基于改进K-means算法的物流配送中心选址研究

针对传统K-means算法需要主观设定K值及无法处理类别型数据问题,文章运用肘部法及轮廓系数法确定合理K值,对类别型数据采取独热编码(One-Hot Encoding)转换为可以处理的连续型数据,并将其运用到在物流配送中心选址中;并综合考虑多种类别的影响因素,构建了相应的影响因素指标体系,提出的模型能够识别输入数据的数值型及类别型数据,实现样本的有效聚类。相关的案例分析结果表明,相比传统K-means聚类,文章的改进K-means算法选址结果可使物流总成本降低8.76%,运营成本降低14.85%,固定成本降低8.09%,效果显著。

改进k-means的多域光纤通信非线性失真补偿方法

为了解决光纤通信信号在传输过程中受到非线性影响而产生的失真问题,提高光纤通信系统的稳定性,提出了改进k-means的多域光纤通信非线性失真补偿方法。构建多域光纤通信传输模型,在传输端利用波长转换器将输入信号传输到光纤,结合干扰原理线性化脉冲恢复光信号。以信噪比描述光纤通信的色散特性,明确信号交互出现非线性失真变化。通过Dijkstra方法改进k-means方法,解调失真星座,避免聚类陷入局部最优,使全部簇信号尽可能接近原始调制中心,实现失真补偿。实验结果表明:利用所提方法对光纤通信非线性失真进行补偿后,聚类效果较佳,信息误码率可降至10^(-7),有效减少了网络传输消耗,提高光纤通信信号质量。

基于改进K-means的局部离群点检测方法

离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。

一种基于K-means聚类算法的沙尘天气客观识别方法

鉴于以往基于污染物浓度时间序列进行分析的沙尘天气识别方法在判断标准上存在一定的主观性,本文提出一种基于K-means聚类算法的沙尘天气客观识别方法。本方法利用环境监测总站的PM2.5和PM10小时浓度资料进行聚类,首先选取最优的分类数目K进行聚类,其次对聚类结果中离散程度较高的类别进行再次聚类,直到无需分类。将本方法应用于西安市2018年2~4月沙尘天气的识别中,结果表明,本方法可有效识别主要沙尘天气。此外,利用本方法可得到沙尘天气典型特征:PM2.5占PM10浓度的比例小于43.5%、PM10浓度高于228μg/m^(3,)符合沙尘天气期间PM10浓度较高且以粗颗粒物为主的物理特征。总体上看,本方法物理基础清晰,可操行性强,适用于大规模数据处理,具有较好的实用价值和应用前景。

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

融合充放电曲线特征与改进K-means聚类的退役锂电池分选方法

为提高退役锂电池分选重组的一致性,提出一种融合电压曲线与能量曲线的数值特征与形态特征,并运用欧氏距离和形态距离进行K-means聚类的分选方法。通过试验获取退役锂电池充放电曲线,融合电压曲线和能量曲线作为分选依据;采用欧式距离度量融合曲线的数值差异;利用分位数方法将融合曲线转化为描述曲线形态变化的特征序列,运用最长公共子序列算法提取特征序列的形态距离用来度量融合曲线的形态差异;以融合曲线的欧式距离和特征序列的形态距离为度量判据,采用改进K-means聚类算法对退役锂电池进行聚类。结果表明:相较于电压曲线或容量曲线分选,采用融合曲线分选,容量、充电电压、放电电压一致性最大提高约23%、93%、16%。相较于欧式距离方法,采用改进K-means算法,容量、充电电压、放电电压一致性最大分别提高了约67%、40%、51%。

基于K-Means聚类算法的井下电缆双端在线局放定位方法

常规的电缆局放定位方法以反射信号识别与定位为主,时间同步偏差相对较大,影响最终的局放定位精准度,因此设计了基于K-Means聚类算法的井下电缆双端在线局放定位方法。该方法通过提取井下电缆双端行波模量特征,将井下电缆局放信号进行相模变换,分析相应电荷气隙平衡条件,获取更加准确的双端局放位置。基于K-Means算法构造电缆在线局放定位聚类中心,将空间距离相似的电缆进行局放判断,排除异常定位数据对聚类结果的影响,从而减小定位误差。采用对比实验验证了该方法的定位精准度高,能应用于实际生活中。

