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.

An Improved Kernel K-Mean Cluster Method and Its Application in Fault Diagnosis of Roller Bearing

For the kernel K-mean cluster method is run in an implicit feature space, the initial and iterative cluster centers cannot be defined explicitly. Against the deficiency of the initial cluster centers selected in the original space discretionarily in the existing methods, this paper proposes a new method for ensuring the clustering center that virtual clustering centers are defined in the feature space by the original classification as the initial cluster centers and the iteration clustering centers are ensured by the further virtual classification. The improved method is used for fault diagnosis of roller bearing that achieves a good cluster and diagnosis result, which demonstrates the effectiveness of the proposed method.

Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact

The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.

基于改进的密度峰值算法的K-means算法

针对传统K-means算法存在的随机选取初始聚类中心和类簇数目需要人为选定,从而导致聚类结果不稳定,容易陷入局部最优解的问题,文章提出了一种基于改进的密度峰值算法（DPC）的K-means算法,该算法首先采用改进的DPC算法来选取初始聚类中心,弥补了K-means算法初始聚类中心随机选取导致易陷入局部最优解的缺陷;其次运用K-means算法进行迭代,并且引入熵值法计算距离优化聚类。在UCI数据集上的实验表明,该算法得到较好的初始聚类中心和较稳定的聚类结果,并且收敛速度也较快,证明了该算法的可行性。

一种改进K-Means算法的服务聚类方法

聚类方法能够提高Web服务检索的能力,针对传统的K-Means聚类算法聚类时间长的缺陷,文中提出了一种改进的K-Means服务聚类方法,并进行了有效性验证,在利用API服务数据集上进行实验,其结果表明:改进的K-Means服务聚类的方法降低了服务聚类的时间复杂度。

基于同态滤波和改进K-means的苹果分级算法研究

针对苹果在分级的过程中,光线不均所导致的表面反光和阴影问题,利用同态滤波和改进的K-means算法予以解决。同态滤波前,将苹果图像由RGB空间转换到HSV空间,再对HSV空间的V分量进行同态滤波增强,最大限度地削弱光线不均带来的影响;对传统K-means聚类算法,新增加距离度量方法、确定聚类数目和初始中心点,能较好地去除苹果阴影对图像分割的影响。从大小、果形、质量、颜色、缺陷5个方面对陕北富县的秦冠苹果进行分级,分级成功率达到97%。利用同态滤波算法结合改进的K-means算法来对苹果图像进行处理,能够大大提高苹果分级的准确性。

Application of K-means and PCA approaches to estimation of gold grade in Khooni district(central Iran)

Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.

基于改进K-means与组合赋权的智能变电站二次系统信息质量评价

随着变电站的智能化和信息化水平突飞猛进,变电站产生和存储的数据越来越多,数据异常和数据缺失等数据质量问题逐渐突出。作为提高数据质量的基础和前提,智能变电站二次系统数据质量评价尤为重要。文章提出一种基于改进K-means和组合赋权法的智能变电站二次系统信息质量评价方法,该方法首先通过正确性、完善性等6个指标对智能变电站二次系统的信息多层次、全方位分析,解决传统智能变电站二次系统信息质量评价指标单一、评价体系简单的问题;其次,针对智能变电站二次系统数据处理的实时性以及准确性的问题,采用改进K-means算法对数据进行预处理;再次,采用变异系数法与序关系法对评价指标进行组合赋权,使权重既带有客观因素,也包含专家的主观判断,从而使评价结果真实可靠;最后,以仿真数据为例说明该方法的有效性。

An improved probabilistic load flow in distribution networks based on clustering and Point estimate methods

Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.

Three Indication Variables and Their Performance for the Troubled-Cell Indicator using K-Means Clustering

In Zhu,Wang and Gao(SIAM J.Sci.Comput.,43(2021),pp.A3009–A3031),we proposed a new framework of troubled-cell indicator(TCI)using K-means clustering and the numerical results demonstrate that it can detect the troubled cells accurately using the KXRCF indication variable.The main advantage of this TCI framework is its great potential of extensibility.In this follow-up work,we introduce three more indication variables,i.e.,the TVB,Fu-Shu and cell-boundary jump indication variables,and show their good performance by numerical tests to demonstrate that the TCI framework offers great flexibility in the choice of indication variables.We also compare the three indication variables with the KXRCF one,and the numerical results favor the KXRCF and the cell-boundary jump indication variables.

