Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm

Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm

During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.

基于KNN算法的教学质量评价模型建立

针对当前教学质量评价存在主观性较强的不足,基于K-最近邻(K-nearest neighbor,KNN)算法,提出教学质量评价模型.确立教学质量评价体系;以教学督导的评价数据为样本数据,通过交叉验证求解最近邻算法参数K的最佳值,从而建立教学质量评价模型.模型以专家数据为样本,评价精度高,评价结果具有较高的可靠性,能根据相关指标快速产生评价等级,提高了教学质量评价效率,使教学质量评价更加客观全面.

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.

Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm

Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy.

激光点云线性KNN算法FPGA实现及加速

针对三维激光点云线性K最近邻(K-nearest neighbor, KNN)搜索耗时长的问题,提出了一种利用多处理器片上系统(multi-processor system on chip, MPSoC)现场可编程门阵列(field-programmable gate array,FPGA)实现三维激光点云KNN快速搜索的方法。首先给出了三维激光点云KNN算法的MPSoC FPGA实现框架;然后详细阐述了每个模块的设计思路及实现过程;最后利用MZU15A开发板和天眸16线旋转机械激光雷达搭建了测试平台,完成了三维激光点云KNN算法MPSoC FPGA加速的测试验证。实验结果表明:基于MPSoC FPGA实现的三维激光点云KNN算法能在保证邻近点搜索精度的情况下,减少邻近点搜索耗时。

面向申威架构的KNN并行算法实现与优化

K近邻(KNN)是人工智能中最常用的分类算法,其性能提升对于海量数据的整理分析、大数据分类等任务具有重要意义。目前新一代神威超级计算机正处于应用发展的初始阶段,结合新一代申威异构众核处理器的结构特性,充分利用庞大的计算资源实现高效的KNN算法是海量数据分析整理的现实需求。根据SW26010pro处理器的结构特性,采用主从加速编程模型实现一种基础版本的KNN并行算法,其将计算核心传输到从核上,实现了线程级并行。分析影响基础并行算法性能的关键因素并提出优化算法SWKNN,不同于基础并行KNN算法的任务划分方式,SWKNN采用任务重划分策略,以避免冗余计算开销。通过数据流水优化、从核间通信优化、二次负载均衡优化等步骤减少不必要的通信开销,从而有效缓解访存压力并进一步提升算法性能。实验结果表明,与串行KNN算法相比,面向申威架构的基础并行KNN算法在SW26010pro处理器的单核组上可以获得最高48倍的加速效果,在同等数据规模下,SWKNN算法较基础并行KNN算法又可以获得最高399倍的加速效果。

多核CPU环境下的并行KNN算法设计

针对KNN算法计算比较耗时的问题,提出将计算任务分解为多个子任务,每个子任务分配给一个线程完成,通过多个线程的并行执行完成工作。将训练集读入一个二维数组,二维数组的每一行只分配给一个线程使用;每个新数据被同时广播给多个线程,每个线程计算该新数据在自己训练集中的最近邻,并将最近邻反馈给主程序;主程序收集每个线程返回的最近邻,以最近邻中的最佳近邻的类别作为新数据的类别。实验证明该并行设计方案充分利用计算资源,加快了计算速度。

A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning

Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.

基于EDA的加权KNN分类算法

针对传统K近邻(KNN)算法对不平衡数据集分类的不足,提出一种基于分布估计算法改进的加权KNN算法EDA-KNN。在没有先验知识的前提下,为了求解最优加权KNN算法的权重向量,构建矩阵结构种群。运用分布估计算法建立概率模型,进行采样、寻优等一系列操作,经过若干次迭代,最终获得使样本分类准确率达到最高的权重向量。通过对多个数据集进行分类,结果表明,EDA-KNN算法能够显著提升对于不平衡数据集分类的准确率,分类器性能稳定。

基于KNN算法的网络入侵检测技术开发

传统算法在网络入侵检测方面存在部分问题,为了进一步提升检测水平,在网络信息攻击手段日益增多的背景下,提出了一种基于最邻近结点(K-NearestNeighbor,KNN)算法的网络入侵检测技术方法。该方法将粒子优化解决局部极值问题,以实现改善网络入侵检测技术的目的。测试结果表明,基于KNN算法的网络入侵检测技术能够较好地识别攻击类型,其误检率显著优于Rabin-Karp、Boyer-Moore、Colussi这3种传统算法,验证了算法的有效性,能够较好地应用于网络入侵行为的预测,表现出良好的预测精度。