基于K-means聚类及模糊判别的卷烟包灰性能综合评价方法

为实现卷烟包灰性能的综合评价和评价结果具象化,以49个卷烟的灰色、裂口率、缩灰率、碳线宽度、碳线整齐度测定结果为原始变量,先运用K-means聚类、模糊判别法将原始变量转换为具象化的得分数据,再运用Critic赋权法赋予各项指标权重,建立了一种卷烟包灰性能综合评价方法。结果表明:将原始变量转换成区间为60~100、平均值在80左右的得分,可使评价结果具象化且更加符合认知习惯;5项指标的权重由高到低依次为裂口率(0.27)>缩灰率(0.25)>灰色(0.18)>碳线整齐度(0.16)>碳线宽度(0.14);卷烟包灰性能可划分为优、良、差三档,各档得分区间依次为(85,100]、[75,85]、[60,75);不同档次代表性卷烟的灰柱视觉效果对比结果证明,综合得分可客观反映卷烟包灰性能的优劣。

基于K-means聚类的相控阵任意形状波束子阵综合方法

传统相控阵由于设计复杂性和成本过高,已经不能满足日益增长的应用需求,子阵划分等技术较好地解决了这个问题。本文提出一种任意形状波束相控阵子阵综合方法,在激励匹配策略下,将子阵综合问题转化为一个子阵布局优化问题,而子阵的复激励可解析地从参考阵列激励计算得到。利用一种无监督聚类K-means方法对子阵布局优化问题进行求解,该方法能同时对子阵的激励幅相进行优化,增加了子阵综合的自由度和灵活性。在任意形状波束子阵综合数值算例中,通过与传统智能优化方法在方向图逼近、激励匹配代价函数、阵列性能参数及计算效率等方面的比较,验证了所提方法的有效性。

基于K-means的多级迭代分区坐标转换方法研究

不同于传统坐标转换,大区域坐标转换通常采用分区转换的方法来保证精度,不同的分区策略对最后的转换精度有很大的影响。因此,大区域坐标转换分区方法的研究有着重要的意义。在分析大区域坐标转换特性的基础上,重点探讨了K-means算法的适用性,提出了一种基于K-means的多级迭代分区坐标转换方法。通过实例验证了方法的可行性,既适应于不同面积、不同坐标点分布的情况,又能提高坐标转换的精度。

Fast-moving target tracking based on mean shift and frame-difference methods

The mean shift tracker has difficulty in tracking fast moving targets and suffers from tracking error accumulation problem. To overcome the limitations of the mean shift method, a new approach is proposed by integrating the mean shift algorithm and frame-difference methods. The rough position of the moving tar- get is first located by the direct frame-difference algorithm and three-frame-difference algorithm for the immobile camera scenes and mobile camera scenes, respectively. Then, the mean shift algorithm is used to achieve precise tracking of the target. Several tracking experiments show that the proposed method can effectively track first moving targets and overcome the tracking error accumulation problem.

基于K-Means聚类的思政教育资源个性化推荐

思政教育可以体现中国特色社会主义本质要求,因此研究基于K-Means聚类的思政教育资源个性化推荐方法。首先,根据两组相邻用户之间的共同喜好,划分思政教育资源个性化推荐等级。其次,选择协同过滤算法归一化样本数据,计算相似度制定用户偏好,构建思政教育资源推荐模型。最后,基于K-Means聚类算法给定目标函数,建立个性化推荐流程,实现思政教育资源推荐,完成方法设计。实践表明,该方法既能够满足思政教育资源的匹配,又能够保证用户对思政教育资源的喜爱程度,具有实际的应用效果。

Calculation of Significant Wave Height Using the Linear Mean Square Estimation Method

Significant wave height is an important criterion in designing coastal and offshore structures.Based on the orthogonality principle, the linear mean square estimation method is applied to calculate significant wave height in this paper.Twenty-eight-year time series of wave data collected from three ocean buoys near San Francisco along the California coast are analyzed.It is proved theoretically that the computation error will be reduced by using as many measured data as possible for the calculation of significant wave height.Measured significant wave height at one buoy location is compared with the calculated value based on the data from two other adjacent buoys.The results indicate that the linear mean square estimation method can be well applied to the calculation and prediction of significant wave height in coastal regions.