High-quality development of China’s power industry: Measurement, spatial pattern, and improvement paths

As the largest manufacturing country,China is striving to improve the development quality of its power industry with the goal of Carbon Peaking and Carbon Neutrality,in order to sustain its high-quality economic growth.In this regard,it is of importance to reveal both the regional development level of China’s power sector and its characteristics in terms of inspiring the next improvement direction.Motived by this purpose,this paper constructs an evaluation indicator system from three dimensions at the province level based on the connotation of high-quality development of the power industry(HDPI).Next,it calculates the HDPI indexes of 30 provinces and explore their development trend and spatial pattern.The results indicate that the total comprehensive performance of all regions was improved in general in the recent decade,but the spatial distribution characteristics of clean,low-carbon,safe and efficient are different.In the aspects of improvement space in future,not only do actively ameliorate the related management regimes or technical fields so as to improve the corresponding indicators’value,but also passively rely on the macro-development such as China’s urbanization level improvement,technological level improvement,and industrial structure upgrading as usual.

中国省际食用菌产业集聚及空间差异化研究

基于2012—2021年我国25个省(市、自治区)食用菌相关统计数据,采用改良区位熵值法探究各地区食用菌产业集聚度及集聚变动趋势,并通过探索性空间数据分析法进一步对集聚度进行了空间差异化分析,以期为培育壮大食用菌产业并使之成为未来农业发展新增长点提供参考依据。结果表明:1)基于时序视角下,食用菌产业集聚变动趋势差异较大,东北地区的食用菌产业集聚水平始终最高,而河南、福建、黑龙江、吉林和江西是我国食用菌产业优势区域;基于空间视角下,食用菌产业集群呈现从东部地区开始向西南地区和西北地区扩展的转移态势。2)食用菌产业集聚整体上呈下降趋势,食用菌产业集聚空间自相关和集聚特征处于不断弱化的过程,集聚热点区域的总体格局保持稳定,总体呈现“东热”的空间格局,吉林、福建一直处于相对稳定的热点区域。我国省际食用菌产业集聚变动趋势差异较大,空间差异化显著,应培育壮大食用菌产业,加强结构性改革,积极创建优势特色食用菌产业集群,以促进食用菌产业高质量发展。

A privacy-preserving vehicle trajectory clustering framework

As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage.Therefore,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy.We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means algorithm.In the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the client.The IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle trajectory.IKV is compared to numerous clustering methods on three datasets.In the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the experiments.Furthermore,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations.These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

张家口柴宣盆地浅层地下水水化学特征及水质评价

地下水作为张家口地区的主要供水水源,近年来受人类活动影响水质有恶化的趋势,影响了当地的用水安全,确定地下水化学特征与水质状况对水资源合理利用具有重要意义。基于2020年水质数据,选取Ca^(2+)、Mg^(2+)、K^(+)、Na^(+)、HCO^(-)_(3)、TH、pH、TDS、SO_(4)^(2-)、Cl^(-)、Al^(3+)、NO^(-)_(3)、F^(-)、Cr^(6+)等水质因子,通过数理统计法、Piper三线图、Gibbs图和岩石风化端元图对张家口柴宣盆地地区浅层地下水水化学特征进行分析,并采用模糊综合评价和改进内梅罗指数法对水质进行评价。结果表明:研究区地下水是Ca^(2+)和HCO^(-)_(3)为主的弱碱性淡水,微硬水、硬水和极硬水分别占39.51%、34.57%和25.93%;沿地下水流向,地下水化学类型由HCO_(3)-Ca·Mg、HCO_(3)-Na、SO_(4)·Cl-Na型转变为HCO_(3)-Ca·Mg、HCO_(3)-Na、SO_(4)·Cl-Ca·Mg型,水化学组分的空间分布特征主要受到硅酸盐、碳酸盐的风化溶解和人类活动影响;地下水水质整体满足III类标准,但部分地区水质较差;沿着地下水流向,水质逐渐变差,主要受原生地质环境和工业、农业污染的影响。

Incident Detection Based on Differential Analysis

Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant challenge, as the need for robust security measures becomes increasingly imperative. This paper presented an innovative method based on differential analyses to detect abrupt changes in network traffic characteristics. The core concept revolves around identifying abrupt alterations in certain characteristics such as input/output volume, the number of TCP connections, or DNS queries—within the analyzed traffic. Initially, the traffic is segmented into distinct sequences of slices, followed by quantifying specific characteristics for each slice. Subsequently, the distance between successive values of these measured characteristics is computed and clustered to detect sudden changes. To accomplish its objectives, the approach combined several techniques, including propositional logic, distance metrics (e.g., Kullback-Leibler Divergence), and clustering algorithms (e.g., K-means). When applied to two distinct datasets, the proposed approach demonstrates exceptional performance, achieving detection rates of up to 100%.

