Small sample estimation for Taylor's power law

An analysis of counts of sample size N=2 arising from a survey of the grass Bromus commutatus identified several factors which might seriously affect the estimation of parameters of Taylor's power law for such small sample sizes. The small sample estimation of Taylor's power law was studied by simulation. For each of five small sample sizes, N=2, 3, 5, 15 and 30, samples were simulated from populations for which the underlying known relationship between variance and mean was given by ² = c^d. One thousand samples generated from the negative binomial distribution were simulated for each of the six combinations of c=1,2 and 11, and d=1, 2, at each of four mean densities, =0.5, 1, 10 and 100, giving 4000 samples for each combination. Estimates of Taylor's power law parameters were obtained for each combination by regressing log₁₀ s ² on log₁₀ m, where s ² and m are the sample variance and mean, respectively. Bias in the parameter estimates, b and log₁₀ a, reduced as N increased and increased with c for both values of d and these relationships were described well by quadratic response surfaces. The factors which affect small-sample estimation are: (i) exclusion of samples for which m = s ² = 0; (ii) exclusion of samples for which s ² = 0, but m > 0; (iii) correlation between log₁₀ s ² and log₁₀ m; (iv) restriction on the maximum variance expressible in a sample; (v) restriction on the minimum variance expressible in a sample; (vi) underestimation of log₁₀ s ² for skew distributions; and (vii) the limited set of possible values of m and s ². These factors and their effect on the parameter estimates are discussed in relation to the simulated samples. The effects of maximum variance restriction and underestimation of log₁₀ s ² were found to be the most severe. We conclude that Taylor's power law should be used with caution if the majority of samples from which s ² and m are calculated have size, N, less than 15. An example is given of the estimated effect of bias when Taylor's power law is used to derive an efficient sampling scheme.     

A Bayesian approach to evaluating site impairment

In the United States, each state is required to list water resources that are declared to be impaired under guidelines set by the Clean Water Act. Measurements are typically collected on a number of chemical constituents and compared with a standard. If there are too many measurements exceeding the standard, then the site is declared impaired. The approach is non-statistical but similar to a Binomial test. The Binomial approach would convert the measurements to binary data then test if the proportion exceeding the standard is excessive. Both methods convert measurements to binary values hence exclude potentially important information in the data. We present a statistical approach using a Bayesian model that uses the raw data instead of the binary transformed data. The population distribution of a family of location-scale parameter models is studied under the model. Posterior distributions from the Bayesian analysis are used in the decision-making process and error probabilities for the Bayesian and the Binomial approaches are compared for a normal population.     

乌鲁木齐地区主要湖泊富营养化现状

根据“七五”期间全国14个城市湖泊富营养化调查拟定和筛选出的总氮、总磷、生化耗氧量、透明度和叶绿素a的含量五个评价参数,用评分法对我区三个主要湖(库)富营养化现状进行评价.柴窝堡湖和红岩水库已达富营养水平,乌拉泊水库达中营养水平.     

全复合材料翼面振动主动控制技术研究

目的减小复合材料结构振动响应。方法以全复合材料翼面为研究对象,结合该翼面结构有限元模型,建立带有压电作动器的结构动力学仿真模型。利用PID(Proportional,Integral and Differential)控制理论设计主动控制律,基于Simulink仿真平台设计控制律程序,通过控制律变参分析得出PID控制各参数的设计规律,基于仿真模型进行主动控制仿真试验。以仿真试验结果为基础,在复合材料翼面上进行振动主动控制地面试验。结果有效地控制了复合材料翼面振动响应,振动响应减小了79.74%,验证了模型和控制律设计的有效性。结论以压电作动器作为控制作动器,通过PID控制理论设计控制律,能够有效控制全复合材料翼面振动,使振动减小。     

48 V/50 Ah磷酸铁锂电池组热仿真

目的基于COMSOL Multiphysics软件对锂离子电池组进行热耦合仿真研究,正确辨识电池的有关热物性参数。方法首先确立锂离子电池组仿真的热模型,关于锂离子电池传热模型中涉及的热物性参数,基于不同形状大小的加热片对电池在绝热环境下进行加热,将电池各热物性参数的获取相互解耦,通过对传热模型方程以及边界条件的简化辨识出锂离子电池的热物性参数。最后,基于已确立的锂离子电池热仿真理论基础以及辨识出的相关参数,运用COMSOLMultiphysics仿真软件对锂电池单体和电池组在不同充电倍率下的温度场进行仿真分析,并进行实验验证模型的精确程度。结果根据仿真结果和实验数据的对比分析,基于COMSOLMultiphysics仿真软件可以较为准确地对锂离子电池组壳体表面和内部的温度场进行描述,误差不超过0.5℃。结论在满足一定误差精度范围内,对锂离子电池组热管理系统进行分析和设计时,通过仿真得到的结果就可以为其提供参考和指导。     

