A SKELETONIZATION ALGORITHM BASED ON EUCLIDEAN DISTANCE MAPS AND MORPHOLOGICAL OPERATORS

In this letter a new skeletonization algorithm is proposed. It combines techniques of fast construction of Euclidean Distance Maps(EDMs), ridge extraction, Hit-or-Miss Transformation(HMT) of structuring elements and the set operators. It first produces the EDM image with no more than 4 passes through an image of any kinds, and then the ridge image is extracted by applying a turn-on scheme and performing a rain-fall elimination to accelerate the processing. The one-pixel wide skeleton is finally acquired by carrying out the HMTs of two structure elements and the SUBTRACT and OR operations. Experimental results obtained by practical applications are also presented.

Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance

Recommendation system can greatly alleviate the "information overload" in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED(PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis(PLSA) to extract users' interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users' similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors' ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

THE MINIMUM SQUARED EUCLIDEAN DISTANCE OF MULTI-h PHASE CODES

The minimum squared Euclidean distance(MSED) of binary multi-h phase codes is presented. The signal segregation degree(SSD) has been put forward to determine MSED of multi-h phase codes. In order to maximize MSED, SSD should be as large as possible. The necessary and sufficient conditions of maximizing SSD are derived. Finally, SSD and the exact formulae for MSED of binary 2-h phase codes are also presented.

Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification

Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately.This study discusses data classification using a fuzzy soft set method to predict target classes accurately.This study aims to form a data classification algorithm using the fuzzy soft set method.In this study,the fuzzy soft set was calculated based on the normalized Hamming distance.Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function.In the classification step,a generalized normalized Euclidean distance is used to determine the similarity between two sets of fuzzy soft sets.The experiments used the University of California(UCI)Machine Learning dataset to assess the accuracy of the proposed data classification method.The dataset samples were divided into training(75%of samples)and test(25%of samples)sets.Experiments were performed in MATLAB R2010a software.The experiments showed that:(1)The fastest sequence is matching function,distance measure,similarity,normalized Euclidean distance,(2)the proposed approach can improve accuracy and recall by up to 10.3436%and 6.9723%,respectively,compared with baseline techniques.Hence,the fuzzy soft set method is appropriate for classifying data.

基于EDM-VKF的转子时变复杂信号时频表示方法

轴轨道的时频表示已被证明是分析非平稳振动信号的有效工具,但是传统时频表示方法分辨率较低,不易区分分布较近的特征频率。为解决该问题,提出转子时变复杂信号时频表示方法,首先运用基于经验模态分解的多尺度小波阈值降噪算法处理原始信号,得到降噪后的转子振动信号;其次针对信号时频谱图,引入欧几里得距离重构时频矩阵,不但提高信号的时频聚集性,而且使时频矩阵具有更好的极值点连贯性避免极值点偏移,进而更准确地检出时频脊线并估计信号基频分量瞬时频率值;最后采用Vold-Kalman滤波器在时域信号中分离基频和其谐波分量,并计算谐波分量的瞬时全谱参数,构建转子信号的高分辨率时频表示。通过对转子系统模拟仿真信号和转子不对中信号分析,验证了该方法的有效性。

结合EDCA和CPA的容错双向选择攻击

当所设计的攻击方案带有容错功能时,往往需要在非常大的候选空间中挑出正确的密钥。如何有效地实现这个目标是侧信道攻击中非常重要且具有挑战性的问题。针对这一问题,以AES-128为目标研究了结合欧式距离增强碰撞攻击(EDCA)和相关能量分析攻击(CPA)的容错双向选择攻击。为了提高碰撞检测的成功率,提出了EDCA,与传统的相关增强碰撞攻击(CCA)相比,EDCA利用欧式距离来区分两组能量迹之间的相似性,其碰撞检测的成功率更高,从而使优化更加实用和有意义。除此之外,结合EDCA和CPA,将密钥以及对应的碰撞对做分组处理,然后进行双向筛选,得到最优的碰撞链,大大减少了候选空间,从而降低了密钥枚举的复杂性,有效地恢复密钥。实验结果表明,在低信噪比SNR=-3 dB和SNR=-6 dB的条件下,设置碰撞对的阈值ThΔ=5,所提出的方案在3000条能量迹时成功率达到98.78%和80.25%,均优于现有方案。

