Estimation of the Population Mean Using Paired Ranked Set Sampling

In the situation where the sampling units in a study can be easily ranked than quantified, the ranked set sampling methods are found to be more efficient and cost effective as compared to SRS. In this paper we propose an estimator of the population mean using paired ranked set sampling (RSS) method. The proposed estimator is an unbiased estimator of the population mean when the set size is even. In case of odd set size the estimator is unbiased when the underlying distribution is symmetric. It is shown that the proposed estimator is more efficient than its counterpart SRS method for all distributions considered in this study.

基于CNN-GRU组合神经网络的数控机床进给系统热误差研究

热变形引起的误差是影响数控机床精度的主要因素之一。为了减小热误差对数控机床精度的影响,提出一种基于CNN-GRU组合神经网络的热误差预测方法。通过热误差实验,采集螺旋曲面专用数控机床直线进给系统的温升数据和热误差数据;利用模糊C均值聚类和灰色关联度分析筛选进给系统温度敏感点;以温度敏感点的温升数据和进给系统热误差为数据样本,建立CNN-GRU热误差预测模型。为验证模型的准确性和实用性,与基于CNN-LSTM和基于LSTM的传统热误差预测模型进行预测对比分析,结果表明CNN-GRU模型预测结果的平均绝对误差、均方根误差和决定系数均优于CNN-LSTM模型和LSTM模型,具有较高的预测精度和鲁棒性。提供的热误差模型可为后续误差补偿奠定基础,为数控机床的热误差预测提供思路。

An Efficient Class of Estimators for the Finite Population Mean in Ranked Set Sampling

In this paper, we propose a class of estimators for estimating the finite population mean of the study variable under Ranked Set Sampling (RSS) when population mean of the auxiliary variable is known. The bias and Mean Squared Error (MSE) of the proposed class of estimators are obtained to first degree of approximation. It is identified that the proposed class of estimators is more efficient as compared to [1] estimator and several other estimators. A simulation study is carried out to judge the performances of the estimators.

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.

Energy Efficient and Intelligent Mosquito Repellent Fuzzy Control System

Mosquitoes are of great concern for occasionally carrying noxious diseases(dengue,malaria,zika,and yellow fever).To control mosquitoes,it is very crucial to effectively monitor their behavioral trends and presence.Traditional mosquito repellent works by heating small pads soaked in repellant,which then diffuses a protected area around you,a great alternative to spraying yourself with insecticide.But they have limitations,including the range,turning them on manually,and then waiting for the protection to kick in when the mosquitoes may find you.This research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active time.The speed and active time depend on the repellent cartridge and the number of mosquitoes.The Mamdani model is used in the proposed fuzzy system(FS).The FS consists of identifying unambiguous inputs,a fuzzification process,rule evaluation,and a defuzzification process to produce unambiguous outputs.The input variables used are the repellent cartridge and the number of mosquitoes,and the speed of mosquito repellent is used as the output variable.The whole FS is designed and simulated using MATLAB Simulink R2016b.The proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation capability.Different simulations of the proposed model are performed in many nonlinear processes.Then,a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS,yielding a maximum relative error of 10%.The experimental outcomes show that the root mean square error is reduced by 67.68%,and the mean absolute percentage error is reduced by 52.46%.Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent.

基于改进流形正则化极限学习机的短期电力负荷预测

为提高短期电力负荷预测的精度与效率,提出一种改进流形正则化极限学习机的短期电力负荷预测方法;首先,为了改善极限学习机(ELM)的泛化性能与效率,并解决随机初始化参数导致极限学习机存在的潜在问题,采用流形正则化理论优化极限学习机;其次,针对流形正则化极限学习机中参数的选择,以及流形正则化极限学习机隐层节点选择的问题,提出将贝叶斯优化算法(BOA)融入到流形正则化极限学习机中以优化流形正则化极限学习机(MRELM)。最后,通过实验数据分析,改进流形正则化极限学习机预测方法将预测平均相对误差降低到了1.903%,30次实验的平均相对误差的方差降低到了1.9‰,平均单次运行时间降低到了6.113 s。