作物重金属铜污染的HHT边际谱特征与污染预测模型

为了解读作物受重金属污染的光谱响应与光谱特征,以不同浓度梯度硫酸铜（CuSO₄·5H₂O）胁迫土壤的盆栽玉米培养胁迫实验为研究对象,依据不同胁迫梯度下玉米叶片反射光谱以及叶片中Cu²⁺含量的测定数据,采用希尔伯特-黄变换（HHT）方法,研究了玉米叶片在不同Cu²⁺胁迫梯度下光谱的Hilbert边际谱变化特征与污染程度预测方法.通过构建玉米叶片光谱的边际谱熵（MSE）,边际谱幅值（MSA）、边际谱陡坡斜率（MSSS）和边际谱包围面积（MSEA）等特征参量,分析叶片在不同Cu²⁺污染程度下的边际谱变化;同时基于边际谱特征参量值与叶片中Cu²⁺含量的相关性分析和逐步回归统计,提出了玉米叶片重金属污染的Cu²⁺含量预测指数模型.实验结果表明,不同Cu²⁺胁迫梯度下,玉米叶片光谱的边际谱为分布在100Hz频率以内的连续谱;MSE值表现出与叶片中Cu²⁺含量呈负相关的变化趋势,而MSA、MSSS和MSEA值都表现出与叶片中Cu²⁺含量呈正相关的变化趋势;由于MSEA值与叶片中Cu²⁺含量的相关性最好,可把MSEA作为监测作物重金属污染衡量或预测的最优指标;根据MSE、MSA、MSSS和MSEA值构建的Cu²⁺含量预计指数模型应用结果比较,证明MSEA指数模型具有最优的预测能力.     

南京北郊云凝结核活化特征观测及闭合研究

采用SMPS-CCNC联用全扫描的方法,于2016年11~12月在南京北郊地区,对8.2~346nm粒径段的气溶胶粒子展开了粒径分辨的云凝结核（CCN）活化特征观测,并利用6种参数化方法对CCN数浓度进行了闭合研究.结果表明本次观测中南京地区的气溶胶CCN活性和吸湿性均较高,其中平均吸湿性参数（k）为0.31.通过污染天和清洁天CCN特征的对比发现,污染天气溶胶和CCN数浓度均较高,但CCN活性和气溶胶吸湿性却较差,可能是由于新鲜排放的黑炭或有机物的影响.对比6种参数化方法的闭合结果,发现利用截断粒径和临界干粒径这两种参数对CCN数浓度进行预报的结果最为理想.此外,探讨了不同时间分辨率的参数对闭合结果的影响,对比发现参数时间分辨率的提高只增加了闭合结果R²的值,对闭合斜率的影响基本可以忽略,因此综合考虑,本研究可直接利用各参数的整体日变化平均来进行CCN数浓度的闭合.     

基于集总参数模型的室内PM2.5浓度预测

基于颗粒物浓度集总参数模型建立室内PM_2.5预测模型,同时对模型中的关键参数穿透率、沉降率理论模型进行理论计算.以常州市某住宅建筑为例,通过动态模型对穿透率和沉降率模型进行实验验证,实验采样时间为2017年3月~2018年1月.根据实验数据计算换气次数在0.31~0.89h^-1范围内PM_2.5通过维护结构的穿透率为0.78~0.97,室内PM_2.5沉降率为0.3~0.69h^-1.本模型能较好地适用于自然通风、机械通风等不同通风工况室内颗粒物浓度预测.当室外PM_2.5浓度在135~150μg/m³变化时,使用过滤效率为82%的新风系统可维持室内PM_2.5浓度值在40~46μg/m³.     

多元时间序列PCA分割及在天然气脱水装置工况识别中的应用

目的建立不同的工作状态下的设备状态预测评估模型。方法利用主成分分析法将多元时间序列数据进行切割,将分割后的数据段按照基于密度的方法,依照定义的距离,对多元时间序列进行聚类合并,从而得到不同工况下的时间序列。结果按照脱水装置工况状态,将重沸器特征对应的多元时间序列划分为不同的数据段。结论有效实现了脱水装置重沸器的参数数据分割,并识别出不同工况。     

Modeling of real scale waste activated sludge anaerobic digestion process by Anaerobic Digestion Model 1 (ADM1)

In this study, real-scale wastewater treatment plant (Hurma WWTP) sludge anaerobic digestion process was modeled by Anaerobic Digestion Model (ADM1) with the purpose of generating the data to understand the process better by contributing to the prediction of the process operational conditions and process performance, which will be a base for future anaerobic sludge stabilization process investments.

Real-scale anaerobic sludge digestion process data was evaluated in terms of known process and state variables and also process yields. Average VS removal yield, methane production yield, and methane production rate values of the anaerobic sludge digestion unit were calculated as 46.4%, 0.49 m³CH₄/kg VS_removed, and 0.33 m³ CH₄/m³day, respectively. In this study, ADM1 was intended to predict the behavior of real-scale anaerobic digester processing sewage sludge under dynamic conditions. To estimate the variables of real-scale sludge anaerobic digestion process with high accuracy and to provide high model prediction performance, values of the four parameters (disintegration rate constant, carbohydrate hydrolysis rate constant, protein hydrolysis rate constant, and lipid hydrolysis rate constant) that have strong effects on structured ADM1 were estimated by using the parameter estimation module in Aquasim program and their values were found as 0.101, 10, 10, and 9.99, respectively. When the numbers of kinetic parameters with the processes included in ADM1 along with the dynamic and non-linear structure of the real scale anaerobic digestion were taken into consideration, model simulations were in good agreement with measured results of the biogas flow rate, methane flow rate, pH, total alkalinity, and volatile fatty acids.