移动通信网中基于级串框架的网络流量性能分析

根据湍流尺度分析中的对数无限可分级串方法(LIDC),对从某移动运营商处获取的无线分组网络流量进行了多尺度行为分析,以GPRS为例,发现现网GPRS流量符合对数无限可分级串框架,并由此对无线分组流量数据进行了深入的特性分析. 结果表明,LIDC方法在分析无线分组网络流量的多尺度行为方面非常有效. 该方法不仅在全尺度范围内能刻画网络流量的尺度特性,而且能确定出某一尺度下的有效分析范围. 同时,经过该分析框架分析表明,无线分组网络流量中的尺度保持了幂律关系、单一尺度及尺度不变性等特点.     

基于三元组信息的网络流量检测点选取算法

为了有效地选取网络流量检测点,根据流守恒假设,提出一种网络流量检测点选取算法．该算法将网络流量检测点选取问题抽象为图的弱顶点覆盖问题,使用三元组信息标记网络节点,通过比较和替换节点的三元组信息并根据最后的三元组信息,完成网络流量检测点的选取．仿真结果表明,新算法不需要了解网络拓扑的全局信息,能动态地排除无法部署的网络节点,有效地解决了网络流量检测点的选取问题．     

某矿区变电站进线段防雷改造措施及仿真分析

介绍了某矿区110kV变电站线路防雷措施的基本情况,分析了该变电站35kV进线段遭受雷击的原因,即线路绝缘水平遭人为提高后与站内绝缘水平等级不匹配和进线段杆塔接地电阻过高,并提出了相应的采用加装绝缘子保护间隙配合避雷器进行差异性保护和采用树枝状接地装置的改造措施。ATPEMTP仿真分析结果验证了改造措施的可行性。     

供电企业信息作业风险评估研究

在深入研究事故致因理论、事故风险管理和供电企业生产作业风险管理的基础上,提出了供电企业信息作业风险评估的模型和方法。有助于供电企业建立一套基于风险管理的信息作业指导书,为信息运维人员提供安全作业操作规范,减少安全事故的发生。     

基于多通道Transformer的交通量预测方法

目前,我国高速公路拥堵程度居高不下,而交通流预测作为实现智能交通系统的重要一环,若能对其实现高精度的预测,那么将能够高效地管理交通,从而缓解拥堵。针对该问题,提出了一种考虑时空关联的多通道交通流预测方法（MCST-Transformer）。首先,将Transformer结构用于不同数据的内在规律提取,然后引入空间关联模块对不同数据间的关联特征进行挖掘,最后,借助通道注意力整合优化全局信息。采用广东省高速公路数据,实现了两小时内92个收费站的高精度流量预测。结果表明：MCST-Transformer优于传统机器学习方法以及部分基于注意力机制的时间序列模型,在120 min预测跨度下,相比贝叶斯回归,MAPE降低了5.1%;对比Seq2Seq-Att以及Seq2Seq这些深度学习算法,所提方法的总体MAPE也能降低0.5%,说明通过多通道的方式能够区分不同数据的特性,进而更好地预测。     

面向交通流量预测的多头注意力时空卷积图网络模型

为了充分获取交通流量数据中隐藏的复杂动态时空相关性,提高交通流量预测精度,提出一种多头注意力时空卷积图网络模型MASCGN。首先,采用多头注意力机制为路网中的交通传感器节点自动分配注意力权重,实现对不同邻居节点的权值自适应匹配,充分获取空间相关性;其次,采用带有门控和注意力机制的时空卷积网络充分提取时间序列相关性,并使用残差块结构实现时空卷积层之间的连接,使得模型更具有泛化能力;最后,分别提取周相关、日相关、邻近时间的序列数据,输入三个并行的时空组件以挖掘周、日、邻近三个时间窗口间的时间周期相关性,并通过全连接层获取最终的交通流量预测结果。利用高速公路交通数据集PEMSO4、PEMSO8进行了15 min、30 min、45 min和60 min的交通流量预测实验。实验结果表明MASCGN模型与现有基线模型相比,在未来短期和长期的交通流量预测任务上都具有更优的建模能力。     

微信流量特性分析与建模

微信是现代互联网的主要应用之一,到目前为止有关微信流量特性分析与建模的研究较少.本文以微信流量为研究对象,分析验证微信流量同时具有自相似性和突发性.针对这两种特性进行微信流量建模,采用线性分形稳定噪声模型刻画微信流量特性,完成了模型的参数估算和效果分析.本文的研究成果是后续的网络性能分析、网络流量监管等的基础.     

A small samples training framework for deep Learning-based automatic information extraction: Case study of construction accident news reports analysis

Knowledge management is crucial for construction safety management. Widely collected and well-organized safety-related documents are recognized to be significant in raising the workers' security awareness and then to prevent hazards and accidents. To improve document processing efficiency, automatic information extraction plays an important role. However, currently, automatic information extraction modeling requires large scale training datasets. It is a big challenge for the engineering industry, especially for the fields which heavily rely on the experts’ knowledge. Limited data sources, and high time and labor costs make it not practical to establish a large-scale dataset. This work proposed a natural language data augmentation-based small samples training framework for automatic information extraction modeling. With the designed cross combination-based text data augmentation algorithm, the deep neural network can be employed to build up automatic information extraction models without large-scale raw data and manual annotations. Characters semantic coding is employed to avoid word segmentation and make sure that the framework can be utilized in different writing language systems. The BiLSTM-CRF model is adopted as the detection core to conduct character classification. Through a case study of two independent accident news report datasets analysis, the proposed framework has been validated. A reliable and robust automatic information extraction model can be established, even though with small samples training.     

The impact of CCT on driving safety in the normal and accident situation: A VR-based experimental study

Correlated color temperature (CCT) of the light source inside the road tunnel plays a crucial role in ensuring driving safety, which is demonstrated by previous studies that CCT influences not only visual effects but also non-visual effects. Although conventional laboratory experiments could simulate the CCT environment inside the tunnel to some extent, they fail to restore driving experience, let alone simulate driving behavior in the accident situation. It has largely remained unclear whether and how CCT would influence visual/non-visual performance of the subjects who are performing driving tasks, especially in the accident situation. Motivated by this gap, a virtual-reality-based framework for assessing the influence of CCT on the visual and non/visual performance in normal driving situation and in accident situation was proposed. In this study, tunnel models under seven different CCTs were created and a rear-end accident was designed in the tunnel. By integrating analog driving equipment, all participants were required to perform virtual driving tasks both in the normal situation and in the accident situation. The non-visual performance (driving fatigue) and the visual performance (reaction time) of the participants were collected and analyzed. Results show that the CCT of light source inside the tunnel was significant on the driving fatigue of the driver who was performing driving task, and it also had a significant impact on the visual performance when the driver was faced with a rear-end accident. Detailed experimental methodology, behavioral explanations underlying these findings, validity of results and practical implications are also discussed in the paper.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.