A method for extracting anomaly map of Au and As using combination of U-statistic and Euclidean distance methods in Susanvar district,Iran

Quantitative descriptions of geochemical patterns and providing geochemical anomaly map are important in applied geochemistry. Several statistical methodologies are presented in order to identify and separate geochemical anomalies. The U-statistic method is one of the most important structural methods and is a kind of weighted mean that surrounding points of samples are considered in U value determination. However, it is able to separate the different anomalies based on only one variable. The main aim of the presented study is development of this method in a multivariate mode. For this purpose, U-statistic method should be combined with a multivariate method which devotes a new value to each sample based on several variables. Therefore, at the first step, the optimum p is calculated in p-norm distance and then U-statistic method is applied on p-norm distance values of the samples because p-norm distance is calculated based on several variables. This method is a combination of efficient U-statistic method and p-norm distance and is used for the first time in this research. Results show that p-norm distance of p=2(Euclidean distance) in the case of a fact that Au and As can be considered optimized p-norm distance with the lowest error. The samples indicated by the combination of these methods as anomalous are more regular, less dispersed and more accurate than using just the U-statistic or other nonstructural methods such as Mahalanobis distance. Also it was observed that the combination results are closely associated with the defined Au ore indication within the studied area. Finally, univariate and bivariate geochemical anomaly maps are provided for Au and As, which have been respectively prepared using U-statistic and its combination with Euclidean distance method.

基于改进ED及CM-BPNN算法的保护测量回路误差状态评估方法

通过对保护测量回路误差状态进行准确评估,能掌握保护装置运行情况并及时发现保护装置存在的隐患。提出了一种基于改进欧氏距离(euclidean distance,ED)及云模型-BP神经网络(cloud model-back propagation neural metwork,CM-BPNN)算法的变电站保护测量回路误差状态评估方法。首先,对测量数据制定不同指标数据的归一化原则;其次,引入改进欧氏距离作为误差评估的启动判据,根据对保护动作性能的影响程度将误差状态进行等级划分并引入云模型计算二级指标的误差状态隶属度;最后,确定每一种误差状态对应的定位映射结果,完成BP神经网络模型的构建,再以此模型进行保护装置电流/电压未知误差状态的评估和定位。基于PSCAD软件搭建220 kV变电站模型,对所提方法的有效性进行验证。

Facility Location Decisions Based on Driving Distances on Spherical Surface

Facility location problems are concerned with the location of one or more facilities in a way that optimizes a certain objective such as minimizing transportation cost, providing equitable service to customers, capturing the largest market share, etc. Many facility location decisions involving distance objective functions on Spherical Surface have been approached using algorithmic, metaheuristic algorithms, branch-and-bound algorithm, approximation algorithms, simulation, heuristic techniques, and decomposition method. These approaches are most based on Euclidean distance or Great circle distance functions. However, if the location points are widely separated, the difference between driving distance, Euclidean distance and Great circle distance may be significant and this may lead to significant variations in the locations of the corresponding optimal source points. This paper presents a framework and algorithm to use driving distances on spherical surface and explores its use as a facility location decision tool and helps companies assess the optimal locations of facilities.

Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

Distance Transform Based Enhancement for Linear Interpolated Images

An approach of distance map based image enhancement (DMIE) is proposed. It is applied to conventional interpolations to get sharp images. Edge detection is performed after images are interpolated by linear interpolations. To meet the two conditions set for DMIE, i. e., no abrupt changes and no overboosting, different boosting rate should be used in adjusting pixel intensities. When the boosting rate is determined by using the distance from enhanced pixels to nearest edges, edge-oriented image enhancement is obtained. By using Erosion technique, the range for pixel intensity adjustment is set. Over-enhancement is avoided by limiting the pixel intensities in enhancement within the range.A unified linear-time algorithm for distance transform is adopted to deal with the calculation of Euclidean distance of the images. Its computation complexity is O (N2 ). After the preparation, i. e.,distance transforming and erosion, the images get more and more sharpened while no over-boosting occurs by repeating the enhancement procedure. The simplicity of the enhancement operation makes DMIE suitable for enhancement rate adjusting.