结合似然关系模型和用户等级的协同过滤推荐算法

针对传统协同过滤推荐算法的稀疏性、扩展性问题,提出了结合似然关系模型和用户等级的协同过滤推荐算法.首先,定义了用户等级函数,采用基于用户等级的协同过滤方法,在不影响推荐质量的前提下有效提高了推荐效率,从而解决扩展性问题;然后,将其与似然关系模型相结合,使之能够综合利用用户信息、项目信息、用户对项目的评分数据,对不同用户给出不同的推荐策略,从而解决稀疏性问题,提高推荐质量.在MovieLens数据集上的实验结果表明,该算法比单纯使用基于似然关系模型或传统协同过滤技术的推荐算法,不仅推荐质量有所提高,推荐速度比传统协同过滤算法明显加快.

基于灰色关联分析的软件工作量估算方法

针对历史数据少、信息贫乏的软件估算问题,提出了一种基于灰色关联分析的工作量估算方法。首先结合灰色关联理论分析了项目特征与工作量的关联度,然后利用回归技术选取最优特征集;在此基础上,计算新项目和历史项目的关联度,并根据关联度大的项目估算软件工作量;最后通过4个典型的软件数据集对估算方法的性能进行分析。实验结果表明,该方法能够在历史数据少的情况下准确估算出软件工作量,其性能优于基于回归分析、BP神经网络和类比估算等方法;而选取最优特征后的灰色关联方法,由于剔除了与工作量相关程度低的特征因子,进一步减小了工作量估算的平均误差率。

潮汐性质相似性判断的两个指标及其应用

提出采用时差法的相关系数和最小二乘潮位拟合法的推算中误差,作为潮汐性质相似性强弱判断的两个量化指标。根据实测潮汐数据进行了有关计算,结果表明这两个指标可以用于潮汐性质相似性强弱的快速判断,另外推算中误差还可以用于验潮站潮位数据缺测时推算精度的检验,建议在沿岸海道测量水位改正作业中综合使用。

基于指标权重的喀斯特地区水资源承载力评价

采用向量夹角余弦法和均方差法确定指标权重,运用多目标灰色关联投影法对贵州省各地区的水资源承载力进行了排序。

基于时空关系和关联规则挖掘的上下文信息缺失插补研究

上下文信息的缺失是上下文信息处理中不可避免的问题,缺失数据插补方法也是数据挖掘中的研究热点。但是,现有的缺失数据的插补方法不太适合上下文信息这一流数据形式,没有充分利用各传感器采集数据之间的关联性,而且在插补的过程中没有考虑传感器数据的时空关系。为了解决现存的缺失数据插补方法的缺陷和不足,该文提出了基于时空关系和关联规则挖掘的上下文信息缺失插补方法(STARM),对传感数据进行空间化和时间序列化,并生成强关联规则对缺失数据进行插补。最后,通过温度传感器采集数据验证了这一算法合理性和高效性。实验证明,该算法在上下文信息缺失估计的准确性要高于简单线性回归算法(SLR)和EM算法等,而且具有较小的时空开销,能够保证实时应用的服务质量(QoS)。

基于ANFIS的高速电主轴热误差建模研究

为了减少热误差对电主轴加工精度的影响,需要建立电主轴的热误差补偿系统,而补偿系统的性能主要取决于热误差预测模型的准确性和模型输入的温度质量。为保证输入模型的温度质量,采用模糊C-均值聚类和灰色关联分析相结合的综合算法优化温度测点,将温度测点的数量由10降至3个,以某台电主轴为试验对象,以电主轴转速为7 000 r/min的温度变量为输入,热误差变量为输出,采用自适应神经模糊推理系统建立了电主轴的热误差预测模型,并以转速为5 000和9 000 r/min的实验数据作为验证,结果表明,建立的ANFIS热误差预测模型可以有效地预测电主轴的热误差,预测模型的残差小于1μm。最后,与误差反向传播神经网络进行对比,结果表明该预测模型具有更高的精度和抗干扰能力。

中国北方地区水环境污染治理绩效综合评价建模研究

为辨别水污染治理工程存在的问题,衡量水污染整治效果,提出北方地区水环境污染治理绩效综合评价建模方法。通过平衡计分卡模型构建综合评价指标体系,计算指标灰色关联权重、层次权重、指标隶属度等,权重矩阵和隶属度矩阵相乘,得到水污染治理绩效模糊综合评价值,根据最大隶属度原则,确定绩效水平评价等级,完成评价模型建立。评价结果表明,治理工程绩效水平为优秀,技术经济效益、环境效益、工程建设与运行管理显著,社会效益存在欠缺,由实地调研数据可知,评价结果与实际情况相符。