Characteristics,classification and KNN-based evaluation of paleokarst carbonate reservoirs:A case study of Feixianguan Formation in northeastern Sichuan Basin,China

The Feixianguan Formation reservoirs in northeastern Sichuan are mainly a suite of carbonate platform deposits.The reservoir types are diverse with high heterogeneity and complex genetic mechanisms.Pores,vugs and fractures of different genetic mechanisms and scales are often developed in association,and it is difficult to classify reservoir types merely based on static data such as outcrop observation,and cores and logging data.In the study,the reservoirs in the Feixianguan Formation are grouped into five types by combining dynamic and static data,that is,karst breccia-residual vuggy type,solution-enhanced vuggy type,fractured-vuggy type,fractured type and matrix type(non-reservoir).Based on conventional logging data,core data and formation microscanner image(FMI)data of the Qilibei block,northeastern Sichuan Basin,the reservoirs are classified in accordance with fracture-vug matching relationship.Based on the principle of cluster analysis,K-Nearest Neighbor(KNN)classification templates are established,and the applicability of the model is verified by using the reservoir data from wells uninvolved in modeling.Following the analysis of the results of reservoir type discrimination and the production of corresponding reservoir intervals,the contributions of various reservoir types to production are evaluated and the reliability of reservoir type classification is verified.The results show that the solution-enhanced vuggy type is of high-quality sweet spot reservoir in the study area with good physical property and high gas production,followed by the fractured-vuggy type,and the fractured and karst breccia-residual vuggy types are the least promising.

基于改进KNN的不均衡信息文本分类算法

针对常规文本分类算法存在文本特征提取不全面的问题,提出基于改进K近邻(K-Nearest Neighbor,KNN)的不均衡信息文本分类算法。首先,通过文本分词与去停用词两个步骤,对不均衡信息文本进行预处理,避免无用数据对分类结果产生干扰。其次,利用互信息特征提取方法,提取不均衡信息文本特征,获取文本特征词与类别之间的相关程度。最后,利用改进KNN原理对待测不均衡信息文本数据进行邻近聚类,设计文本分类算法。实验结果表明,该算法的分类查准率始终在98%以上,优于对照组。

改进型加权KNN算法的不平衡数据集分类

K最邻近(KNN)算法对不平衡数据集进行分类时分类判决总会倾向于多数类。为此,提出一种加权KNN算法GAK-KNN。定义新的权重分配模型,综合考虑类间分布不平衡及类内分布不均匀的不良影响,采用基于遗传算法的K-means算法对训练样本集进行聚类,按照权重分配模型计算各训练样本的权重,通过改进的KNN算法对测试样本进行分类。基于UCI数据集的大量实验结果表明,GAK-KNN算法的识别率和整体性能都优于传统KNN算法及其他改进算法。

基于KNN的特征自适应加权自然图像分类研究

针对自然图像类型广泛、结构复杂、分类精度不高的实际问题,提出了一种为自然图像不同特征自动加权值的K-近邻(K-nearest neighbors,KNN)分类方法。通过分析自然图像的不同特征对于分类结果的影响,采用基因遗传算法求得一组最优分类权值向量解,利用该最优权值对自然图像纹理和颜色两个特征分别进行加权,最后用自适应加权K-近邻算法实现对自然图像的分类。实验结果表明,在用户给定分类精度需求和低时间复杂度的约束下,算法能快速、高精度地进行自然图像分类。提出的自适应加权K-近邻分类方法对于门类繁多的自然图像具有普遍适用性,可以有效地提高自然图像的分类性能。

KNN算法的数据优化策略

为了解决基于KNN(K-Nearest Neighbors)算法的非参数回归短时交通状态预测模型执行效率低的问题,提出了KNN算法的数据优化策略。通过对交通状态时空特性的研究,采用层次化对象构造交通状态向量,并根据交通状态的自重复性对历史样本数据库进行数据压缩。实验证明,优化策略提高了KNN算法的执行效率,经过压缩后的数据存取时间比压缩前缩短了8.66%。

用于不均衡数据集分类的KNN算法

针对KNN在处理不均衡数据集时,少数类分类精度不高的问题,提出了一种改进的算法G-KNN。该算法对少数类样本使用交叉算子和变异算子生成部分新的少数类样本,若新生成的少数类样本到父代样本的欧几里德距离小于父代少数类之间的最大距离,则认为是有效样本,并把这类样本加入到下轮产生少数类的过程中。在UCI数据集上进行测试,实验结果表明,该方法与KNN算法中应用随机抽样相比,在提高少数类的分类精度方面取得了较好的效果。