Classification of Anomalies in Multivariate Streaming Phasor Measurement Unit Data Using Supervised and Clustering Ensemble Techniques

Amutha, A.; Uthra, R. Annie; Roselyn, J. Preetha; Brunet, R. Golda

doi:10.1109/iceca52323.2021.9675892

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Since the base learner of the presented method, the self organizing fuzzy inference scheme can bale to self-learn a transparent from data stream on chunk wise via human interpretable method. Amutha et al [17] categorized anomalies in streaming PMU data by taking each feature with supervised and clustering ensemble methods into account. Now, the study utilizes random forest that is a supervised ensemble-based method.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Rajakumar¹,

Devi²

2023

Intelligent Automation &Amp; Soft Computing

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Rajakumar¹,

Devi²

2023

Intelligent Automation &Amp; Soft Computing

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“…Therefore, how high the matching value should be set will affect the stability and plasticity of the judgment. These two values are mutually exclusive, and the best matching value is usually obtained by trial-and-error [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology

Mon

2022

Sustainability

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The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance.

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Classification of Anomalies in Multivariate Streaming Phasor Measurement Unit Data Using Supervised and Clustering Ensemble Techniques

Cited by 2 publications

References 23 publications

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology

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