基于PCA-FPP-BP神经网络的高层建筑火灾安全评价

为解决高层建筑构造复杂、人员密度大、火灾触发因素繁多而造成高层建筑火灾安全评价困难的问题,本文提出基于PCA-FPP-BP神经网络的高层建筑火灾安全评价模型。首先运用主成分分析(PCA)对构建的高层建筑火灾安全评价指标降维处理,筛选主要信息;接着基于三角模糊数构建模糊评判矩阵,利用模糊优先规划(FPP)求解指标的权重值,减少主观的影响;最后考虑到指标间关系错综复杂彼此交叉和反馈的特性,选择BP神经网络对高层建筑火灾安全进行评价。通过工程案例证明该评价模型的实用性以及可靠性。     

基于广义概念的城市燃气管道泄漏精确定位

为了准确地检测城市燃气管道泄漏,提出了一种基于广义概念的管道泄漏检测定位方法。声发射技术对于管道泄漏的检测、定位是一个极好的工具,但由于泄漏源的传播容易受到周围背景噪声以及复杂工况的影响,其定位误差较大。基于时延估计的互相关信号处理方法被广泛用于管道泄漏检测定位,但由于泄漏应力波传播通道的动态特性,使得源信号在传播过程中会产生波形变化,给互相关函数峰值位置的确定带来困难。由此引入广义相关分析方法,通过对信号进行前置滤波,在一定程度上减少了传播通道动态特性因素对泄漏点定位的不利影响,得到了更为准确的时延估值。在此基础上,通过模拟实验,编写Matlab神经网络代码,构造GRNN模型,进一步预测定位。结果表明,GRNN预测的声发射检测值、互相关定位值以及广义相关定位值,相比之前定位精度分别得到提高,其中基于广义相关的延时估计方法定位最为精确,将该方法用于工程实际中,可以更加精确地定位出泄漏点。     

基于互联网+的轨道交通动力设备安全管理系统的研究

根据我国轨道交通设备安全管理的需求,提出基于互联网+的轨道交通动力设备安全管理系统平台的框架,运用J2EE开发工具,采用嵌入式高速数据采集技术、故障诊断技术,构建形成集成化、网络化和信息化的轨道交通动力设备安全管理系统。完善智能的设备安全管理系统,能有效提高轨道交通动力设备的安全监管力度,保障设备安全。     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

基于离散仿真分析的废弃电器电子产品回收网络优化

优化电子废弃物回收网络对控制回收成本、降低环境污染具有重要意义.因此,本文建立了一种综合考虑运输、存储及淡旺季的回收过程离散仿真模型,提出了一种以经济、能源及环境为指标的回收网络评价方法.同时,以安徽省为例,结合现有回收模式,分别设计了4种回收网络布局及运输方案,在不同的回收覆盖率及库存能力条件下以回收成本、燃油消耗及CO2排放为优化目标对比分析各备选方案,提出了在报废量大于0.15万t·a-1的地区建立回收点并采取全局运输模式的最优方案.研究表明,本文提出的方法同样适用于我国其他省市回收网络的优化.     

机场环境下飞机LY12CZ结构腐蚀损伤预测方法研究

依据LY1 2CZ材料的EXCO溶液浸泡加速腐蚀试验数据、飞机LY 12CZ构件在机场环境下的真实腐蚀损伤数据以及腐蚀损伤等效原则和当量关系原理,获得了该材料在2种环境下腐蚀的当量因子均值(α) =0.023 6,构建了LY12CZ材料于EXCO溶液中浸泡腐蚀的神经网络腐蚀深度预测模型.在此基础上提出了利用所建的神经网...     

基于DEM地表水文特征和汇水子流域的提取研究

文章以天津于桥水库上游流域为研究对象,以DEM(数字高程模型)为基础数据,对DEM生成方法、DEM精度、流域地形地貌特征及阈值的选取三个方面分别作了详细研究。试验结果表明,从于桥水库上游流域的水文特征所提取的河网分布与河流与实际分布情况基本吻合,淋河、沙河、黎河三大流域的面积误差范围分别为-3.44%、2.28%和7.63%。     

植物修复在水环境生态修复中的应用

介绍了平原河网水资源现状及问题成因,对植物修复在水环境生态修复中的应用作了详细的论述,分析了其存在的问题,展望了植物修复应用的发展方向。     

大气污染总量控制规划智能决策支持系统的开发与研究

对大气污染总量控制规划智能决策支持系统的开发步骤和总体结构做了介绍。并描述了该系统中的知识库系统、模型库系统、数据库系统和图形库系统的设计思想,特别是对知识库的构造进行了较详细的叙述。还介绍了神经网络技术在该系统中的应用。指出大气污染总量控制规划智能决策支持系统将为大气污染总量控制提供更科学、更合理、更有效的规划方案。     

基于高通量绝对定量测序解析岗南水库微生物群落的时空分布特征及关键驱动因素

为了探究水源水库微生物群落时空演变特征及其环境驱动因子,采用高通量绝对定量测序技术,结合数据分析了相对定量和绝对定量对岗南水库微生物群落演变和驱动因子的影响.结果表明,基于绝对定量的微生物群落α-多样性呈现出明显的季节差异(p＜0.05),基于相对定量的微生物群落α-多样性没有差异;绝对定量的微生物群落a-多样性高于相...