Robust fuzzy clustering algorithms integrating membership guided image filtering

Qiao, CaiCai; Wu, Chengmao; Li, Changxing; Wang, Jiaye

doi:10.1007/s11760-022-02144-z

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…However, due to the power system's extraordinarily enormous size, how to properly analyze the current data swiftly necessitates the direction of matching control algorithms. Existing data processing and analysis techniques include fuzzy clustering [13], K-means clustering [14][15][16], neural networks [17][18][19][20], and others. The literature [21] developed a deep neural network algorithm and clustering analysis-based approach for detecting abnormalities in sensor network data.…”

Section: 丨 Introductionmentioning

confidence: 99%

Research on the strategy of locating abnormal data in IOT management platform based on improved modified particle swarm optimization convolutional neural network algorithm

Yuan¹,

Li²,

Di³

et al. 2023

Preprint

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The IOT management platform is used to handle and transmit data from many types of power system terminal devices. The current IOT management platform has a low data processing efficiency and a high mistake rate when it comes to finding anomalous data. Furthermore, the effective selection and optimum decision of the convolutional neural network’s structural parameters has a significant impact on prediction performance. Based on this, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a convolutional neural network (CNN) and a global optimization decision of key structural parameters of a convolutional neural network using an improved particle swarm optimization (APSO) algorithm. First, an index model is created to determine if the data retrieved from the IOT management platform is anomalous or not. Second, the structure of the convolutional neural network-based decision method for finding anomalous data is examined. Following that, an enhanced particle swarm optimization technique is developed to optimize the structural parameters of the convolutional neural network, and an APSO-CNN with improved performance for anomalous data localization is generated. Finally, the established algorithm’s correctness, feasibility, and efficacy were evaluated using the Adam optimizer. The results reveal that the established APSO-CNN-based decision algorithm for anomaly data localization offers considerable benefits in terms of accuracy and running time, with extremely interesting application potential.

show abstract

Section: 丨 Introductionmentioning

confidence: 99%

Research on the strategy of locating abnormal data in IOT management platform based on improved modified particle swarm optimization convolutional neural network algorithm

Yuan¹,

Li²,

Di³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, due to the power system's extraordinarily enormous size, how to properly analyze the current data swiftly necessitates the direction of matching control algorithms. Existing data processing and analysis techniques include fuzzy clustering [13], K-means clustering [14][15][16], neural networks [17][18][19][20], and others. Literature [15] proposes a fault routing method based on VMD and permutation entropy feature extraction and combined with the K-means clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on the strategy of locating abnormal data in internet of things management platform based on improved modified particle swarm optimization convolutional neural network algorithm

Yuan

et al. 2023

The Journal of Engineering

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The Internet of Things (IOT) management platform is used to manage and transmit data from a variety of terminal devices in the power system. In terms of detecting abnormal data, the existing IOT management platform has a low data processing efficiency and a high error rate. In addition, the optimal selection and determination of the structural parameters of a convolutional neural network (CNN) have a substantial effect on its prediction performance. On this basis, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a CNN and a global optimization decision of key structural parameters of a CNN using an improved particle swarm optimization (APSO) algorithm. Initially, an index model is developed to identify whether the data obtained from the IOT management platform is abnormal. Second, the structure of the CNN‐based anomaly detection approach is investigated. Next, an improved particle swarm optimization approach is designed to optimize the structural parameters of the CNN, and an APSO‐CNN with higher performance for anomalous data localization is constructed. Using the Adam optimizer, the accuracy, feasibility, and efficiency of the established method were assessed. The results demonstrate that the developed APSO‐CNN‐based decision method for anomaly data localization offers significant advantages in terms of precision and execution speed, with potentially intriguing application potential.

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RETRACTED ARTICLE: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

2022

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The main purpose of identifying and locating the vessels of the retina is to specify the various tissues from the vascular structure of the retina (which could be differencied between wide or tight) of the background of the fundus image. There exist several segmentation techniques that are spreading to divide the retinal vessels, depending on the issues and complexity of the retinal images. Fuzzy c-means is one of the most often used algorithms for retinal image segmentation due to its effectiveness and speed. This paper analyzes the performance of improved FCM algorithms for retinal image segmentation in terms of their ability and capability in segmenting and isolating blood vessels. The process we followed in our paper consists of two phases. Firstly, the pre-processing phase, where the green channel is taken for the color image of the retina. Contrast enhancement is performed through CLAHE , proceeded by applying bottom-hat filtering to bottom-hat filtering is applied with the purpose to define the region of interest. Secondly, in the segmentation phase the obtained image is segmented using FCM algorithms. The algorithms chosen for this study are: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM N, FCM SICM and, SSFCM performed on DRIVE and STARE databases. Experiments accomplished on DRIVE and STARE databases demonstrate that the DSFCM N algorithm achieves better results on the DRIVE database, whereas the FGFCM algorithm provides better results on the STARE database in term of accuracy. Concerning time consumption. The FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images.

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Robust fuzzy clustering algorithms integrating membership guided image filtering

Cited by 4 publications

References 35 publications

Research on the strategy of locating abnormal data in IOT management platform based on improved modified particle swarm optimization convolutional neural network algorithm

Research on the strategy of locating abnormal data in IOT management platform based on improved modified particle swarm optimization convolutional neural network algorithm

Research on the strategy of locating abnormal data in internet of things management platform based on improved modified particle swarm optimization convolutional neural network algorithm

RETRACTED ARTICLE: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

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