The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve performance navigation services. Due large number performances, combinatorial clustering optimization very important, efficiency manual screening low. Firstly, based on basic model, objective equations are derived. Secondly, fuzzy c-means (FCM) al...

In view of failure characteristics of wind turbine gear box, this paper puts forward a method for fault diagnosis based on the ensemble local means decomposition (ELMD) and fuzzy C-means clustering (FCM) method. Resolve the vibration signal of different fault state of high speed gear box by ELMD to obtain the PF component, and obtain its singular value, which is composed of known sample and tes...

-Impulse noise detection is a critical issue when removing impulse noise and impulse/gaussian mixed noise. The framework combines Robust Outlyingness Ratio (ROR) detection mechanism and Fuzzy C Means (FCM) clustering algorithm and Nonlocal Means (NLM) filter. ROR for measuring how impulse like each pixel is and then all pixels are divided into four clusters according to the ROR values. The dete...

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applyin...

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is ...

إ ةيرهجملا جماربلا لمشي اذهو ،ةيرورملا ةآرحلا ةاآاحم جمارب روطت يف مهاس ةيتامولعملا ايجولونكتلا يف ريبكلا مدقتلا ن ) Microscopic ( ، نلا ىلع بلطلا ةاآاحم اًضيأ لمشي نايحلأا ضعب يفو ، تاعطاقتو قرط نم اهيف امب لقنلا ةموظنم ةاآاحمب حمسيل يللآا بساحلا تاقيبطت قاطنو لق . ةيرهجملا ةيرورملا ةآرحلا ةاآاحم جمارب نيب نراقتو عجارت ةقرولا هذهو ) Microscopic ( ةيلومشلاو ) Macroscopic ( فلاتخلاا هجوأ ىلع ةزآر...

Fuzzy C-means (FCM) is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the Spatially Guided FCM (SGFCM) algorithm is presented which segments multi...

This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into a...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

Much research has shown that fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this propo...