基于改进模拟退火算法的防空武器集群打击目标动态分配

传统的防空武器集群打击目标动态分配方法由于设定的约束条件不够全面,导致该方法在分配效果上表现不佳;因此,文章提出了一种基于改进模拟退火算法的防空武器集群打击目标动态分配方法;该方法综合考虑作战效果、作战成本和作战效率等多个因素,确定了相应的目标函数,并设定了相应的约束条件;通过计算打击目标的状态转移概率,并对打击目标的状态准则进行判定,定义了打击目标的状态转移方程;在改进的模拟退火算法的指导下,确定了最优的打击目标分配方案;实验结果分析表明,与传统的防空武器集群打击目标动态分配方法相比,文章设计的基于改进模拟退火算法的方法在实际应用中表现出较低的资源损耗量,均不高于4%,且具有良好的分配效果。

Improved algorithm of cluster-based routing protocols for agricultural wireless multimedia sensor networks

Low Energy Adaptive Clustering Hierarchy(LEACH)is a routing algorithm in agricultural wireless multimedia sensor networks(WMSNs)that includes two kinds of improved protocol,LEACH_D and LEACH_E.In this study,obstacles were overcome in widely used protocols.An improved algorithm was proposed to solve existing problems,such as energy source restriction,communication distance,and energy of the nodes.The optimal number of clusters was calculated by the first-order radio model of the improved algorithm to determine the percentage of the cluster heads in the network.High energy and the near sink nodes were chosen as cluster heads based on the residual energy of the nodes and the distance between the nodes to the sink node.At the same time,the K-means clustering analysis method was used for equally assigning the nodes to several clusters in the network.Both simulation and the verification results showed that the survival number of the proposed algorithm LEACH-ED increased by 66%.Moreover,the network load was high and network lifetime was longer.The mathematical model between the average voltage of nodes(y)and the running time(x)was concluded in the equation y=−0.0643x+4.3694,and the correlation coefficient was R2=0.9977.The research results can provide a foundation and method for the design and simulation of the routing algorithm in agricultural WMSNs.

改进平均间隙法在某电厂沉降观测中的应用研究

用于检验基准网稳定性的平均间隙法在寻找不稳定点时存在过程复杂、计算量大、检验对象受平差基准影响等不足。为解决这些问题,提出一种结合密度峰聚类的改进平均间隙法,并应用于某火电厂沉降监测。应用情况表明,与传统平均间隙法相比,改进平均间隙法能够正确检验出不稳定点,计算复杂度低,检验效率更高,结果的可靠性更高。

矿用卡车驾驶员操作水平的评价方法研究

为提高驾驶员操作水平,提出了一套完整的驾驶员操作水平评价方法。运用物联网数据采集技术,实现了对卡车运输数据的动态采集;提出了安全、能耗和效率3个驾驶员操作评价一级指标,并建立了相应的二级评价指标;采用改进的主成分分析法和K-means聚类算法对实验样本进行模型构建。构建的评价模型能够有效地评价驾驶员的操作水平,得到的驾驶员操作标准参考值可为提高驾驶管理水平和操作技术提供可靠的参考。

基于改进DBSCAN省级电力物资仓库聚类的配送车辆路径优化研究

鉴于电力物资仓库分布点过多且较为分散,其多起点路径配送优化问题比较复杂,文中提出了一种改进DBSCAN聚类算法来简化电力物资多仓库配送车辆路径的两阶段方法。首先,将区域所有仓库进行聚类划分,得到若干个仓库簇,由此将多起点路径配送优化问题转化为多个仓库簇的单起点路径配送优化问题。然后,使用改进C-W法对模型进行求解。最后,以浙江省电力物资仓库作为配送实例,验证了文中所提两阶段方法及算法的可用性和可行性。