生命线工程网络抗震可靠性分析方法的比较研究

生命线工程网络抗震可靠性分析是进行生命线工程系统规划、设计、改造与优化的基础。因此 ,寻找一种高效的适应性强的系统可靠性分析方法对实际工程应用具有重要的意义。本文主要介绍两种高效的网络系统两终端连通可靠性分析方法———递推分解算法和有序二分决策图算法。首先 ,分别对两种算法的原理及实现过程进行了详细的论述和说明 ,并且用c语言编制了二分决策图算法的计算程序。然后 ,利用这两种方法及随机模拟算法对 2 0个以往研究中的经典算例和两个实际工程网络———河南省电力网和上海市浦西供水管网 ,进行了网络系统抗震连通可靠性分析。通过对实例分析结果的比较研究 ,得到了一些经验性的结论 ,以期为优化设计工程网络时选择系统可靠性的分析工具提供参考。研究表明 ,由于递推分解算法具有适应性强和能够求得问题近似解的优点 ,因此有良好的实际工程应用前景。     

CO气体激光检测仪中基于FPGA的信号源设计

在激光检测仪的系统设计中,要求信号源四路波形同步异频发生,且有移相、倍频、波形叠加等要求。利用FPGA技术完成满足以上要求的直接数字频率合成器(DDS)的设计。在实现中选用Altera公司的FPGA芯片EP2C8Q208C8为主芯片进行设计,充分利用了该芯片的超大集成性和快速性。在改进的DDS算法结构的基础上,系统的复杂度降低,更趋于模块化,产生的波形频率更准确。     

管制员安全能力模型研究

为准确评价管制员安全能力,基于能力的定义和管制员工作职责,界定管制员安全能力的内涵;依据该内涵及其工作特性,建立由身体素质、业务素质和意识3个结构维度组成的管制员安全能力模型。其中,身体素质包括生理状况和心理状况;业务素质包括教育状况、培训状况、技能状况和经验状况;意识包括安全意识和工作意识。在各项指标性质差异较大情况下,利用功效系数法实现评价指标的标准化以减小误差,客观反映指标情况,同时采用G1-法和熵值法集成的主客观综合赋权法确定指标的权重,在一定程度上弥补主客观单一赋权的不足,最后通过逼近理想解排序法(TOPSIS)评价管制员安全能力。结果表明,按照从大到小排序对管制员安全能力的影响因素依次为意识、身体素质和业务素质。就对所论能力的影响而言,心理状况强于生理状况;技能状况强教育、培训和经验状况;安全意识对意识的影响大于工作意识。     

基于尺寸效应的采空区危险度RS-TOPSIS法辨识

为准确预测地下采空区危险性,选用采空区结构的跨度、暴露面积、高度、埋深、矿柱尺寸布置等5个采空区危险度结构尺寸影响因素作为评价指标,建立采空区危险度粗糙集-逼近理想解排序法(RS-TOPSIS)综合评价体系。基于粗糙集理论(RS)中的粗糙依赖度,通过计算评价指标与评价等级间的粗糙依赖度得到指标权重。以40个采空区探测系统(CMS)实测采空区作为评价对象,根据单指标分类区间下限构造5个不同等级的典型采空区,结合逼近理想解的排序法(TOPSIS),实现采空区危险度5级贴近度的分类,并辨识实测采空区危险度。研究结果表明,用为采空区群矿山建立的采空区危险度基于结构尺寸效应的RS-TOPSIS法,能够实现危险度5级分类辨识,辨识结果与采空区危险度数值分析结果吻合度为92.5%。     

煤层透气性系数的优化和简化计算方法

煤层透气性系数是用以考察保护层开采效果和设计瓦斯抽采系统的重要参数之一,钻孔径向流量法是求取该系数最常用的算法。利用理论推导和图形分析方法,探讨流量准数、时间准数、煤层透气性系数与参数A,B间的关系。在此基础上,针对科研工作和现场管理的实际情况,提出求解透气性系数的2种新算法及其算法流程,并对时间准数-参数A,B函数关系中的间断点问题进行讨论和分析,解决此区间内求解透气性系数相应计算公式等问题。理论和实例分析表明:算法1在不更改最终计算公式和结果的前提下,省去了繁琐的试算过程,既保证了可靠性也优化了计算流程;算法2以稍减精度的代价对计算流程进行进一步的简化,其结果仍有较高精度。     

基于BP神经网络算法的北京市地铁站应急疏散能力仿真评估模型

地铁站的应急疏散能力是地下铁道安全运营设计中非常重要的部分,对于维持社会稳定,保障人民安全具有重大意义.本文利用BP神经网络计算方法和MATLAB软件,选取可能影响地铁站疏散能力的相关因素作为评价指标,建立了应急疏散指标体系,并以北京北三环区域周边地铁站作为主要研究对象,对北京市地铁站应急疏散能力进行了仿真评估,提出了增加疏散通道、疏散楼梯、安全出口等数量,以提高地铁站应急疏散能力等一些可供参考的建议.     

环境模型参数识别与不确定性分析

在对水文模型实例的参数不确定性分析基础上,分别采用传统灵敏度分析方法、HSY算法、线性回归等方法对模型参数特性进行了识别与比较研究.结果表明参数优化算法与传统灵敏度分析方法不能解释模型结构复杂性特征,采用不确定性分析方法对环境模型参数进行识别提供了深入分析与理解模型系统的有效途径.     

Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.