Mathematical Advancement of General Chlorine Bulk Decay Model

An accurate chlorine bulk decay model is needed to ensure that potable water meets the microbial and the chemical safeties at the treatment plant and throughout the distribution. Among the mathematical models available,the general second-order chlorine bulk decay model( GBDM) is the most fundamentally sound.Application of the GBDM,however,has been hindered by its numerous fictive parameters and lack of an analytical solution. This theoretical work removes the two obstacles. The GBDM is solved through transformation and integration. The analytical solution provides deep insights into the GBDM and facilitates the parameterization and sensitivity analysis. The background natural organic matter( NOM) is characterized with the probabilistic distribution of functional groups. It reveals that the mean of the function group distribution is correlated with the initial chlorine decay rate coefficient( κ_0). A simple formula is developed to determine κ_0 directly from the initial chlorine decay. The theoretical treatment reduces the fictive parameters to a minimum. For the common lognormal distribution,the GBDM needs only three parameters,well defined as initial chlorine demand X_0,median rate coefficient km,and heterogeneity index σ. For more complicated scenarios,composite distributions are constructed through superposition of individual distributions. A highlighted example is to predict chlorine decay in blends of different waters. With the theoretical and mathematical advancement here,the GBDM can be applied effectively to any reactive background matter in any reaction systems.     

既有建筑节能改造保温材料的NSGA-Ⅱ协同设计

保温措施是既有建筑节能改造中最有效的节能措施之一。在墙体节能改造设计中,保温材料种类及其厚度的确定是至关重要的,受到多个目标参数的制约和影响。基于反问题的研究方法,建立数值计算方法与多目标遗传算法NSGA-Ⅱ相结合的优化模型,以单位面积墙体全年总能耗和保温材料成本为目标参数,对成都地区某既有建筑墙体节能改造的保温材料种类与厚度进行了协同设计。结果表明,采用常规的保温厚度,常用的8种保温材料中有6种都不是最优解,但是可以通过改变保温材料的厚度或单价等方法使解向Pareto前沿逼近,以实现单位面积墙体年总能耗最小和改造成本最小之间的平衡。     

基于KL散度的用户相似性协同过滤算法

大多数用户相似性算法在计算用户相似性时只考虑了用户间的共同评分项,而忽略了用户其他评分中可能隐藏的有价值信息.为了准确评估用户间的相似性,提出了一种基于KL散度的用户相似性协同过滤算法.该算法不仅利用了共同评分项,还考虑了其他非共同评分信息的影响.该算法充分利用了用户的所有评分信息,提高了用户相似性度量的可靠性和准确性.实验结果表明,该算法优于当前主流的用户相似性算法,且在没有共同评分信息的条件下,仍能有效地完成用户相似性度量,解决了对共同评分项的完全依赖问题,具有更好的适应性.     

基于锁相放大算法的微弱光谱分析

由于强噪声背景的影响,在进行光谱探测的过程中收集到的信号很微弱。为了能有效的去除噪声,提出了采用锁相放大算法去除噪声提取光谱特征信号的方法。研制编写锁相放大算法,对收集的光谱数据进行处理,分析处理后的结果,光谱特征获得了放大,进一步进行两种结果的中值归一化对比,该光谱曲线与原始光谱曲线线型基本一致,且具有平滑去噪效果。该成果对微弱光谱信号去除噪声的研究具有实际意义。     

面向车辆识别的样本自反馈式级联检测方法

以解决机器学习的样本提取问题、提高车辆识别的精度为目的,以Adaboost算法、级联检测方法、样本自反馈方法为基础,提出了一种面向车辆识别的样本自反馈式级联检测方法。方法兼具Adaboost级联检测器的架构优势和样本自反馈方法的样本扩充能力。通过前车图像的识别实例阐述了样本自反馈式级联检测方法的识别过程,进而使用常规Adaboost检测器和样本自反馈式级联检测方法对图像数据集进行了识别,将生产者精度、用户精度、总体识别精度、k值作为评价指标对识别效果进行了比较,证明了样本自反馈式级联检测方法具有较高的识别精度。分析了样本自反馈式级联检测方法个别评价指标较低的原因,提出了相应问题的解决思路。     

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tunnels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwater inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel‘s geometry and environmental situations.In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estimation of leakage was obtained using the stepwise algorithm. Statistics like R, R~2, R_P~2 and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seepage into tunnels.     

城市交通发展规模与交通资源适应性模型及算法

为科学确定城市交通的发展规模,在分析关键交叉口转向能力的基础上,提出了基于超级节点的城市交通网络构建方法;考虑公交资源以及公交优先发展战略的因素,构建了城市交通发展规模与交通资源适应性的数学模型,并设计了基于反馈的免疫克隆算法.以城市内某一区域为例对模型和算法进行验证,结果表明:模型及算法具有较好的实用性,能够为城市交通规划部门提供良好的决策支持.     

基于火积耗散原理的热网加热器优化设计与分析

为使热网加热器具有最优的换热性能,基于粒子群优化算法,以火积耗散数为目标函数,提出热网加热器优化设计的最小火积耗散优化方法.与传统的热网加热器设计计算不同,本文的优化设计不预先设定热网加热器的结构,通过优化确定热网加热器的最佳尺寸,同时,热网加热器优化设计考虑了工质的相变,以潜热修正值对壳侧蒸汽凝结的对流换热系数进行修正.对一热网加热器优化设计结果表明,通过优化设计,热网加热器的效能增加7.8%,同时热网加热器功率消耗下降19.6%,表明当热网加热器的热力性能最优时,其功耗达最小值.     

一种高效二进前馈神经网络学习算法

为解决二进前馈神经网络(BFNN)缺乏高效实用学习算法的问题,提出一种新的融合自适应调节结构和权值的BFNN学习算法.该算法借鉴并改进了极限学习机(ELM)方法,可以高效地训练单隐层的BFNN来解决分类问题.为了满足网络的训练精度,算法可以自动增加隐层神经元个数和调节网络隐层及输出层神经元权值;同时为了提高网络的泛化精度,算法通过建立二进神经元敏感性作为度量隐层神经元重要性的尺度,自动地裁剪重要性小的神经元,并对裁剪损失的信息进行补偿.实验结果验证了该算法在处理离散分类问题时的可行性和有效性.     

一种改进的AprioriTid算法

针对经典Apriori算法多次扫描数据库产生I/O负载影响运行效率等问题,在对Apriori算法的原理及其相关改进算法研究的基础上,提出了一种基于压缩集的改进Apriori算法,即Apriori Tid_M算法.通过有效的裁剪方法减少无效项集的产生,减少候选项集的数量,从而提高算法的效率.仿真实验表明,在支持度相同但数据量不同,以及数据量相同但支持度不同这两种条件下,Apriori Tid_M算法在性能上和运算时间上都比Apriori算法有很大程度的改善.