Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

k 近邻居(k-NN ) 方法被评估预言流入的流动率和四水质量，也就是化学的氧需求(货到付款) ，推迟的固体(SS ) ，全部的氮(T-N ) 和总数在废水处理植物(WWTP ) 的磷(T-P ) 。为在干燥、湿的天气条件下面决定最近的邻居(NN ) 的数字的搜索范围和途径开始基于根均方差(RMSE ) 被优化。为考虑数据尺寸的最佳搜索范围是一年。平方基于根(SR ) 途径比距离优异基于因素(DF ) 在决定 NN 的适当数字来临。然而，两条途径的结果稍微变化了取决于水质量和天气条件。流入的流动率精确地在测量价值的一标准差以内被预言。流入的水质量很好在湿、干燥的天气条件下面与吝啬的绝对百分比错误(MAPE ) 被预言。为七天的预言，在预兆的精确性的差别处于干燥天气条件并且稍微是不到 5% 处于湿天气条件更坏。总的来说， k-NN 方法被验证为预言 WWTP 流入的特征有用。

Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.

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.

GHM-FKNN:a generalized Heronian mean based fuzzy k-nearest neighbor classifier for the stock trend prediction

Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.

A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.

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.

基于不规则区域划分方法的k-Nearest Neighbor查询算法

随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细介绍了一种基于不规则区域划分方法的改进型k NN查询算法,并利用对大规模数据集进行分布式并行计算的模型Map Reduce对该算法加以实现.实验结果与分析表明,Map Reduce框架下基于不规则区域划分方法的k NN查询算法可以获得较高的数据处理效率,并可以较好的支持大数据环境下数据的高效查询.

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).

Pruned fuzzy K-nearest neighbor classifier for beat classification

Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.

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.

Propagation Path Loss Models at 28 GHz Using K-Nearest Neighbor Algorithm

In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach.

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes.Because of its operation,the application of this classification may be limited to problems with a certain number of instances,particularly,when run time is a consideration.However,the classification of large amounts of data has become a fundamental task in many real-world applications.It is logical to scale the k-Nearest Neighbor method to large scale datasets.This paper proposes a new k-Nearest Neighbor classification method(KNN-CCL)which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts.The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters.The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets.Finally,sets of experiments are conducted on the UCI datasets.The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.

A case-based reasoning method of recognizing liquefaction pits induced by 2021 M_(W) 7.3 Madoi earthquake

Earthquake-triggered liquefaction deformation could lead to severe infrastructure damage and associated casualties and property damage.At present,there are few studies on the rapid extraction of liquefaction pits based on high-resolution satellite images.Therefore,we provide a framework for extracting liquefaction pits based on a case-based reasoning method.Furthermore,five covariates selection methods were used to filter the 11 covariates that were generated from high-resolution satellite images and digital elevation models(DEM).The proposed method was trained with 450 typical samples which were collected based on visual interpretation,then used the trained case-based reasoning method to identify the liquefaction pits in the whole study area.The performance of the proposed methods was evaluated from three aspects,the prediction accuracies of liquefaction pits based on the validation samples by kappa index,the comparison between the pre-and post-earthquake images,the rationality of spatial distribution of liquefaction pits.The final result shows the importance of covariates ranked by different methods could be different.However,the most important of covariates is consistent.When selecting five most important covariates,the value of kappa index could be about 96%.There also exist clear differences between the pre-and post-earthquake areas that were identified as liquefaction pits.The predicted spatial distribution of liquefaction is also consistent with the formation principle of liquefaction.

2012—2022年山西省3A级以上景区空间分布特征及影响因素研究

山西省具有丰富的旅游资源,但11个地市级旅游资源赋存和旅游经济发展不协调.以2012—2022年山西省3A级以上景区为研究对象,运用核密度估计分析、标准差椭圆、平均最近邻指数、不平衡指数和缓冲区分析等空间分析方法和数理统计分析方法,重点研究山西省3A级以上旅游景区的空间分布特征及其影响因素,从而为山西省合理有效地开发、配置旅游资源提供数据支撑.结果表明:(1)通过标准差椭圆和平均最近邻指数分析得出,山西省3A级以上旅游景区总体分布呈南北延伸,空间结构类型为集聚型,3A级以上旅游景区逐渐向晋南、晋东南发展;(2)通过核密度分析发现,各地级市3A级以上景区分布不均衡,景区空间分布密度区域有明显差异,高密度区域分布在太原市周边;(3)交通条件、城市等级、水系、地形地貌、经济水平均是影响3A级以上旅游景区空间分布的重要因素.

