Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA)

Karthick, R.; Meenalochini, P

doi:10.4038/jnsfsr.v48i4.10340

Cited by 37 publications

(7 citation statements)

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“…Because of these limits, Battery ESS cannot be concurrently charged and discharged at every time period. In Equation (25), 𝜇 1 (T) and 𝜇 2 (T) represents the charging and discharging state-of-battery ESS. 64 A value of one means Battery ESS is charging or discharging, whereas a value of 0 indicates the inverse.…”

Section: Modeling Of Essmentioning

confidence: 99%

“…Because the batteries in EVs only retain roughly 70% of their initial charge after 10 years of usage, switching batteries is utilized in stationary energy-storage-systems to equalize the peak-demand of EV-FCS. 24,25 ESS and renewable energy sources can be used to create more efficient direct current charging stations, according to a study that was presented. 26 The four distinct power converters that make up the FCS are controlled by an entire structural design for a fast-charging-station with the wrapper of solar PV and local-energy-storage.…”

Section: Introductionmentioning

confidence: 99%

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Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

Gopal,

Jawahar,

Athmanathan

et al. 2023

Optim Control Appl Methods

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A hybrid method is proposed for electric‐vehicle (EV) fast charging station (FCS)‐based power electronics converters with energy‐storage‐systems (ESS) and renewable‐energy‐sources (RESs). The proposed approach is the combination of the fire hawk optimizer (FHO) and gradient boost decision tree (GBDT) algorithms; hence called as FHO‐GBDT approach. The key objective of the FHO‐GBDT approach is to lessen the peak power demand on the grid. The proposed method is incorporated into EV‐FCS with the capability of a mixture of RESs and energy‐storage‐systems. The capacities of energy‐storage aid in improving power‐demand by lessening the demand for peak power. The structure of the energy storage system minimizes the net cost of the DC micro‐grid (MG). The ESS is mostly composed of batteries, which are reused by EVs. The proposed approach and the ESS enable a decrease in obtaining the greatest amount of power possible from the power‐grid (PG). By then, the performance of the proposed approach is simulated in MATLAB, and it is compared to various existing methods. The simulation result shows that the proposed method offers more power than the existing methods.

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Section: Modeling Of Essmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

Gopal,

Jawahar,

Athmanathan

et al. 2023

Optim Control Appl Methods

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“…The fundamental reason for this could be the two key limitations of CNN in radiology, which are a lack of a big data set for thermography and the relocation problem (Karthick & Sundararajan, 2021). Regardless, CNN has been the most widely used model in small datasets together with image pattern recognition for the past 2 years, because they had a lot of improvements concurrently reduces the quality of original images and required image training samples (Karthick & Meenalochini, 2020). To overcome these problems, deep learning based breast cancer categorization facilitates to recognize the types of disease.…”

Section: Introductionmentioning

confidence: 99%

Infrared thermal images using PCSAN‐Net‐DBOA: An approach of breast cancer classification

Vijayarajan,

Manoj Kumar,

Sudha

et al. 2024

Microscopy Res & Technique

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This manuscript proposes thermal images using PCSAN‐Net‐DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR‐IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched‐filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one‐dimensional quantum integer wavelet S‐transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD‐PCSANN‐DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD‐PCSANN‐DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD‐CNN), breast cancer classification from thermal images utilizing Grunwald‐Letnikov assisted dragonfly algorithm‐based deep feature selection (BCD‐VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD‐SqueezeNet), respectively.Research Highlights The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD‐PCSANN‐DBO method is implemented using Python.

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“…The motivation of Web services often experience dynamic and unpredictable workloads, such as sudden traffic spikes during marketing campaigns or events. Traditional methods of scaling infrastructure may not be agile enough to handle these fluctuations, making it imperative to explore more adaptive and intelligent solutions 29,30 …”

Section: Introductionmentioning

confidence: 99%

“…Traditional methods of scaling infrastructure may not be agile enough to handle these fluctuations, making it imperative to explore more adaptive and intelligent solutions. 29,30 This research endeavor aims to explore Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network proposes a novel approach to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services.…”

Section: Introductionmentioning

confidence: 99%

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

Dhakshnamoorthy,

Sridhar,

Ramanathan

et al. 2024

Quality & Reliability Eng

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Web service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively.

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Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA)

Cited by 37 publications

References 0 publications

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

Infrared thermal images using PCSAN‐Net‐DBOA: An approach of breast cancer classification

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

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