A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Improved evidential fuzzy c-means method

Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

Kernel method-based fuzzy clustering algorithm

The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

IFCEM based recognition method for target with interval-overlapped hybrid attributes

When the attributes of unknown targets are not just numerical attributes,but hybrid attributes containing linguistic attributes,the existing recognition methods are not effective.In addition,it is more difficult to identify the unknown targets densely distributed in the feature space,especially when there is interval overlap between attribute measurements of different target classes.To address these problems,a novel method based on intuitionistic fuzzy comprehensive evaluation model(IFCEM)is proposed.For numerical attributes,targets in the database are divided into individual classes and overlapping classes,and for linguistic attributes,continuous interval-valued linguistic term set(CIVLTS)is used to describe target characteristic.A cloud modelbased method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively.An improved inverse weighted kernel fuzzy c-means(IWK-FCM)algorithm is proposed for solution of attribute weight vector.The possibility matrix is applied to determine the identity and category of query target.Finally,a case study composed of parameter sensitivity analysis,recognition accuracy analysis.and comparison with other methods,is taken to verify the superiority of the proposed method.

Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning

Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data.

基于随机森林模型的长江流域分区多源融合降水模拟方法研究

基于3种卫星降水产品,提出了一种基于随机森林模型的长江流域分区多源融合降水模拟算法(FCM-RF算法)。采用模糊C均值算法,结合地面观测站点资料对长江流域进行降水区域划分,引入降水比降刻画降水空间性,进一步通过普通克里金插值法优化融合结果,得到一套长江流域空间分辨率为0.25°×0.25°的多源融合降水产品,并对其进行了评估。结果表明:FCM-RF算法在长江流域具有良好的表现,可以有效提高原始卫星降水产品对于降水事件的捕捉能力,在验证站点模拟降水量与实测降水量的相关系数可达到0.76;FCM-RF算法在年际上具有相似变化特征,对于春秋季降水的敏感性较高,在夏季由于强降水影响表现欠佳,冬季由于雨量稀少、存在固态降水,呈现出误差小、相关系数较低的特点;FCM-RF算法在东南地区具有较强的降水捕捉能力,在青藏高原地区的准确性较低。

基于FCM和粗糙集属性重要度理论的综合评价系统

应用FCM和粗糙集属性重要度理论研究了评价和预测问题中样本的聚类分析与各因素的合理赋权问题,提出了一种新的综合评判方法———基于FCM和粗糙集属性重要度理论的综合评判(FCM-WMRS方法);并开发了基于FCM-WMRS方法的区域科技能力综合评判系统;最后,依据中国科学院可持续发展研究组所提供的评价指标体系和有关数据,并结合系统运行结果对各区域科技能力水平现状进行了分析。

基于FCM-Fisher判别分析的难采储量分类

目前储量的分类标准要求所有指标值符合既定的指标范围,否则难以划分储量类别。为克服这一问题,结合模糊C均值法和Fisher判别分析法实现难采储量的分类。首先基于效益指标运用模糊C均值算法自动搜索储量的最佳类别,再利用Fisher判别分析法建立储量效益指标类别与储量属性指标的关系式。在已知储量指标值的情况下,通过此关系式即可判别储量的类别。最后以大庆某油田为实例,对其难采储量进行分类,有效指导难采储量滚动开发决策。

结合ECM和FCM聚类的遥感图像分割新方法

模糊C均值算法(FCM)具有良好的聚类性能从而被广泛应用于图像分割领域,但其存在距离测度鲁棒性差、需预先给出初始聚类数目、未考虑图像局部相关特性等问题。本质上讲,FCM算法是一种局部搜索优化算法,如果初始值选择不当,不仅需要更多的迭代次数,而且会收敛到局部最优解。针对上述问题,结合进化聚类(ECM)和FCM算法,提出了一种遥感图像分割的新方法。利用ECM解决模糊C均值聚类算法的初始化中心选择问题,再利用FCM算法对获得的聚类中心进行优化,完成模糊聚类划分,通过去模糊化转换为确定性分类,实现聚类分割。实验结果表明,该方法能以较少的迭代次数收敛到全局最优解,具有较好的稳定性和鲁棒性,有较好的分割效果,提高了遥感图像分割方法的效率。