基于AED-CEEMD-Transformer的锂离子电池健康状态估计

锂电池健康状态(state of health,SOH)的精确预测评估对电池设备安全稳定运行极为重要,通过对SOH的快速准确预测,可以提高电池设备的安全性并降低出现故障的风险。针对难以精确预测锂离子电池SOH的问题,本文采用电池容量作为SOH的指标,提出一种利用平均欧几里得距离(average euclidean distance,AED)和互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)方法建立基于Transformer网络结构的锂离子电池健康状态估计算法。首先,我们利用AED评估电池数据库中的电池与待预测电池初期循环容量之间的相似度,并选出具有相似容量退化趋势的电池作为训练集以提高模型的训练速度,然后利用CEEMD方法将电池容量曲线分解为容量再生部分以及退化趋势部分,将各个分量分别使用Transformer网络来建立锂电池退化模型,进而得到锂离子电池的SOH预测结果。本文使用分别来自斯坦福大学与马里兰大学的两个具有不同充放电策略与不同测试环境下的锂离子电池数据集来验证了所提出的电池预测算法的准确性。本文所提模型的均方根误差均能控制在4%以内,具有较好的精确性,并通过与基于LSTM、RNN、GRU的常用锂离子电池健康状态估计算法结果的比较,验证了所提出估计方法的优越性。

基于反射强度的改进欧式距离聚类钢轨点云分割方法

线路点云数据结构化是深化专业计算与分析的技术前提。点云分割是点云数据结构化的基础。钢轨作为轮轨走行面,其空间位置的连续及平顺情况直接影响行车安全,因此轨道结构分割中,首先需对钢轨进行分割处理。针对传统欧式距离聚类中线路全景点云数据遍历导致距离阈值难统一、难界定,造成分类过多不易查找,或人工选取初始点及调参带来的自动化程度不高的情况,提出基于反射强度的改进欧式距离聚类钢轨点云分割方法。在对轨道结构特性分析的基础上,采用布料滤波算法进行地面滤波,区分地面点与地物点,精简线路点云为轨道结构点云;融合点云反射强度属性,提出提取率概念,确定钢轨高反射强度区间,进行钢轨顶面点云预分割,进而以轨顶面预分割点作为初始点,根据钢轨断面轨头高和轨头宽构造对角线长度来计算距离阈值,由Kd-Tree找到小于轨头距离阈值的点进行欧式距离聚类,实现对轨头凸集点云的分割。多路段钢轨点云分割试验,精确率及召回率均大于90%,说明该方法可行有效。

基于改进Criminisi算法的中国古画修补系统

Criminisi算法广泛用于文物修补中,由于Criminisi算法采用全局搜索的方式寻找匹配块,导致修补速度慢,因此提出一种基于改进Criminisi算法的中国古画修补系统。首先,改进优先权函数,使用加权求和的形式,解决了修补后期如果优先权为0则导致修补效果差的问题;然后,在计算样本块间的相似度时,引入几何距离判断更优的匹配块,避免只考虑颜色差导致的纹理匹配错误;最后,使用步长为2的搜索方式,减少了冗余搜索,提高了修补速率。另外,使用MATLAB R2021a设计了古画修补系统,方便文物保护人员进行操作。实验结果表明,该算法提升了修补质量,提高了修补速度。

基于时间序列压缩分割的监测数据异常识别算法研究

为有效识别桥梁健康监测数据的异常,减少误预警、漏预警现象,保障桥梁监测数据的质量和有效性,针对大跨度斜拉桥长期监测数据的缺失、离群和漂移3类异常数据,提出基于时间序列压缩分割的监测数据异常识别算法。该算法将原始监测数据时间序列通过基于序列重要点(Series Importance Point, SIP)的时间序列线性分段(Piecewise Linear Represent, PLR)算法(PLR_SIP)得到数条时间子序列;然后采用欧氏距离进行时间子序列的相似性分析,并基于改进的局部离群因子(Local Outlier Factor, LOF)算法计算每条时间子序列的局部离群因子;最后将其与设定的阈值相比较,从而识别出监测数据的异常。为验证该算法的准确性与工程实用性,对某公路大跨度斜拉桥健康监测数据进行异常识别。结果表明:采用PLR_SIP算法对原始时间序列压缩分割得到的时间子序列能够准确地反映原序列的变化趋势和范围;改进的LOF算法突破了传统LOF算法仅能识别离群值这类无持续时间异常的局限性,能够排除噪声的干扰,实现对离群、缺失和漂移3种异常的识别。该算法无需定义训练集,直接以原始监测数据作为算法的输入,同时能够自适应调整阈值参数,具有良好的可扩展性、实时性、准确性和高效性,适用于处理实时、大量的桥梁健康监测数据。

