Investigation into features of graphite nanofluid synthesized using electro discharge process

This paper presents a partitioning around medoid (PAM)-based novel method to realize the recognition of the tool wear state in milling. In PAM, the representative objects called medoids are used to define clusters and average dissimilarities are applied to assess the medoids, which make PAM robust to outliers and therefore improve the clustering performance. Meanwhile, locality preserving projections (LPP) method is utilized to further increase the clustering accuracy by dimension reduction. To show the effectiveness of the proposed method, end milling experiment of Ti-6Al-4V alloy were carried out and the commonly used k-means and fuzzy c-means (FCM) algorithm are introduced to make a comparison with PAM algorithm by using five clustering evaluation indicators. The results show that PAM performs higher accuracy and robustness than the other two clustering algorithm.     

模糊聚类算法综述

模糊聚类算法是近年来图像分割技术领域的研究热点之一。本文在对模糊C均值聚类算法分析的基础上,结合目前在图像分割中的应用研究,对模糊C均值聚类算法的有效性进行了比较分析。从隶属度、聚类数和其它方面,评述改进的模糊c均值聚类算法。最后讨论模糊c均值聚类算法目前存在的问题和发展方向。     

基于改进半监督模糊C-均值聚类的发动机磨损故障诊断

为解决在少量油液样本条件下发动机磨损故障诊断难的问题,提出一种改进半监督模糊C-均值聚类算法(Improvedsemi-supervised fuzzy c-means clustering algorithm,ISS-FCM).定义一种优化的目标函数,将无标签样本与训练样本间的平均距离度量考虑在内并赋予其一定权值,以...     

基于和声搜索优化多T-S模糊神经网络的聚合釜过程软测量建模

根据多个模型相加可以提高整体预测精度和鲁棒性的思想,提出一种基于模糊C均值聚类算法的多T-S模糊神经网络模型对聚氯乙烯(polyvinylchlorid,PVC)聚合生产过程中的氯乙烯(vinyl chloride monomer,VCM)转化率和转化速率进行预测。首先采用主元分析来对软测量模型的辅助变量进行选择以降低模型维数,并提出和声搜索和最小二乘法相结合的混合优化算法来优化T-S模糊神经网络子模型的结构参数。仿真结果表明该模型能够显著提高PVC聚合过程中经济技术指标预测的精度和鲁棒性,可以满足聚合釜生产过程的实时控制要求。     

A differential evolution fuzzy clustering approach to machine cell formation

Searching for similar parts and associated machines plays a crucial role in machine cell design. The aim of this paper is to propose a new fuzzy clustering method to solve machine cell formation problems in a fuzzy environment. Fuzzy c-means (FCM) has been applied to machine cell formation problems. However, performance of existing FCM algorithms depends highly on the initial states (cluster centers or membership degrees). Additionally, the number of clusters (machine cells) has to be provided beforehand. In practice, it is difficult for the machine cell designer to select a proper set of initial states or to determine the optimal number of machine cells before the overall machine cell configuration is formed and the operational result is observed. In this paper, a new fuzzy clustering approach combining differential evolution (DE) algorithms with FCM formulas is proposed to overcome these deficiencies. The proposed DE-based fuzzy clustering method can automatically determine the correct number of cells and generate an optimal machine cell configuration at the same time. Experimental results demonstrate that the proposed algorithm performs well in searching solutions to the fuzzy machine cell formation problem with automatic cluster number determination.     

D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks

Owing to the scattered nature of Denial-of-Service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a hybrid clustering method is introduced, namely a density-based fuzzy imperialist competitive clustering algorithm (D-FICCA). Hereby, the imperialist competitive algorithm (ICA) is modified with a density-based algorithm and fuzzy logic for optimum clustering in WSNs. A density-based clustering algorithm helps improve the imperialist competitive algorithm for the formation of arbitrary cluster shapes as well as handling noise. The fuzzy logic controller (FLC) assimilates to imperialistic competition by adjusting the fuzzy rules to avoid possible errors of the worst imperialist action selection strategy. The proposed method aims to enhance the accuracy of malicious detection. D-FICCA is evaluated on a publicly available dataset consisting of real measurements collected from sensors deployed at the Intel Berkeley Research Lab. Its performance is compared against existing empirical methods, such as K-MICA, K-mean, and DBSCAN. The results demonstrate that the proposed framework achieves higher detection accuracy 87% and clustering quality 0.99 compared to existing approaches.     

Mapping and fuzzy classification of macromolecular images using self-organizing neural networks

In this work the effectiveness of the fuzzy kohonen clustering network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules is studied. The algorithm combines Kohonen's self-organizing feature maps (SOFM) and Fuzzy c-means (FCM) in order to obtain a powerful clustering technique with the best properties inherited from both. Exploratory data analysis using SOFM is also presented as a step previous to final clustering. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with self-organizing feature maps (SOFM) and manual classification. Experimental results prove that this new technique is suitable for working with large, high-dimensional and noisy data sets and, thus, it is proposed to be used as a classification tool in electron microscopy.     

