Improved Butterfly Optimization Algorithm: PI Controller for 7-Level Inverter

Sangtani, Meghna

doi:10.46253/jcmps.v4i3.a2

Cited by 4 publications

(4 citation statements)

References 4 publications

(5 reference statements)

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“…It is computed with the intention that integral square error  is minimized as well as it is stated in Eq. (10). The Haar wavelet orthogonal property is exhibited in eq.…”

Section: Haar Wavelet Function For Processing Of mentioning

confidence: 99%

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Compressed Sensing Reconstruction Approach using Self Adaptive Butterfly Optimization Algorithm for Bio-Signals

Sasidhar¹

2022

JCMPS

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Various applications require competent reprocessing and data representation in signal processing. To efficiently represent the signal the compression is represented as a standard technique. Nowadays, numerous novel approaches are adopted for compression at the sensing level. Compressed Sensing (CS) is represented as a growing domain that is based on revelation, and it gathers a sparse signal linear projection such as sufficient information for reconstruction. Using the CS, signal sampling is performed at a rate under the Nyquist sampling rate when relying on signals sparsity. In addition, original signal reconstruction from a few compressive measurements could be authentically used by CS deviated reconstruction approaches. The major objective of this work is to use a novel CS approach to reconstruct signals in biomedical data. Therefore, by performing three phases the signal can be compressed such as measurement matrix design, signal reconstruction, and signal compression. Here, the compression phase involves a novel working technique that follows three operations such as the transformation of signal,  evaluation and normalization. In this work, the Haar wavelet function is exploited for the evaluation of the theta. Furthermore, this work assures the superiority of the developed model by using the optimization process with the estimation process. The Haar wavelet function vector coefficient is optimally chosen by exploiting a novel optimization approach named Self Adaptive Butterfly Optimization Algorithm (BOA) algorithm. At last, the adopted model performance is evaluated with the conventional techniques and the outcomes reveal the betterment of the proposed model.

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“…It is computed with the intention that integral square error  is minimized as well as it is stated in Eq. (10). The Haar wavelet orthogonal property is exhibited in eq.…”

Section: Haar Wavelet Function For Processing Of mentioning

confidence: 99%

“…Following artificial experiences [8], the first sensory modality coefficient c and the first power exponent coefficient of the fragrance coefficients are chosen in the conventional BOA [10]. Numerous simulations are requiring previous to the preliminary c and the preliminary a is chosen.…”

Section: Proposed Self Adaptive Boa Algorithmmentioning

confidence: 99%

Compressed Sensing Reconstruction Approach using Self Adaptive Butterfly Optimization Algorithm for Bio-Signals

Sasidhar¹

2022

JCMPS

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“…FCM [42] is a approacheswhich uses fuzzy theory to evaluate and represent crucial data, and it is one of several clustering methods. Compared to K-means, HC, SOM, and other clustering models [43] [44], FCM more accurately depicts the unpredictability of the manufacturing process and more accurately mimics the actual world.…”

Section: Introductionmentioning

confidence: 99%

Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

Gorikapudi¹,

Kondaveeti²

2023

Preprint

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Internet of Things (IoT) is a networking paradigm shift that aims to link almost everything on the world. Due to the limited form of smart devices, energy-efficient routing would be critical to their effective implementation. Clustering techniques divide a network's nodes into groups or clusters, each of which is led by a specified node known as the cluster head. Clustering methods have been presented in the area of Wireless Sensor Networks (WSN), while their application in IoT might solve comparable problems. By transferring a major portion of communication overhead to the cluster head, clustering would promote topology maintenance and energy efficient routing. Therefore, this article aims to establish a novel clustering model in IoT, in which the cluster head selection is performed via a proposed Sandpiper Optimization with Cycle Crossover Process (SOCCP) model. Moreover, this cluster head selection includes certain constraints including (i) Distance (ii) Energy (iii) security (based on the risk levels), and (iv) Cluster Radius, respectively. At first, the clustering is done by the Optimized Fuzzy C-Means (FCM) algorithm. Here, the determination of the cluster head and cluster radius is tuned optimally via an adopted SOCCP model. At the end, the results of the adopted technique are computed to extant based on various metrics including alive node analysis, risk analysis, distance analysis, cluster radius analysis, as well as normalized energy analysis, respectively.

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“…FCM 26 is an approach which uses fuzzy theory to evaluate and represent crucial data, and it is one of several clustering methods. Compared to K‐means, hierarchical clustering (HC), self‐organizing map, and other clustering models, 27,28 FCM more accurately depicts the unpredictability of the manufacturing process and more accurately mimics the actual world.…”

Section: Introductionmentioning

confidence: 99%

A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

Gorikapudi,

Kondaveeti

2023

Concurrency and Computation

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SummaryThe Internet of Things (IoT) is a paradigm change in networking that aspires to connect almost everything on the globe. Clustering methods have been presented in the context of wireless sensor networks, and their application to the IoT could solve similar problems. However, existing clustering protocols have issues with the clustering structure that affect their performance, and some approaches pose a risk of privacy leakage during the data handling process. Therefore, this article aims to establish a novel secure and energy‐aware clustering model in the IoT, in which cluster head selection is performed via a proposed sandpiper optimization with cycle crossover process (SOCCP) model. Moreover, this cluster head selection includes certain constraints, including distance, energy, security, and cluster radius, respectively. The clustering is done by the optimized fuzzy C‐means algorithm. Here, the determination of the cluster head and cluster radius is tuned optimally via an adopted SOCCP model. Final results of the employed technique are calculated using a variety of metrics, such as alive node analysis, risk analysis, distance analysis, cluster radius analysis, as well as normalized energy analysis, respectively.

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Improved Butterfly Optimization Algorithm: PI Controller for 7-Level Inverter

Cited by 4 publications

References 4 publications

Compressed Sensing Reconstruction Approach using Self Adaptive Butterfly Optimization Algorithm for Bio-Signals

Compressed Sensing Reconstruction Approach using Self Adaptive Butterfly Optimization Algorithm for Bio-Signals

Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model

A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

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