A simplified two-dimensional boundary element method with arbitrary uniform mean flow

To reduce computational costs, an improved form of the frequency domain boundary element method（BEM） is proposed for two-dimensional radiation and propagation acoustic problems in a subsonic uniform flow with arbitrary orientation. The boundary integral equation（BIE） representation solves the two-dimensional convected Helmholtz equation（CHE） and its fundamental solution, which must satisfy a new Sommerfeld radiation condition（SRC） in the physical space. In order to facilitate conventional formulations, the variables of the advanced form are expressed only in terms of the acoustic pressure as well as its normal and tangential derivatives, and their multiplication operators are based on the convected Green＇s kernel and its modified derivative. The proposed approach significantly reduces the CPU times of classical computational codes for modeling acoustic domains with arbitrary mean flow. It is validated by a comparison with the analytical solutions for the sound radiation problems of monopole,dipole and quadrupole sources in the presence of a subsonic uniform flow with arbitrary orientation.

Geochemical and Geostatistical Studies for Estimating Gold Grade in Tarq Prospect Area by K-Means Clustering Method

Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.

改进萤火虫算法与 K-means 算法结合的 配电网负荷聚类特性分析

负荷聚类特性分析是实现配电网的定制电力、高品质供电、高可靠性供电的重要基础.然而现有的Kmeans聚类分析方法,受限于数据样本集和聚类初始中心的选取等,会出现因初始中心不同造成聚类结果差异大的问题.为此,针对配电网负荷数据特点,提出一种基于改进萤火虫算法和K-means算法结合的配电网负荷聚类特性分析方法.利用萤火虫优化算法全局搜索能力强的优势,考虑类内相似度和类间差异度,寻优K-means算法初始中心,使聚类结果的聚类有效性指标取得最小值;进一步针对萤火虫算法在处理负荷数据时的弱点,通过密度法为萤火虫算法加入优秀初代个体,改进吸引公式以及个体间概率吸引移动的方式优化迭代过程中的个体移动方式,加快萤火虫算法前期收敛速度,并实现后期稳定收敛,算法更快地接近极值,计算速度更快.算例验证了本文所提算法的聚类有效性,并针对某配电台区电力负荷数据,寻得K-means算法最优初始中心,使得聚类结果的戴维森堡丁指标(Davies-Bouldin index,DBI)最小,负荷聚类结果类内差异小,类间差异大,最终聚类中心的特征代表性强,为负荷类型划分、聚类特性分析提供重要依据,为需求侧差异化电力服务定制奠定有力基础.

RESEARCH ON THEORETIC EVIDENCE AND REALIZATION OF DIRECTLY-MEAN EMD METHOD

Emprical mode decomposition(EMD) is a method and principle of decomposing signal dealing with Hilbert-Huang transform (HHT) in signal analysis, while directly-mean EMD is an improved EMD method presented by N.E.Huang, the inventor of HHT, which is aimed at solving the problems of EMD principle. Although the directly-mean HMD method is very remarkable with its advantages and N. E. Huang has given a method to realize it, he did not find the theoretic evidence of the method so that the feasibility of the idea and correctness of realizing the directly-mean EMD method is still indeterminate. For this a deep research on the forming process of complex signal is made and the involved stationary point principle and asymptotic stationary point principle are demonstrated, thus some theoretic evidences and the correct realizing way of directly-mean EMD method is firstly presented. Some simulation examples for demonstrating the idea presented are given.

STUDY ON MEAN ACTIVITY C0EFFICIENT OF 5,10,15,20—TETRAKIS PORPHYRIN SODIUM BY FREEZING—POINT DEPRESSION METHOD

The mean activity coefficient of 5, 10,15 , 20-tetrakis (P-methoxyl-O-sulfophenyl)porphyrin sodium in dilute aqueous solution has been determined in the modality range 0. 00547-0. 08871 mol · kg-1at 273. 2 K by the freezing-point depression method . The results of γ± are 0. 9945-0. 7695, it is in close agreement with that by isopiestic method.

Mean Square Numerical Methods for Initial Value Random Differential Equations

In this paper, the random Euler and random Runge-Kutta of the second order methods are used in solving random differential initial value problems of first order. The conditions of the mean square convergence of the numerical solutions are studied. The statistical properties of the numerical solutions are computed through numerical case studies.