基于MMA7260QT的危险场所跌倒检测设备的研制

在石化企业生产过程中,如何正确检测判别跌倒行为已成为危险场所安全防护的关键,传统的判别方法已不适应石化行业特殊的工作环境和行为特点。通过对三轴加速度传感器MMA7260QT原理的研究,结合毒性气体传感器和无线传输技术,采用低功耗微处理器MSP430F249,研制了一种危险场所用跌倒检测设备。介绍了三轴加速度传感器MMA7260QT的结构和原理,给出了设备系统设计。根据石化行业危险场所特点,建立了跌倒模型,分析了三轴加速度传感器检测信息的处理,确定出跌倒判断阈值。实验结果表明,研制的设备跌倒检测准确,符合石化行业危险场所应用需求。     

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。     

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.     

PVC电缆火灾早期特征气体的组成分析和传感器探测

利用气体传感器对电缆绝缘过热释放的气体进行监测有望实现电气火灾早期预警,目前该技术的发展由于特征气体未得到确认而受到限制。分析了聚氯乙烯(PVC)电缆绝缘层在不同温度下释放的气体组成,并对其主要成分进行传感器测试。TG-IR和GC-MS分析表明PVC电缆绝缘层在200℃前已出现微小失重,释放出以邻苯二甲酸二辛酯(DOP)等塑化剂为主的气体。气敏测试中,商用半导体气体传感器对DOP和电缆蒸气产生较好、相似的响应性能。因此,DOP可以作为PVC电缆火灾早期的特征气体。     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.