基于FCM算法的零件簇编码分析

给出一个基于模糊c-平均(FCM)算法的零件簇聚类分析的过程模型来描述分析过程;构造了适合于零件簇聚类分析的FCM算法,该方法考虑了零件簇对象特征之间的模糊关系和各零件对象特征聚类中心之间的距离,无需设计权重系数;通过实例进行了聚类分析,并与模糊聚类和k-平均聚类两种方法进行比较,证明该FCM算法是有效的。     

基于改进核模糊聚类算法的软测量建模研究

针对发酵过程软测量建模采用单模型建模方法存在计算量大和精度较差的问题,提出一种基于改进核模糊聚类算法的多模型神经网络软测量建模方法.该方法首先使用主元分析方法对样本数据进行数据处理,所得主元变量作为模型的输入变量,然后使用基于粒子群优化算法的核模糊C均值聚类算法(PSKFCM)对数据集作聚类划分,最后针对每个聚类建立局部神经网络模型,多个局部神经网络模型估计结果的融合即为软测量模型的输出.将所提建模方法应用于红霉素发酵过程生物量浓度软测量建模,结果表明所建软测量模型具有较高的精度和良好的泛化能力.     

基于粒子群优化KFCM的风电齿轮箱故障诊断

针对基于有监督学习的方法无法识别未知类别故障,提出了一种基于粒子群优化模糊核聚类(kernel fuzzy c-means clustering,简称KFCM)的风电机组齿轮箱故障诊断方法。首先,建立以训练样本分类错误率为目标的聚类模型,利用KFCM对训练样本进行分类;然后,以初始聚类中心和核函数参数作为优化变量,利用粒子群优化算法求解聚类模型,获得最优分类结果下每个类的类心;最后,根据新样本与各类心之间的核空间样本相似度判断新样本属于已知故障或者未知故障。以某风电机组齿轮箱为例,对提出方法的有效性进行试验验证。结果表明,与传统基于有监督学习的神经网络方法相比,该方法能有效诊断已知和未知类别的故障。     

Automated identification of normoblast cell from human peripheral blood smear images

In this paper, we have presented a new computer‐aided technique for automatic detection of nucleated red blood cells (NRBCs) or normoblast cell from peripheral blood smear image. The proposed methodology initiates with the localization of the nucleated cells by adopting multilevel thresholding approach in smear images. A novel colour space transformation technique has been introduced to differentiate nucleated blood cells [white blood cells (WBCs) and NRBC] from red blood cells (RBCs) by enhancing the contrast between them. Subsequently, special fuzzy c‐means (SFCM) clustering algorithm is applied on enhanced image to segment out the nucleated cell. Finally, nucleated RBC and WBC are discriminated by the random forest tree classifier based on first‐order statistical‐based features. Experimentally, we observed that the proposed technique achieved 99.42% accuracy in automatic detection of NRBC from blood smear images. Further, the technique could be used to assist the clinicians to diagnose a different anaemic condition.     

基于模糊核聚类和引力搜索的风电齿轮箱故障诊断

为了诊断风电齿轮箱已知类别和未知类别的故障,提出了基于模糊核聚类和引力搜索的故障诊断方法。首先建立以训练样本分类错误率为目标的聚类模型,利用模糊核聚类对训练样本进行分类;然后利用引力搜索算法求解聚类模型,获得最优分类结果下每个类的类心;最后根据新样本与各类心之间的核空间样本相似度判断属于已知故障或者未知故障。结果表明,该方法准确度高,可有效用于风电齿轮箱故障诊断。     

用于分割无损检测图像的改进的抑制式模糊C均值聚类算法

由于无损检测图像灰度分布不均衡,常用的模糊C均值聚类算法不能对图像中的目标与背景进行有效分割,故提出一种改进的抑制式模糊C均值聚类算法(IS-FCM)对无损检测图像进行分割。通过对抑制式模糊C均值聚类算法(S-FCM)的目标函数融入每一类的总隶属度以均衡化目标像素和背景像素对聚类结果的影响,在构建的新目标函数基础上推导出新的隶属度和聚类中心迭代形式,然后分析了所提算法的收敛性并给出了执行步骤,最后通过无损检测图像对所提算法进行分割实验。结果表明,IS-FCM算法不仅能够对灰度分布不均衡的无损检测图像进行有效分割,还扩展了S-FCM算法的应用范围,增强了鲁棒性和适应性。     

