Pattern Recognition of Time-Varying Signals Using Ensemble Classifiers

Subbarao, M. V.; Terlapu, Sudheer Kumar; Chakravarthy, V. V. S. S. S.; Satapaty, Suresh Chandra

doi:10.1007/978-981-15-3828-5_76

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…ML algorithms can forecast future trends from data (Akhlaghi et al (2021)). ML can also be used as a part that helps in pattern recognition (Subbarao et al (2021)). Nest Thermostat 1 provides an example of a device for setting a room's temperature based on the occupant's preferences.…”

Section: Smart Homesmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

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SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

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Section: Smart Homesmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Since, PQD identification is such a sensitive matter that may lead to catastrophic cascading effect and eventually might end up scenarios like blackout, it should be sincerely addressed with optimum possibility of detection failure. In this regard ensemble MLCs can bring more generalization with uplifted predictability as it combines the prediction of a number of learners [14]. Boosting and bagging are two popular ensemble learning methods.…”

Section: Introductionmentioning

confidence: 99%

Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

Mishra,

Mallick,

Gadanayak

et al. 2024

IEEE Access

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The modern era power system is constantly undergoing constructive changes and implementations both in source and load side. Certainly, the distributed generators, unconventional/nonlinear loads, charging stations etc are mostly integrated through power electronics interfaces. As a result, frequent power quality disturbances appear in the system that is to be mitigated at the earliest. Since detection is the prerequisite for mitigation, therefore the article presents a novel intelligent power quality detection scheme to detect and classify the PQ Events. At first, the energy feature of the 5 band limited modes are calculated from variational mode decomposed voltage signals. Then the mode energy features are utilized to train a novel Hybrid Arithmetic Whale Optimized light gradient boosting machine classifier. A total of 15 different PQ events have been investigated and exceptional classification results have obtained with optimum computational complexity, both under noiseless and noisy conditions. Moreover, the accuracy of the proposed PQ classification schemes found to be towering against other related pre-published works. Finally, the ability of the proposed detection scheme is validated in real time though OPAL-RT 4510 and grid simulator hardware in loop setup.

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“…A PQ disturbance is defined as any variation in the power signals from their usual levels [1]. PQ disturbances are caused by rapid changes in the frequency and voltage of the clean sinusoidal waveforms [2]. Fault clearing, utility switching, and non-linear loads are the causes of these sudden changes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

M.,

G.,

et al. 2023

ECJSE

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The use of sensitive electrical gadgets in industries, buildings, smart cities, and homes has increased drastically in recent years. PQ events such as interruptions, surges, and sags have a high impact on these sensitive devices. The failure of these delicate devices in real-time applications, particularly smart applications, may result in significant damage. The supply quality decreases because of the failure of internal transmission system elements, unbalanced loads, and other outdoor issues such as like weather. Several academics have proposed techniques to analyze these PQ disturbances, including wavelet packets, S-transform, rough sets and neural networks. In all the available algorithms, the classification procedure involves the extraction of a large set of features from the transformed outputs, training the classifier, and finally making a conclusion with the classifier. Because of the involvement of a large number of features, the computational cost of all these methods increases. To reduce complexity and enhance classification efficiency, the proposed method focuses on extracting fewer low-complexity wavelet features from signals. Pattern recognition (PR) methods, such as the wide variety of K-nearest neighbors (KNN) and ensemble classifiers, are used to classify PQ events in this study. The performance of the proposed ML approaches' performance is evaluated at various training and testing rates. Subsequently, the performance of the proposed strategies was compared to that of the current methods to determine the dominance of the proposed approaches.

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Pattern Recognition of Time-Varying Signals Using Ensemble Classifiers

Cited by 8 publications

References 29 publications

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

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