Experimental research on combined effect of obstacle and local spraying water fog on hydrogen/air premixed explosion

In order to reveal the mechanism of water fog explosion suppression and research the combined effect of water fog and obstacle on hydrogen/air deflagration, multiple sets of experiments were set up. The results show that the instability of thermal diffusion under lean combustion conditions is the main influencing factor of hydrogen/air flame surface instability, and the existence of water fog will aggravate the hydrogen/air flame surface instability. When obstacle is not considered, 8 μm, 15 μm, 30 μm water fog can significantly reduce the flame velocity and explosion overpressure of hydrogen/air, 45 μm fine water fog plays the opposite role. When considering the relative position of the water fog release position and the obstacle, the 8 μm, 15 μm, 30 μm water fog has almost no suppression effect when released near the obstacle, but a significant suppression effect occur, when using the 45 μm water fog. In the field of theoretical research, the research results not only provide an experimental basis for the fine water fog to reduce the consequences of hydrogen explosion accidents, and the optimal diameter range used by the water fog, but also provide experimental reference for the numerical simulation of hydrogen/air explosion suppression in semi-open space, and promote the development of hydrogen explosion suppression theory. In terms of engineering applications, this study can provide a theoretical basis for the layout of fire fighting equipment in the engine room of nuclear power plants or hydrogen-powered ships.     

WSN低功耗低时延路径式协同计算方法

针对云计算应用于无线传感器网络(Wireless Sensor Network,WSN)时延敏感型业务时存在的高传输时延问题,提出了一种WSN低功耗低时延路径式协同计算方法。该方法基于一种云雾网络架构开展研究,该架构利用汇聚节点组成雾计算层;在数据传输过程中基于雾计算层的计算能力分步骤完成任务计算,降低任务处理时延;由于汇聚节点计算能力较弱,时延降低将导致能耗增加,WSN工作寿命减短,为此提出能耗约束下的任务映射策略,并利用离散二进制粒子群优化(Binary Particle Swarm Optimization,BPSO)算法解决能耗约束下的时延优化问题。仿真结果表明,在相同的能耗约束下,对比其他算法,基于BPSO算法得出的映射方案能有效降低业务处理时延,满足时延敏感型业务的需求。     

水溶性丙烯酸涂料的环氧树脂改性

本实验论述了水溶性丙烯酸钢铁底漆涂料制备、性能以及用环氧树脂对其进行改性。原水溶性丙烯酸钢铁底漆涂料在进行耐盐雾测定试验时仅为 72h ,未通过国家规定标准。本实验的目的在于 ,以丙烯酸树脂为主要成膜物质 ,加入其他的辅助成膜物质 ,如环氧树脂来改善其抗盐雾、抗腐蚀、耐候等性能。结果表明 ,一定温度下在水溶性丙烯酸钢铁底漆涂料中加入 1/ 7的环氧树脂后 ,经测定其耐盐雾性可达 10 6h ,达到了国家的规定标准     

车联网中基于雾计算和多TA的条件隐私保护认证方案

车联网在生活中扮演着越来越重要的角色,它可以有效地防止交通拥堵从而减少交通事故。然而,在车联网中总是有非法车辆试图接入车联网并发布虚假消息。此外,现有方案多数存在计算效率低下的问题。针对上述存在的问题进行了研究,提出了一种车联网中基于雾计算和多TA的条件隐私保护认证方案。在保护车辆用户身份的条件下实现了车辆、雾节点、TA三者之间的身份认证,且在车辆追踪阶段可以还原车辆用户的真实身份,从而实现条件隐私保护。雾计算的使用降低了方案的计算和通信开销,同时多TA模型的使用也解决了单TA单点故障的问题。安全性证明和性能分析的结果表明该方案是安全且高效的。最后对当前方案进行了总结以及对未来研究作出了展望。     

