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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Sasikala, Mungamuri
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Accurate plant species analysis for plant classification using convolutional neural network architecture Patil, Savitha; Sasikala, Mungamuri
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp160-170

Abstract

Recently, plant identification has become an active trend due to encouraging results achieved in plant species detection and plant classification fields among numerous available plants using deep learning methods. Therefore, plant classification analysis is performed in this work to address the problem of accurate plant species detection in the presence of multiple leaves together, flowers, and noise. Thus, a convolutional neural network based deep feature learning and classification (CNN-DFLC) model is designed to analyze patterns of plant leaves and perform classification using generated fine-grained feature weights. The proposed CNN-DFLC model precisely estimates which the given image belongs to which plant species. Several layers and blocks are utilized to design the proposed CNN-DFLC model. Fine-grained feature weights are obtained using convolutional and pooling layers. The obtained feature maps in training are utilized to predict labels and model performance is tested on the Vietnam plant image (VPN-200) dataset. This dataset consists of a total number of 20,000 images and testing results are achieved in terms of classification accuracy, precision, recall, and other performance metrics. The mean classification accuracy obtained using the proposed CNN-DFLC model is 96.42% considering all 200 classes from the VPN-200 dataset.
An efficient novel dual deep network architecture for video forgery detection Chandrakala, Chandrakala; Sasikala, Mungamuri
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp458-471

Abstract

The technique of video copy-move forgery (CMF) is commonly employed in various industries; digital videography is regularly used as the foundation for vital graphic evidence that may be modified using the aforementioned method. Recently in the past few decades, forgery in digital images is detected via machine intellect. The second issue includes continuous allocation of parallel frames having relevant backgrounds erroneously results in false implications, detected as CMF regions third include as the CMF is divided into inter-frame or intra-frame forgeries to detect video copy is not possible by most of the existing methods. Thus, this research presents the dual deep network (DDN) for efficient and effective video copy-move forgery detection (VCMFD); DDN comprises two networks; the first detection network (DetNet1) extracts the general deep features and second detection network (DetNet2) extracts the custom deep features; both the network are interconnected as the output of DetNet1 is given to DetNet2. Furthermore, a novel algorithm is introduced for forged frame detection and optimization of the falsely detected frame. DDN is evaluated considering the two benchmark datasets REWIND and video tampering dataset (VTD) considering different metrics; furthermore, evaluation is carried through comparing the recent existing model. DDN outperforms the existing model in terms of various metrics.
A novel AI-AVO approach for maximum power generation of PMSG S. Chinamalli, Prashant Kumar; Sasikala, Mungamuri
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp99-114

Abstract

Permanent magnet synchronous generators (PMSGs) are necessary for producing wind energy that is both highly reliable and reasonably priced. An inventive control technique for the driven interior PMSG (IPMSG) is presented here to maximize wind energy output and decrease losses. This research established an innovative optimization strategy for the highest wind power generation with reduced overall loss in PMSG-based Wind power generation systems. Considering, that the tip speed ratio (TPR), rotor speed ???????? , and quadrature axis current ???????? are optimized in the proposed work in such a way to enhance wind power generation. Further, the direct axis current ???????? is calculated from the optimized rotor speed ????????. The minimization of core loss is considered as the fitness function, which is a function of the direct current axis ????????and quadrature current axis ????????. The optimization is carried out using the explored aquila with African vulture optimization (EA-AVO) technique, which is the conceptual incorporation of prevailing techniques, like the aquila optimization algorithm (AOA) and the AVO algorithm. The performance of the proposed method is validated over the conventional methods, in terms of power output, losses, efficiency, and convergence analysis. According, the findings show that the proposed method attains less overall loss of 149.62 at the starting stage of 50 rotor speed, and it was 36.46% higher than AQO, 36.17% higher than AVOA, 36.59% higher than GOA methods 36.42%, and higher than WHO+PI approaches.
Hybrid optimization tuned deep neural network-based wind power generation system for permanent magnet synchronous generator control Chinamalli, Prashant Kumar S. S; Sasikala, Mungamuri
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2599-2615

Abstract

Wind energy, a cost-effective renewable source, has seen substantial growth. permanent magnet synchronous generator (PMSG) equipped wind turbines demonstrate superior performance in variable-speed applications. However, there remains a notable research gap in optimizing the overall system efficiency for such wind energy systems. Therefore, this research presents to develop a deep learning-based optimization technique that improves the efficiency of PMSG-based wind energy systems by minimizing overall system losses and maximizing energy output. Core loss and rotor speed data were fed into a deep neural network for various operating conditions ranging from 50 to 1000 rpm, to determine optimal system parameters. This work introduces a hybrid lyrebird-based coati optimization algorithm (LB-COA) to optimize the deep neural networks (DNN) classifier, combining two advanced optimization techniques to improve model performance. Simulation results validate that the proposed optimization strategy efficiently boosts the system's dynamic performance and overall power efficiency.
Adaptive fuzzy logic controller based BLDC motor to improve the dynamic performance for electric tractor application Yenegur, Ashwini; Sasikala, Mungamuri
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2186-2196

Abstract

Permanent magnet brushless DC (PMBLDC) motors are widely used in a variety of industrial applications due to their high-power density and ease of regulation. The three-phase power semiconductors bridge is the standard way for controlling these motors. In order to initiate the inverter bridge and switch on the power devices, rotor position sensors must be provided with the correct commutation sequence. The power devices commutate progressively 60 degrees, depending on the location of the rotor. The right speed controllers are necessary for the motor to run as efficiently as possible. PI controllers are commonly employed with permanent magnet motors to achieve speed control in simple manner. Nevertheless, these controllers provide challenges in managing control complexity, including nonlinearity, parametric fluctuations, and load disturbances. PI controllers need accurate linear mathematical models. To overcome this, in this paper adaptive fuzzy logic controller (FLC) for controlling the speed of a BLDC motor is presented. When the motor drive system uses the adaptive FLC technology for speed control, it exhibits better dynamic behavior and is more resistant to changes in parameters and load disturbances. The main objectives of this work are to analyze and appraise the functioning of an electric tractor driven by a PMBLDC motor drive using adaptive FLC. The PMBLDC motor drive controllers are simulated using MATLAB/Simulink software.
Co-Authors Chandrakala, Chandrakala Chinamalli, Prashant Kumar S. S patil, savitha S. Chinamalli, Prashant Kumar Yenegur, Ashwini