light under fog computing platform in data control of real-time traffic flow

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security.

     

车载光电测量平台晃动监测数据分析

车载环境下由于加速度计自身和外界环境干扰等因素的影响,真实的加速度信号叠加了大量干扰信号。针对加速度计信号特点,采用小波阈值去噪对加速度计信号进行了滤波处理。建立一个振动信号模型,将真实的加速度计的输出噪声作为干扰成分叠加到该模型上,选择较优的小波参数,对仿真信号进行小波阈值去噪,去噪后信噪比（SNR）由4．24dB提高到20．45dB,均方根误差（RMSE）由0．051改善到0．0081。据此对真实加速度计输出信号进行去噪处理,实验结果表明：小波阈值去噪对加速度信号具有良好的滤波效果。     

轨道交通站台安全门控制系统

轨道交通站台安全门作为公共安全防护设备,广泛应用在地铁、轻轨等轨道交通中的高架和地面站台上,具有节能、环保和安全功能。因此它在设计、制造、安装过程中需要充分考虑其自身的可靠性及乘客安全、行车安全。本文将对安全门系统的网络、控制及软件三方面进行详细论述。     

智能交通系统综合信息平台的设计

在原各职能部门的信息系统基础上,为充分利用日益增长的海量数据,并针对原信息系统因信息分散而在宏观分析方面的不足,提出了一个基于数据仓库、联机分析处理和数据挖掘3项技术的智能交通系统综合信息平台.讨论了平台的总体结构,数据的预处理方法,对该平台的数据仓库、多维数据集扣数据挖掘模型进行了设计与研究,并对实现的关键技术和方法做了简要的说明.经测试,该平台是实用和可靠的.     

嵌入式平台下的车辆跟踪系统设计

设计并实现了NVIDIA嵌入式平台Jetson TX2上的车辆跟踪系统。从摄像头采集YUV420格式的视频数据,然后将数据送到Tegra Parker硬件HEVC编码器进行编码,输出码流经过RTP封装后通过UDP广播发送,利用Gstreamer多媒体框架开发接收及解码程序,最后,针对获取的视频动态进行车辆的跟踪与显示。运行Yolo V2检测算法,对车辆进行检测,从而为跟踪系统提供跟踪对象。利用Kalman滤波算法对车辆的位置进行预测,再经过Meanshift算法进行车辆跟踪。系统能够实现帧率为60 f/s的超高清4K视频实时编码和传输,此系统中的HEVC硬件编码器编码速率比PC端x265编码器大3个数量级,PSNR比PC端x265编码器高6 dB,更加适用于智能交通中。     

基于Spark平台的心电大数据分析处理

为有效解决互联网医疗时代海量心电数据的处理问题,在Spark云平台下,提出一种双层并行化的改进遗传K-means聚类算法,用于心电数据挖掘。克服传统K-means算法对初始中心点敏感以及串行聚类算法效率低下等问题,结合Mallat小波变换预处理技术,较好实现海量心电数据中R波的提取。通过对MIT-BIH数据库的读取和分析,其结果表明,该算法比传统遗传K-means算法具有更高的聚类准确度,与串行聚类算法和Map Reduce计算模型相比,运行效率也有了较大提升。     

Forward Observer system for radar data workflows: Big data management in the field

There are unique challenges in managing data collection and management from instruments in the field in general. These issues become extreme when “in the field” means “in a plane over the Antarctic”. In this paper we present the design and function of the Forward Observer a computer cluster and data analysis system that flies in a plane in the Arctic and Antarctic to collect, analyze in real time, and store Synthetic Aperture Radar (SAR) data. SAR is used to analyze the thickness and structure of polar ice sheets. We also discuss the processing of data once it is returned to the continental US and made available via data grids. The needs for in-flight data analysis and storage in the Antarctic and Arctic are highly unusual, and we have developed a novel system to meet those needs.We describe the constraints and requirements that led to the creation of this system and the general functionality which it applies to any instrument. We discuss the main means for handling replication and creating checksum information to ensure that data collected in polar regions are returned safely to mainland US for analysis. So far, not a single byte of data collected in the field has failed to make it home to the US for analysis (although many particular data storage devices have failed or been damaged due to the challenges of the extreme environments in which this system is used).While the Forward Observer system is developed for the extreme situation of data management in the field in the Antarctic, the technology and solutions we have developed are applicable and potentially usable in many situations where researchers wish to do real time data management in the field in areas that are constrained in terms of electrical supply.     

