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All Journal International Journal of Applied Power Engineering (IJAPE) International Journal Science and Technology (IJST)
Chandini, Shaik
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Control of a stand-alone variable speed wind turbine generator system Sunil Kumar, Chava; Banka, Sujatha; Chandini, Shaik; Vaishnavi, Jakkoju; Himabindu, Gorla; Meghana, Gudibandi
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i3.pp727-736

Abstract

The focus of the work is on optimizing the wind power system to generate high-quality power from renewable energy sources. This article describes how to control a stand-alone PMSG wind turbine system using perturb and observe (P&O) maximum power point tracking (MPPT) controller. This aids in the regulation of output voltage levels and the maximum power provided to the load. The present study employs P&O MPPT control algorithm to optimize energy extraction from the wind resource, while simultaneously ensuring a stable voltage throughout the load. The goal of MPPT approaches is to establish a reference speed so that the wind energy conversion system (WECS) control system can follow the MPPT trajectory. The MPPT controllers can keep the system running smoothly irrespective of the wind speed fluctuations. There is a significant power output improvement over conventional controllers when using the proposed MPPT controller, according to the comparison results. The DC-DC boost converter was implemented for enhancing the low AC voltage given by the permanent magnet synchronous generator (PMSG).
Brain Tumor Detection using Deep Learning Sudha, Ms. K; Latha Maheswari, T.; M, Harish; Chandini, Shaik; MS, Jishnu
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2147

Abstract

Brain tumor detection using deep learning has emerged as a crucial approach to improving early diagnosis and treatment planning. This project presents a novel hybrid deep learning model based on the ShuffleNet architecture to enhance the accuracy and efficiency of brain tumor detection from medical images. Traditional machine learning (ML) models rely on hand- crafted features, which are often time-consuming and less effective. Deep learning, on the other hand, automates feature extraction, improving detection accuracy and reliability. The proposed system leverages the ShuffleNet framework, known for its lightweight and high-performance characteristics, making it well-suited for real- time applications. To further enhance the model’s capability, we modified ShuffleNet by removing its last five layers and replacing them with 15 newly designed layers that increase expressiveness and feature extraction capacity. Additionally, we integrated a leaky ReLU activation function in the feature map to mitigate the vanishing gradient problem and improve model generalization. These enhancements result in superior feature representation and higher classification accuracy for brain tumor pathology detection. The dataset used for model training comprises MRI scans labeled with different tumor types. Preprocessing techniques such as normalization, augmentation, and contrast enhancement are applied to ensure robust training. The modified ShuffleNet model demonstrates higher precision, recall, and F1-score compared to traditional CNN-based models, while maintaining computational efficiency. This system can be deployed in real-time clinical settings to assist radiologists in early tumor detection, reducing human error and enhancing diagnostic speed. The integration of deep learning into medical imaging represents a significant step toward automated, accurate, and efficient brain tumor detection, ultimately improving patient outcomes.
Co-Authors Banka, Sujatha Himabindu, Gorla Latha Maheswari, T. M, Harish Meghana, Gudibandi MS, Jishnu Sudha, Ms. K Sunil Kumar, Chava Vaishnavi, Jakkoju