THE INEFFICIENCY OF THE LEAST SQUARES ESTIMATOR AND ITS BOUND

It was suggested by Pantanen that the mean squared error may be used to measure the inefficiency of the least squares estimator. Styan[2] and Rao[3] et al. discussed this inefficiency and it's bound later. In this paper we propose a new inefficiency of the least squares estimator with the measure of generalized variance and obtain its bound.

Estimation of Population Variance Using the Coefficient of Kurtosis and Median of an Auxiliary Variable under Simple Random Sampling

In this study we have proposed a modified ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coefficient of kurtosis and median of an auxiliary variable x. The estimator’s properties have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some specified conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency. The developed estimator therefore is suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.

Quality analysis of crustal tilt and strain observations in China's earthquakes in 2014

This work analyzes the quality of crustal tilt and strain observations during 2014, which were acquired from 269 sets of ground tiltmeters and 212 sets of strainmeters. In terms of data quality, the water tube tiltmeters presented the highest rate of excellent quality,approximately 91%, and the pendulum tiltmeters and ground strainmeters yielded rates of81% and 78%, respectively. This means that a total of 380 sets of instruments produced high-quality observational data suitable for scientific investigations and analyses.

数控滚齿机工作台热-力变形分析及预测建模

为了减小滚齿机工作台变形对加工精度的影响,对工作台热、力变形进行了研究,提出一种基于子种群自适应思维进化算法优化反向传播(SAMEA-BP)神经网络的滚齿机工作台热-力变形预测方法。通过SAMEA对BP神经网络的初始值、权重和阈值等参量进行调整,有效提升了基于神经网络的热-力变形预测准确度。结合K均值聚类策略和灰色关联分析(GRA)对影响热误差的温度测点进行耦合性和关联度分析,将热误差输入变量从8个测点减少到3个;针对滚齿加工中切削力导致的工作台变形,利用机床主轴电流表征切削力,并作为预测模型的输入变量。试验结果表明:本文模型平均预测精度为95.1%,与其他模型进行的对比分析验证了本文SAMEA-BP模型的有效性和泛化性。

基于NLMS自适应滤波的近红外光谱去噪处理方法研究

为了去除直接采集的近红外(NIR)光谱中含有的噪声,将归一化最小均方(NLMS)自适应滤波方法引入到NIR光谱去噪领域中。以51份土壤样品的NIR光谱为研究对象,探讨NLMS自适应滤波方法在NIR光谱预处理中的应用,并将处理后的结果与土壤中有机质的含量相关联,建立模型。结果表明,通过NLMS自适应滤波去噪后的光谱,预测集的相关系数r由处理前的0.8284提高至0.9654,预测均方根误差(RMSEP)由处理前的0.3385降至0.1606。由此可见,NLMS自适应滤波对NIR光谱的去噪有显著效果,可以有效地提高光谱的分析精度和模型的稳健性,为NIR光谱的预处理提供了一种新方法。

中误差和邻近关系的多尺度面实体匹配算法研究

将中误差引入面实体匹配的过程,结合相邻面实体邻近聚集算法,提出一种基于中误差和邻近关系的面实体匹配算法,可以解决多尺度空间数据匹配的阈值大小和多对多关系难确定的问题。试验结果表明该方法具有良好的稳定性和可靠性。

CMA(p,q)型自适应算法的稳态性能分析

在数字通信中具有广泛应用的盲均衡恒模算法CMA(p,q),其参数p和q值一般选取为正整数1和2.为适应更一般环境,有必要将参数p和q值从整数推广至普通正实数.与传统的性能分析方法即对权值误差向量的协方差矩阵进行递归推导不同,基于能量守恒关系,直接对误差函数进行泰勒级数展开,并利用分离假设原理,推导出CMA(p,q)的通用稳态封闭解析表达式,并建立关于p,q值在可选择范围的理想的数学模型.仿真结果表明,复数或实数数据的仿真值和理论值均较为匹配.