基于加权实例推理的缓倾斜综采工作面液压支架选型研究

为实现地质构造简单的缓倾斜综采工作面液压支架智能化选型,提出了一种基于加权实例推理的液压支架选型方法。首先,建立了液压支架选型实例库;其次,采用粗糙集理论和序关系分析法进行权重构造;另外,将液压支架的条件属性分为3种类型计算相似度;最后通过匹配实例选型。以某煤矿选型方案为例,并以50组液压支架的属性数据进行验证。结果表明,该方法的准确率为88%,能够为液压支架的智能化选型提供较好的参考依据。

基于熵值法的人工林林木邻体结构优化方法

我国人工林普遍存在生产力低、树种单一、结构不合理的问题,亟须开展有效的林分改造和群落重建工作。以黄龙山油松人工林为对象,基于熵值法和贪婪算法对油松人工林林木邻体结构开展模拟优化调整。结果表明:(1)人工林样地在模拟优化前,其树种属性、空间距离和大小分化等方面均与天然林样地存在较大差异。(2)基于熵值法的邻体结构模拟优化模型可以有效地优化调整人工林中林木的邻体结构,改善人工林的胸径、树高和单木地上生物量等的分布状态,有利于油松人工林向近自然化方向演替。(3)基于熵值法的优化调整方法具有良好的适用性,应用范围广泛,权重指标客观,计算逻辑合理,是人工林结构调整的新方法。

孝感市院前急救医疗服务空间可及性研究

目的 探究孝感市院前急救医疗服务的可及性特征,为优化有限急救医疗资源配置提供依据。方法 以孝感市内2020年所有提供院前急救医疗服务的急救站为供方,孝感市内所有人口为需方。采用最短路径分析计算所有供方急救医疗服务设施点到达需方的最短到达时间。结果 孝感市院前急救医疗服务平均最短到达时间为46.32 min,覆盖人口为80%的平均最短到达时间为66.08 min,98.53%人口可在2 h内获得院前急救医疗服务。在各辖区中,孝南区院前急救平均最短到达时间最短为6.48 min,大悟县最长为85.38 min。结论 孝感市的院前急救医疗服务的空间可及性较差,内部各辖区可及性存在较大差异。相较于其他各县(市),医疗资源丰富、人口密集的孝南区院前急救医疗服务可及性较好。对院前急救资源空间可及性较为薄弱的区域,实施合理布局增设院前急救医疗服务机构、增强交通网络等综合策略,可改善院前急救医疗服务可及性。

创建neighbor-joining进化树的快速算法(英文)

为了改善Saitou和Nei提出的neighbor-joining进化树算法(SN)及Studier和Keppler的改进算法(SK),降低计算的时间复杂度,设计了一种快速算法.该算法涉及3种技术第一,引入一个线性数组A[N],用于存储距离矩阵每一行的值,以减少许多重复计算;第二,A[i]的值在算法开始时全部计算,在迭代步中间只进行更新3个变化的值;第三,设计了一个紧凑的公式用于计算OTUs之间的边长,并对该公式进行了证明.实验结果表明随着节点数的增多,该算法比SN算法快几十倍到上百倍,比SK算法快2倍以上;在一台桌面计算机上,该算法能在3min左右创建具有2000个节点的进化树.以空间换时间,减少最内层循环的计算量是设计多重循环算法的基本思路.

航空发动机后向RCS统计特性分析方法

为解决采用传统固定带宽核密度估计方法分析雷达散射截面(RCS)统计特性时精度低的问题,设计了K最近邻法计算Epanechnikov核密度估计的动态窗宽。以每个相邻样本的欧氏距离判断样本局部密度,通过样本点与最近邻的距离来调整核函数的窗宽以完成核密度估计,并将其用于发动机后向RCS的统计特性分析。采用改进的Epanechnikov核密度估计与传统核密度估计,对服从固定分布的4种RCS随机样本点的累积概率密度函数进行拟合,以验证算法的精度。结果表明:改进的Epanechnikov核密度估计的均方根误差比传统核密度估计的分别减小31.2%、38.8%、38.1%、31.9%。结合第2代RCS统计特性分析模型,以Kolmogorov-Smirnov拟合精度检验为拟合指标,应用改进的Epanechnikov核密度估计计算发动机后向RCS的统计特性并对其规律进行分析可知,对数正态分布更符合C波段和X波段的HH和VV极化的统计特性分布;卡方分布更符合C波段以及Ku波段的HV和VH极化;威布尔分布更符合X波段的HV、VH极化以及Ku波段的HH、VV极化。