Browsing by Author "Bhuvaneswari, R."

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    An investigation into battery modelling for electric vehicles and applications for electric power systems /
    (IEEE, 2022-03-29) Bhuvaneswari, R.
    A system for battery management is vital in reliable and safe battery operation. They are being extensively applied in high power applications, hybrid electric vehicles, and many more arenas to ensure intermittent power supply. The paper aims at providing a detailed study of the different batteries available in the market and their efficacy when exposed to different environments. The main parameters taken into consideration are the service life, nominal voltage, charging and discharging rates, and temperatures. Firstly, types of battery modeling are studied comprehensively followed by various batteries used in the industry for EVs and Power system applications. Various battery models such as electrical, thermal, and coupled electrothermal model are discussed. Subsequently, the battery condition estimates for the charging state, health estimation, and internal temperature are extensively studied. Then, the major types of battery modeling along with traditional battery charging and optimization techniques are presented with necessary equations and simulation proofs. The practical results implemented are also presented for reference.
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    Super-pixel segmentation based skin texture pattern recognition /
    (IEEE, 2021-12-02) Bhuvaneswari, R.
    Super-pixel segmentation is widely used nowadays in image processing to enhance segmentation accuracy. A new detection model is proposed for skin texture pattern recognition of Leopard, Cheetah, and Jaguar. In this model, a combination of Histogram of Gradient (HOG) and superpixel segmentation is used for extracting the features and the segmentation task of the target animal. This method does not require several superpixels to be created in advance, whereas it can automatically partition the image to its content into a suitable number of superpixels without any over or under segmentation. Then, the obtained features are fed into a Support Vector Machine (SVM) classifier to classify the skin texture pattern of Leopard, Cheetah, and Jaguar. The validation is performed which shows that the classifier achieves an accuracy of 96.67 %.