基于多元精细复合多尺度波动散布熵和累积欧氏距离矩阵测度的风电机组变桨轴承退化状态评估

针对风电机组变桨轴承服役过程环境噪声干扰严重、退化状态评估精度低的问题,提出一种基于多元精细复合多尺度波动散布熵和累积欧氏距离矩阵测度的退化状态评估模型。该模型将监测数据状态特征获取过程由单通道拓展为多通道进行,通过提出的多元精细复合多尺度波动散布熵算法来获取多通道监测数据的多尺度状态特征,并将累积和检验算法与欧氏距离矩阵测度方法相结合,用于定量衡量基准样本与待分析样本间的差异,从而实现变桨轴承退化状态评估。风电机组变桨轴承全寿命周期加速疲劳实验验证结果表明:该模型能够及时捕捉到变桨轴承的初始退化时刻并且准确跟踪整个退化过程。

基于组合赋权与灰云模型的综合能源系统需求响应效益评价

考虑到当前综合能源系统研究缺乏完善的需求响应量化评估方案,难以在优化运行层面综合评估需求响应效益这一问题,提出了一种基于改进AHP群决策—CRITIC组合赋权与灰云模型的综合能源系统需求响应效益评价方法。首先,从综合能效性、社会经济性和需求侧互动性三个维度构建了需求响应效益评价指标体系。其次,采用一致性和权重拟合性更优的指数标度法构造判断矩阵,降低赋值误差,并通过AHP群决策法确定主观权重,从而削弱主观极值偏差对权重的影响;在由CRITIC法确定客观权重后,基于最小欧氏距离建立组合权重模型,并通过非线性规划求取最优组合权重;针对评价等级信息的模糊性与隶属等级的随机性,采用正态灰云白化权模型确定指标分类等级与场景评分。最后,以北方某综合能源系统为例,根据用户参与需求响应方式设置了4种运行场景,分析了不同需求响应对系统运行的影响与作用,算例结果表明所提指标体系与评价方法科学有效。

一种基于拓扑结构变化的主动配电网自适应保护方法

随着逆变型分布式电源大量接入配电网,配电网运行方式灵活多变,保护方案的设计与保护装置的动作范围受拓扑变化的影响。在此背景下并结合工程实际,提出一种基于拓扑结构变化的主动配电网自适应保护方法。通过多点量测信息感知配电网的运行状态,利用电压量和电流量之间的相关性,对拓扑结构的变化进行识别。然后,根据拓扑结构的变化,形成新的关联矩阵,自适应调整系统的保护方案与参数。最后,通过仿真验证了所提出的方法有效性。

小半径曲线梁桥的无缝线路有限元模型简化研究

为更简便地建立小半径曲线梁桥的无缝线路有限元模型,降低计算成本并保证计算精度的可靠性,本文比较分析不同简化程度的模型。首先,依据已有桥上无缝线路模型的形式,通过不同程度简化桥梁上部结构,由简到繁确立了单刚臂模型、多刚臂模型、准梁格模型、平面壳模型、空间壳模型、实体模型共6种模型,并通过扣件横向和纵向阻力试验取得了模型的关键参数;其次,以曲线半径为200 m的(40+50+50+40) m连续梁桥为实例,计算了桥上无缝线路在伸缩、挠曲、制动和断轨等其他组合工况下的结果;最后,以实体模型的计算结果为基准,将前5种模型的计算结果与实体模型的进行对比,通过欧几里得距离量化分析了各模型的计算精度。研究结果表明:综合考虑模型复杂程度和计算精度,多刚臂模型和准梁格模型相比于空间壳模型性价比更高,梁格法在桥梁工程中被大量用于异形桥梁计算,且支座分布以及边界条件更符合实际情况。因此,推荐在进行小半径曲线梁桥的无缝线路计算时采用准梁格模型。