安全联锁设备SIL验证中的参数估计偏差分析*

为提高安全联锁系统（SIS）不可用度计算的准确性,减少SIL验证的不确定性偏差,研究参数估计置信度分别为70%和90%时功能安全标准IEC61508的恒定失效率算法与实际非恒定失效率（威布尔分布）算法结果的偏差。结果表明:在70%置信度时,采用指数分布算法计算PFDavg,比威布尔分布算法的计算结果偏大,2者的相对偏差平均值为68.01%;90%置信度时,2者的相对偏差平均值为42.63%,揭示功能安全标准IEC61508恒定失效率计算将显著高估SIS的不可用性,可能造成企业不必要的整改和相应的维护成本。     

The unintended impact of helmet use on bicyclists’ risk-taking behaviors

Introduction: Safety is a critical factor in promoting sustainable urban non-motorized travel modes like bicycles. Helmets have shown to be effective in reducing injury severity in bicycle crashes, however, their effects on bicyclists’ behaviors still requires deeper understanding, especially amid the emerging trend of using shared bicycles. Risk compensation effects suggest that bicyclists may offset perceived gains in safety from wearing a helmet by increasing risk-taking behaviors. A better understanding of these compensation effects can be useful in assessing various bicycle safety related programs. Method: Using a sample of 131 bicyclists from the San Francisco Bay area, this research studies how bicyclists respond with respect to risk-taking behaviors under various urban-street conditions, as a function of helmet use. Study participants were each shown 12 videos, shot in Berkeley, California, from the perspective of a bicyclist riding behind another bicyclist. A fractional factorial experiment design was used to systematically vary contextual attributes (e.g., speed, bike lane facilities, on-street parking, passing vehicles) across the videos. After each video, participants were asked to indicate if they would overtake the bicyclist in the video. With the help of data adaptive estimation techniques, targeted maximum likelihood estimation (TMLE) was applied to estimate the average risk difference between helmeted users and non-users, controlling for self-selection effects. Individual-based nonparametric bootstrap was performed to assess the uncertainty associated with the estimator. Results: Our findings suggest, on average, individuals more likely to wear a helmet are 15.6% more likely to undertake a risky overtaking maneuver. Practical Applications: This study doesn’t try to oppose mandatory helmet laws, but rather serves as a cautionary warning that road safety programs may need to consider strategies in which unintended impact of bicycle helmet use can be mitigated. Moreover, our findings also provide additional evaluation component when it comes to the cost-benefit assessment of helmet-related laws.     

加速退化试验方案优化问题经验参数估计方法

加速退化试验需要开展多组试验,投放多个试验样品,应力值的选取和样本量的投放以及截尾时间或截尾数的确定直接影响了加速退化试验的效率和质量,加速退化试验的最优设计问题成为试验方案设计的关键因素。在开展试验方案优化分析时,必须知道退化函数,而开展加速退化试验的目的就是估算退化函数中的未知参数,如何事先估计退化函数中未知参数的大致分布情况成为解决这一矛盾的关键。本文给出了一种基于可靠性预计的加速退化试验方案优化问题经验参数粗略估计方法,为解决这一问题提供了参考。     

液压振动台功率谱复现控制研究

PID控制因为结构简单、稳定性好、可靠性高等优点被广泛应用。针对实际控制系统往往存在非线性因素,常规PID不能满足实际需求,设计了基H1法频响函数估计的变参数PID控制算法。通过对两点激励液压振动试验系统进行随机振动试验,表明H1法变参数PID控制算法对振动台具有良好的控制效果,可以实现高精度的功率谱复现。     

基于深度学习方法的PM2.5精细化时空估算模型

提出一种基于深度学习方法的地面PM_2.5浓度时空估算模型（PM_2.5-DNN）,该模型基于葵花-8卫星反演的AOD数据,结合PM_2.5监测站和气象站点观测数据对北京市地面PM_2.5浓度进行了逐时的高精度模拟,同时将PM_2.5-DNN模型的模拟性能与当前的主流方法进行了对比研究.结果表明,使用PM_2.5-DNN模型估算的北京地区1km分辨率每小时地面PM_2.5浓度与地表监测站观测数据对比的一致性较好,模型估算精度可达到R²=0.88,性能优于当前的主流方法.本文所提出的方法适用于区域尺度PM_2.5浓度时空分布细粒度建模与估算,采用端到端的训练方式构建模型,为精细的PM_2.5浓度估算提供了一个简便而有效的方法模型.     

武夷山风景名胜区生态系统服务价值评价

参照Costanza等对全球生态系统服务价值的测算方法,初步估算了武夷山风景名胜区的生态服务价值.研究结果表明,该区生态系统服务总价值为12.59×106 USD·a-1,其中生态效益占79.67%.这反映出该区生态系统具有明显的生态功能.该区生态系统服务价值占全国的0.001 444%,调节水分等对全国生态服务功能贡献相对较高.该景区1996-2001年生态服务功能发生了一定程度的变化,2001年提供的生态服务功能价值比1996年减少了1.94×105 USD·a-1.由于存在许多不确定因素以及方法上的不成熟,故所得武夷山风景名胜区生态系统服务价值只是一个保守的、较低的估计值.     

基于单元分析的青铜峡灌区农业非点源污染估算

针对灌区农业非点源污染难以监测控制的具体特点,基于单元分析的观点,提出了负荷贡献率的概念,并在此基础上,建立了灌区农业非点源污染负荷估算方法;以青铜峡灌区为例,利用2005-2006年灌溉周期灌排水质监测试验资料对灌区年农业非点源污染矿化度、铵氮、总氮和总磷负荷进行了估算,并与平均浓度法进行比较.计算结果表明:青铜峡灌区农业非点源污染年输出盐分283万t,铵氮0.55万t,总氮4.11万t,总磷123 t;结合黄河干流控制断面水质资料比较分析,青铜峡灌区农业非点源污染负荷中盐分输出占干流区间增量的70%,铵氮和总氮分别是区间点源污染负荷的0.28倍和1.52倍,对黄河干流水质影响很大;而总磷由于输出量较小,对干流水质影响不大.     

基于PNGV模型的锂离子电池荷电状态估计

目的 提升不同老化情况下的锂离子电池荷电状态(SOC)估计精度。方法 基于PNGV模型(Partnership for a New Generation of Vehicles)，对锂离子电池SOC进行估计。首先通过双线性变换对PNGV模型进行离散化，采用带有遗忘因子的递归最小二乘法(FFRLS)，对电池模型参数进行在线辨识，利用卡尔曼滤波(EKF)算法进行SOC估计，并通过动态工况验证SOC估计精度。结果 以多种误差指标考察不同循环下的试验数据，在不同电池老化状态下具有较好的预测精度。相比基于Thevenin模型的算法，基于PNGV模型的算法可以将SOC平均绝对误差减少约60%，同时也可以将SOC估计最大绝对误差波动范围降低53.8%。结论 本算法引入PNGV模型后，解决了基于Thevenin模型算法误差大、不稳定的问题，提升了动力电池系统在不同老化环境下的适应性。     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.