Track-Pattern-Based Characteristics of Extratropical Transitioning Tropical Cyclones in the Western North Pacific

Based on the Regional Specialized Meteorological Center(RSMC)Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETCs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FCM)according to their track patterns.The characteristics of the six hard-clustered ETCs with the highest membership coefficient are shown.Most tropical cyclones(TCs)that were assigned to clusters C2,C5,and C6 made landfall over eastern Asian countries,which severely threatened these regions.Among landfalling TCs,93.2%completed their ET after landfall,whereas 39.8%of ETCs completed their transition within one day.The frequency of ETCs over the WNP has decreased in the past four decades,wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETCs among the six clusters,a number that has increased insignificantly over the past four decades.The surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying ETCs.Our results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.

基于FCM-LVQ网络模型的疏勒河流域水安全评价

疏勒河流域水安全状况对祁连山脉的生态安全具有重要影响。针对疏勒河流域水安全问题,提出一种环境-生态-监管-治理模型,构建出祁连山脉内陆河流域水安全评价指标体系,通过模糊C-均值聚类分析(FCM)法结合专家打分法对指标数据进行处理,运用学习向量量化(LVQ)网络模型得到疏勒河流域水安全评价等级,并与单纯使用LVQ神经网络和BP神经网络的评价结果进行对比,以验证评价模型的实用性。结果表明:疏勒河流域水安全状况2013年表现为不安全,2014-2016年表现为基本安全,2017-2019年表现为安全,整体呈现为逐渐上升的趋势,这与流域内实际情况是相符的。另外,FCM-LVQ网络模型在运行速度及评价结果精度上明显更优于另外两种网络模型,可在流域水安全评价中推广使用。

Adaptive Image Digital Watermarking with DCT and FCM

A novel adaptive digital image watermark algorithm is proposed. Fuzzy c-means clustering （FCM） is used to classify the original image blocks into two classes based on several characteristic parameters of human visual system （HVS）. One is suited for embedding a digital watermark, the other is not. So the appropriate blocks in an image are selected to embed the watermark. The wetermark is embedded in the middle-frequency part of the host image in conjunction with HVS and discrete cosine transform （DCT）. The maximal watermark strength is fixed according to the frequency masking. In the same time, for the good performance, the watermark is modulated into a fractal modulation array. The simulation results show that we can remarkably extract the hiding watermark and the algorithm can achieve good robustness with common signal distortion or geometric distortion and the quality of the watermarked image is guaranteed.

Research and Implementation of the Enterprise Evaluation Based on a Fusion Clustering Model of AHP-FCM

Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.

基于改进FCM聚类算法的高速公路交通状态识别

为有效识别高速公路交通状态,提高路网服务水平,基于改进的模糊C均值(Fuzzy CMeans,FCM)聚类算法对高速公路交通数据进行分析。首先,采用熵权法确定交通流量、空间占有率、平均速度和路网充裕度4个交通状态分类指标的权重,并对每个样本赋予不同的加权系数。然后,将样本权重计算纳入算法迭代过程,进而实现高速公路交通状态识别。最后,比较改进FCM算法与传统FCM算法的目标函数值、迭代次数及运行时间,结果表明:与传统FCM算法相比,改进FCM算法的目标函数值较小,迭代次数较少,运行时间较短,在数据中表现出更好的适应性;由改进FCM算法得到的聚类结果能准确、全面地反映交通数据的变化情况,实现道路交通状态的有效识别。

Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps

Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.

新冠疫情下国际班列通道综合风险测算

为分析并测算全球新型冠状病毒肺炎(COVID-19)疫情下以中欧、中亚班列为代表的国际班列通道的综合风险,便于决策者因险施策、防患于未然。首先,构建疫情下通道沿线节点国家综合风险评价指标体系,采用模糊C均值(FCM)聚类算法对沿线国家风险等级进行分类;然后,综合考虑节点间风险的相互影响,构建通道风险测算模型;最后,利用蒙特卡罗法进行仿真试验,计算不同通道去向、返程班列的风险得分。结果表明:中欧班列中/东向通道风险最高,中亚班列南向通道、新通道通行风险相对较小,需有效开拓南向通道和新通道,分担通道货运高风险。

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal

We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FCM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.

Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm

For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.