Trajectory planning of redundant manipulator using fuzzy clustering method

The solution of inverse kinematics and trajectory planning with performance criteria for a redundant manipulator is proposed with modification in fuzzy c-means. A new fuzzy clustering model based on a new generalized validity index based on weighted within-scatter metrics and between-cluster scatter metrics for the manipulator is proposed. In order to understand the proposed algorithm and to show its performance, two simulation studies of trajectory planning with manipulability criteria for a redundant manipulator are modeled to solve the problems of finding association rules in the data and of setting up an appropriate classification procedures. The problem of redundant manipulator (which is a multi-input, multi-output nonlinear system) is new in terms of solution by clustering method. The proposed algorithm for the trajectory planning of the manipulator is simulated using Matlab®. All practical steps, from data acquisition to model validation, are illustrated using a 4 degree of freedom robot manipulator. The simulated results are compared with the numerical methods of the trajectory planning. The results are presented graphically. The proposed method has the advantage of simplicity, flexibility, and good tracking performance.     

基于微波谐振器的谷物湿度检测系统

设计和研制一种能连续测量谷物湿度的微波检测系统.提出利用微波谐振器的干扰特性测量谷物的湿度,并设计专用于谷物湿度检测的中心开通孔的微波谐振器.当谷物颗粒通过微波谐振器的中孔时,其质量和湿度都令谐振器的谐振频率产生偏移和功率衰减,谷物的质量和湿度两者所引起的谐振频率偏移和微波功率衰减是相对独立的,在同一湿度下谷物的质量仅仅与微波功率的衰减量有关.因此在得到谐振频率的偏移量和微波功率的衰减量后,就可以计算谷物的湿度.在大量试验的基础上,对试验数据进行模糊聚类,从而对微波谐振器的频偏和功率衰减特性进行建模,据此设计并研制了谷物湿度的微波连续检测系统.试验调试表明:该检测系统适宜于谷物的连续湿度检测,检测精度达到92%以上.     

基于DSP的中药丸料湿度密度在线检测系统

设计与研制一种能在现代化中药制药生产线上在线连续测量中药丸料湿度与密度的微波检测系统。系统包括三个组成模块：微波信号源模块，微波谐振腔，数据处理模块。其中，微波谐振腔是专门设计用于中药丸料湿度与密度检测的中心开通孔的金属谐振腔，当中药丸料通过微波谐振腔的中孔时，丸料密度及湿度均令谐振腔的谐振频率产生偏移和功率衰减，数据处理模块据此计算中药丸料的湿度与密度。为提高检测精度，采用模糊聚类算法对实验数据进行分组，对金属谐振腔的频偏和功率衰减特性进行建模，并利用DSP设计并实现了中药丸料湿度与密度的在线检测。现场调试表明：该检测系统适宜于现代化中药制药生产线上的湿度连续在线测量，其检测精度达到93％以上。     

A new method to fuzzy modeling and its application in performance evaluation of tenants in incubators

As we know fuzzy modeling is one of the most powerful techniques to extract experts’ knowledge in the form of fuzzy if-then rules. In this research work, a new method to fuzzy modeling is proposed in which the main goal is to construct a fuzzy rule-base of the type of Mamdani. In the proposed method, fuzzy c-means (FCM) clustering is used for structure identification and two optimization problems are used for parameter identification. The proposed method is used to simulate experts’ knowledge for performance evaluation of tenants in incubators. The authors have implemented their proposed method in a real numerical example successfully.     

用模糊聚类Fuzzy C-Means算法实现图像分割

本文描述了一种有效的Fuzzy C-Means(FCM)聚类算法的数学原理及其在图像分割中的应用,该算法的主要特点是提供了一种非监督的模糊聚类方式。为了减少计算量,文章引入了塔型数据结构PDS(Pyramid Data Structure),并对FCM算法的初始模糊矩阵的选取和实验结果进行了讨论。     

模糊C均值算法在烧结断面图像分割中的应用

利用模糊C均值的算法,可以实现烧结图像的自动分割。传统的模糊C均值算法在判定像素归属时没有考虑像素的空间信息,对在烧结图像上存在的大量噪声非常敏感。为提高烧结图像分割的准确性,对传统FCM算法中的目标函数进行修正。实验表明采用FCM算法对烧结图像有良好的分割效果,而改进的FCM算法对图像中噪声具有较强的鲁棒性。     

面向用户需求的制造资源服务模糊聚类研究

通过分析用户需求偏好和资源灰色关联度,建立资源模糊相似矩阵并构建资源模糊聚类树。以复合值模糊聚类树为基础,以资源类内聚合度、资源类间分离度和用户需求满意度为目标,构建资源聚类优化数学模型,然后运用遗传算法进行优化。算法采用了交叉概率和变异概率自适应的重构策略和保优操作,避免了算法的早熟,增强了算法的寻优能力和搜索效率。通过实例仿真验证了算法的有效性。