车辆检测中一种兴趣区域提取方法

在基于视频图像方法的车辆检测系统中,兴趣区域的提取是至关重要的.提出了一种新颖的方法来检测图像中的兴趣区域,为后续处理提供有效的特征信息.该方法的核心思想就是根据兴趣函数抽取图像中的兴趣点;然后按照一定的策略将这些兴趣点进行合并处理组成兴趣区域,最后根据阈值选取合适的区域作为最后的兴趣区域检测结果.给出了相关实验结果和分析,结果表明该方法是有效可行的.     

车辆性能参数智能检测系统

研制了一套车辆性能参数检测系统。介绍了系统的组成及功能、软件设计、检测流程控制和试验结果等。该系统以单片机为核心,实现了车辆检测过程的自动化控制和各种参数的智能化检测。     

汽车记录仪数据采集系统的设计

从硬软件的角度研究了基于 S3C2410的汽车记录仪数据采集系统的设计,对采集系统中三大主要模块即:实时时钟信号模块、车速采集模块、开关量模块分别进行了研讨,经过实验证明,将此采集系统应用于汽车记录仪中,该采集系统采集精度高,抗干扰性强,性能可靠,价格低廉,具有很好的应用前景和较高的通用性与实用性。     

Intrusion detection system over real-time data traffic using machine learning methods with feature selection approaches

International Journal of Information Security - The intrusion detection system (IDS) plays an important role in extracting and analysing the network traffics to detect aberrant activity. However,...     

基于交通网格的车载导航系统在eBoxII上的实现

对eBoxII用做车载导航系统的可行性进行了研究,实现了基于嵌入式开发设备eBoxII的车载导航系统.完成了系统中语音数据和海量地图数据的存储、地图显示的优化,语音同步播报、后台通信服务等关键技术.该车载终端为上海交通信息网格3.0版的移动导航终端,由后台交通网格系统提供支撑服务.大量实验数据表明,该车载终端具有较高的运行效率.     

LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data

As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD.     

基于集对分析的网络流量大数据均衡调度方法

为了有效降低网络流量传输时的网络负载,基于集对分析策略,提出DNS网络流量大数据均衡调度方法。设计Dramp流量路由控制方法,根据链路权值量化,提出优化函数,实现多路径网络流量的传输,在静态域名解析(DNS)的环境下,重新构建网络链路,设计了一个包括网络流量负载信息和网络信息实体输入信息,且序列一体的传输链路,用于配合路由控制,最后基于集对分析思想,根据定向控制器分析信息,保证路由和链路的匹配,实现网络流量的均衡调度。实验数据表明,与传统方法相比,设计的DNS网络流量大数据均衡调度方法,网络时延降低了21%,丢包率降低了27%,可以有效降低网络负载。     

汽车电动空调的数据采集系统的研究

针对汽车电动空调压缩机的相关参数的采集,本文提出了基于Measurement Studio和CAN总线的软硬件采集系统,该系统由DSP TMS320F2812,CAN232接口卡,以及PC机组成.给出本系统的硬件结构及软件流程.介绍了Measurement Studio软件的功能和特点,运用Visual c++和Measurement Studio开发本数据采集系统.实验结果表明,可实现本数据采集系统之功能.     

Real-time validation of vision-based over-height vehicle detection system

Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%.