基于隶属函数的骆岗机场新型大雾预报方法

准确预报大雾对机场减少航班大面积延误和旅客滞留有重要意义。本文阐述一种适用于机场的大雾短时预报方法。通过选取骆岗机场历史地面逐时观测气象资料,依据气温趋势不同类型,运用模糊隶属度与发生条件概率模型,分别建立短时大雾的预报方法。利用VC++程序开发了骆岗机场大雾短时预报软件,可扩展硬件构建实时预警系统。使用2011年地面资料检验结果显示:对未来3小时大雾预报准确率为91.3%,TS评分为0.84。因此,本文提出的大雾短时预报方法在骆岗机场附近3小时大雾预报中是有效的。     

带钢轧机导卫设备的改进及自动控制

为了满足现代化的生产需求,采用双丝杠传动,解决了带钢轧机导卫设备的主传动机构导卫刚度和可调性差问题,实现了导卫前后的同步运动和双导向,同步导向误差率低于1％;液压移出装置,解决了在线检修影响生产和不能整体移入移出的问题,提高了设备维修的效率;水雾除尘装置,增加了导卫降尘的功能,消除了烟尘对环境的污染,降尘量达到90％;同时设计了自动化控制系统,生产效率提高60％。     

基于CFD的挑流泄洪雾化特性研究

针对泄洪雾化影响范围不易采用数学模型直接模拟的问题,以某高坝电站泄洪为研究对象,先采用三维数学模型计算挑流水舌入水流速、入水角度等水力特征值,再基于此特征值选用原型观测校正后的计算公式预报泄洪雾化范围,并通过物理模型试验对计算结果进行验证分析。结果表明,受比尺效应影响,物理模型试验预测的雾化影响范围小于公式计算的范围,但两者的变化趋势一致;雾流受惯性力和水舌风作用,沿水舌轴线做爬坡运动,与原型观测现象一致;受出口挑流影响,泄洪雾化影响集中在水舌落入点的下游,而水垫塘上游、右岸降雨强度较小,影响范围有限。     

一种基于小波分析的新型图像破雾处理方法

视频是监控系统中的一个重要信息来源,其信息量直观且丰富。但在恶劣天气条件下,尤其是雾霾天气,会使视频监控的可视性能急剧下降。采用适当的图像增强算法,可以有效地改善雾霾天气条件下监控视频的可视性。文章基于小波分析,设计一种以图像增强为基础的新型图像破雾处理方法,以期达到实用的目的。     

基于雨雾分离处理和多尺度网络的图像去雨方法

雨带来的雨条纹和雨雾会降低户外拍摄图像的质量,为了去除雨雾对图像的影响,提出了一种基于雨雾分离处理和多尺度卷积神经网络的图像去雨方法。首先利用导向滤波将雨线和图像细节信息提取到高频层,雨雾和背景信息则分离到低频层;然后构建多尺度卷积神经网络来去除高频层中的雨线,网络中融入多个稠密连接模块以提升特征提取的准确性;其次构建多层特征融合的轻量级去雾网络来去除低频层中的雨雾,采用参数一体化结构避免了估计多个大气散射模型参数导致的次优解;最后再结合处理后的高低频结果还原出清晰图像。在多个合成的雨雾数据集以及真实自然场景图像上进行测试,定性和定量结果表明,提出的方法在去除雨雾影响的同时较好地保留了色彩信息,和近年的算法相比,图像结构相似性约提升了0.02~0.08,图像峰值信噪比约提升了0.2~3.5 dB。     

Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography

The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billion-dollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's interior. So, we suggest a model called multi-layer storage (MLS) based on security using elliptical curve cryptography (ECC). The suggested model focuses on the significance of cloud storage along with data protection and removing duplicates at the initial level. Based on divide and combine methodologies, the data are divided into three parts. Here, the first two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding technique. The other part of the encrypted data is saved in the cloud. The viability of our model has been tested by research in terms of safety measures and test evaluation, and it is truly a powerful complement to existing methods in cloud storage.