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。     

PLC与组态王十字路口交通灯控制系统设计

使用欧姆龙PLC控制器设计十字路口交通灯控制系统。在设计硬件接线图与软件功能图的基础上,编写出相应的梯形图程序．采用CX—Programmer软件和Kingview 6．53组态软件进行模拟仿真。实验结果表明,该交通灯控制系统界面友好。运行正常,具有较好的控制效果。     

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.     

基于改进SSD的道路交通标志检测

针对复杂环境下交通标志检测精度低的问题,设计了一种检测精度更高的目标检测算法,对SSD深度学习目标检测算法进行了优化改进;将深度特征表征能力较强的Resnet50网络模型融入于SSD算法中;采用K-means++聚类算法确定SSD中先验框的尺寸,提高交通标志的检测率;分别利用SSD模型和改进的SSD模型做检测对比实验,结果表明,改进算法对各类型交通标志的检测精度比原SSD算法更高;改进的SSD方法对交通标志进行检测能取得较好效果,弥补了原算法的不足.     

防误操作新型电力短路接地线的研制

挂接地线误操作导致的恶性误操作事故时有发生.提出了一种全新的智能化电力短路接地线,该接地线具备完善的报警功能,能有效避免带电误挂接地线或漏拆接地线合闸送电的误操作事故.     

车联网仿真测试评价技术研究综述

智能化和网联化是汽车发展的重要方向,车联网技术作为智能网联汽车体系架构的重要组成部分,近年来成为业界的研究热点之一。车联网的大规模部署及其在智能汽车领域的广泛应用前需要对其性能及功能进行全面、深入的测试评价,然而在真实环境下进行车联网的测试与评估存在成本高、难度大的问题,因此通过仿真手段对其进行评价分析是当前的主流测试手段。本文总结了主流的网络仿真器和交通仿真器,对现有车联网仿真平台进行了分类,研究并对比分析了典型的车联网仿真平台;针对车联网的应用特性,研究并归纳了影响车联网仿真性能的车辆移动模型、信道传播模型及驾驶员行为等;从网络仿真指标、车联网应用相关指标归纳了车联网功能及性能测试的典型评价指标。     

某特大简支梁桥墩身开裂的事故排查

桥梁裂缝严重影响结构安全性和耐久性,不符合“绿色建造”和“可持续发展”的相关理念。文章对某特大简支梁桥桥墩开裂现象进行事故排查,为裂缝修复工作提供依据,以促进桥梁施工各个环节的规范化。考虑支座中未拆除的临时连接螺栓对墩、梁的约束作用,首先以Abaqus分析未拆除的临时连接螺栓对支座水平剪切变形的约束作用,然后以Midas计算了3种最不利工况下桥墩墩顶处的支座反力,最后以ANSYS分析3种最不利荷载组合时墩身应力。结果表明,即使按墩顶的4个纵向活动支座临时连接被同时破坏的最不利情况考虑,墩身在3种最不利工况下的最大主拉应力也仅为0.08 MPa,远未达到C30混凝土的拉应力容许值0.73 MPa。因此,架梁完成后未及时拆除支座临时连接的限位螺栓并不是导致该桥墩身开裂的主要原因,事故调查需从其他方面入手。但该疏忽会在桥墩中引起拉应力,应尽量避免,确保桥梁建造的各个环节更为规范。     

基于NN的无信号混合交通交叉口自行车冲突避让行为建模仿真

在我国城市无信号交叉口,自行车与混合交通流的其他车辆(如机动车、自行车、行人)的冲突避让行为,难以通过传统数学方法建立准确的仿真模型.本文在以往交叉口自行车微观模型基础上,采用冲突车辆与自行车的相对位置矢量、相对速度矢量以及期望速度与实际速度相对矢量作为输入,自行车二维空间加速度矢量作为双输出的神经网络建立数学模型.利用实地采集自行车-机动车冲突、自行车-自行车冲突和自行车-行人冲突样本数据,进行神经网络训练和仿真验证,并对骑车者性别、冲突车辆类型和冲突速度进行了模型灵敏度分析.结果表明,模型能较好地反映无信号交叉口自行车与其他车辆及障碍物的冲突避让行为.     

基于BP神经网络和模糊推理系统的短时交通流预测

本文研究短时交通流预测。短时交通流预测是智能交通系统研究和实践的必要基础。本文提出和建立了一个短时交通流量预测模型,该模型利用一个基于规则的模糊系统,非线性地组合BP 神经网络模型和自适应卡尔曼滤波模型的交通流量预测结果,使得短时交通流量的预测结果更加准确可靠。该模型将传统方法和人工智能方法有机结合,一方面,利用人工神经网络强大的动态非线性映射能力,从而提高预测精度;另一方面,充分发挥卡尔曼滤波的静态线性稳定性,解决了单独使用BP神经网络进行预测时识别率不理想和可信度不高的问题。实验结果表明,本文提出的短时交通流预测模型具有较高的准确度和可靠度。