基于Matlab的火灾探测报警系统可靠度Monte Carlo仿真

介绍了火灾探测报警系统的结构,指出了火灾探测报警系统失效原因,针对火灾探测报警系统的特点,确定了蒙特卡洛可靠度分析法.给出了系统的概率模型,确定了在Matlab下应用Monte Carlo方法对火灾探测报警系统的可靠度进行仿真的具体步骤,得到火灾探测报警系统可靠度仿真曲线.将仿真结果与试验结果相比较,基本一致,验证了该仿真方法能正确描述火灾探测报警系统的可靠度随使用寿命的变化规律,为进一步应用该方法来分析火灾探测报警系统的的各可靠性指标提供了理论基础.     

煤矿生产系统干扰事件的离散小波变换检测

小波变换常用于数字信号传输的噪声检测与处理,为了检测煤矿生产系统中的突发事件,将其模拟为数字信号传输中的干扰,构建系统稳态方程和对应的扰动方程,根据离散小波变换原理,将煤矿生产系统中的监测数据分解为近似部分和细节部分,再进行离散变换。采用多尺度边缘检测法,由光滑后的一阶和二阶导数检测出信号的模极大值点即为瞬态突变点。将A矿记录到的瓦斯浓度监控数据进行可行性分析后,在MATLAB的wavelet工具中对该瓦斯浓度监控数据作离散小波变换,找到四个瓦斯浓度瞬态突变点,那么这四个时刻发生了煤与瓦斯突出事件。结果表明该方法简单、实用性强。据此从计算机实时监控和现场技术防突方面提出应对煤矿干扰事件的措施。     

基于单片机甲烷浓度监测的研究

设计了一种基于单片机来实现的甲烷浓度检测和报警功能的系统,该检测系统以AT89S52单片机为核心,包括有电源电路、传感器采集电路、信号放大电路、红外遥控电路、声光报警电路、显示电路等功能模块组成,通过传感器采集电路采集信号,输出与甲烷浓度对应电压信号,把电压信号进行A/D转换后送入单片机,经单片机处理后驱动器显示出被测气体甲烷浓度值,若被测甲烷浓度大于报警电路预设数值,报警电路发出报警,可以及时切断供电系统,防止瓦斯爆炸事故的发生.该系统采用高精度敏感元件,集成运算放大器INA128,ADc0809,以及远距离高可靠性通信芯片,且具有高速的处理能力,是一种电路设计新颖,参数测量准确,操作方便的甲烷浓度报警监控系统,实现了数字瓦斯气体的实时监控.为实现数字化井下测控和可视化综合管理提供了切实可行的方案.     

载重车辆超载检测与阻止装置研究

针对载重车辆严重的超载问题,对车辆超载的起因、危害、检测方法等进行了深入的分析,提出了一种基于光电式位移传感器的车辆超载检测与阻止装置,并对其进行了结构设计与原理分析。利用车辆载荷与车身高度之间的对应关系,在车辆静止的状态下,通过检测车架与车桥的距离,判断车辆的载荷情况。根据超载情况提供不同的超载报警提醒,甚至切断起动机电源线、喷油器供电线及点火信号线,使发动机无法起动或正常工作,从而从源头上阻止车辆超载现象的发生。     

基于高斯模型对氯气泄漏浓度分布的快速估算方法研究

利用高斯模型对氯气泄漏浓度分布进行了较全面地探讨和研究,分析了高斯模型的实用性,探索了通过地面任意两点的浓度测量值快速计算泄漏源流强和泄漏点的高度以及地面浓度最大值及最大值位置的方法。提出了估算浓度等于和大于某一定值的区域的办法,并通过三个不同浓度的等值线的估算值和模拟值进行了比较,证明该方法的合理性。该方法完全可以推广到其它的模型和各种不同的条件,对于提高事故处置的侦检效率具有积极的意义。     

以风险预控为核心的安全管理体系理论研究与应用

以系统安全理论的观点和方法,通过建立人机环境系统物理模型,剖析两类危险有害因素诱发事故的机理,阐述危险有害因素在生产系统中不同影响程度和情况.将生产系统划分为本质安全型、风险型、危险型和危机型四种形式,并与之对应将安全管理分为本质安全管理、风险预控管理、隐患排查治理和应急响应管理四个等级.进而提出推行“以风险预控为核心、以隐患排查治理为基础、以应急响应为补充、以本质安全为目标”的“四位一体”安全管理模式.     

物联网在感知矿山建设中的应用研究

感知矿山建设是当前矿山建设研究的重要领域,主要是将目前备受国家、行业和企业界重视的物联网技术应用到矿山建设中,实现矿山生产安全的全面感知。在论述物联网发展、可视化管理理念的基础上,从感知矿山建设的步骤和主要内容等角度进行分析,提出了感知矿山建设模型,为基于物联网的感知矿山建设提供理论指导。     

Emergency preparedness through game playing

The lack of emergency preparedness in Mauritius has been the cause of many tragedies. Our approach to tackle this problem was by developing an emergency preparedness game layered and fused with a disaster warning and guidance system that emanates clarity to the unfathomable bearings of emergencies and natural disasters. The emergency preparedness game is based on a selection of diverse real life-threatening difficulties that entail different strategies aimed at bettering the survival instincts of users. It uses story-telling scenarios along with in-game footnotes that yield directives on how to brave fierce and unpredictable calamities. The game reinforces a sense of self-composedness and suppressing untimely fears of users in horrendous circumstances. With regard to the warning system, it unremittingly feeds users with notifications during emergencies, that encases shortest escape routes to lead them to safe locations via a fully functional GPS map. This application brings some novelties that are virtually non-existent in related applications. For instance, this application includes a warning and guidance system, a 3D scenario game to prepare its users for disasters, an interactive survival toolkit selection, an SMS rescue feature and a mass notification system via the web.     

Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Objective: The objective of this study is to develop a novel algorithm on a mobile system that can warn drivers about the possibility of a collision with a pedestrian. The constraints of the algorithm are near-real-time detection speed and a good detection rate.

Method: Histogram of gradients (HOG)-based detection is widely used in pedestrian safety applications; however, it has low detection speed for real-time systems. Hence, it has no direct usage for mobile systems. In order to achieve near-real-time detection speed, partial Haar transform predetections are applied to an image before HOG detection. The partial and HOG detections are merged and a score-based confidence level is defined for the final detection phase. In this way, the outcome is prioritized and different warning levels can be issued to warn the driver before a possible pedestrian collision.

Results: The proposed algorithm provides an increase in detection speed (from 46 to 76 fps) and detection rate (from 80 to 91%) with respect to HOG-based pedestrian detection. It also improves confidence of the results by multidetection merging and score assignment to detections.

Conclusions: Performance improvement of the algorithm is compared with respect to state-of-the-art detectors/algorithms. Based on the detection rate and detection speed performance, it can be concluded that the proposed algorithm is suitable to be used for mobile systems to warn drivers about the possibility of collision with a pedestrian.