Conference Papers

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Author: search.filters.author.Parida, S.

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    Laboratory investigation of photovoltaic panel performance under the shaded condition
    (Institute of Electrical and Electronics Engineers Inc., 2020) Tripathi, A.K.; Mangalpady, M.; Ray, S.; Parida, S.
    The most encouraging use of solar energy is its conversion into electrical energy by using solar photovoltaic (PV) panel. The performance of solar-based PV panel is undoubtedly influenced by the quantity of solar radiation, which is reaching on the panel surface. The occurrence of shading over the panel surface is a vital environmental phenomenon which affects the penetration of solar radiation to reach the overall surface area of photovoltaic cells. The shading on PV panels may happen due to trees, the formation of mists, accumulation of dirt elements on the panel surface, close by long-standing structures, shadows of different panels in its region, neighbouring structures and so on. This paper is mainly focused on the study of shading impact on the panel performance. Further, this paper also observed the influence of shading on the variation of surface temperature of the PV panel. The present study shown a significant reduction of 41.40% in the maximum power output (Pmax) of the panel due to 25% shading strength of the single cell in the panel. Moreover, it was also observed that the increase in the percentage shading strength over the panel surface shifts the maximum power point (MPP), of the panel characteristics, towards the lower output voltage, which affects the effective operation of the charge controller. Further, it was seen that the shading impact degrades the performance of the panel as well as in charge of the rise of the surface temperature of the panel. In the present investigation, it was observed that the temperature of the unshaded cell rises at the rate of 1.753%, due to the shading phenomena over the panel surface. © 2020 IEEE.
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    Attention Assisted Patch-Wise CNN for the Segmentation of Fluids from the Retinal Optical Coherence Tomography Images
    (Springer Science and Business Media Deutschland GmbH, 2024) Anoop, B.N.; Parida, S.; Ajith, B.; Girish, G.N.; Kothari, A.R.; Kavitha, M.S.; Rajan, J.
    Optical Coherence Tomography (OCT) is an important imaging modality in ophthalmology to visualize the abnormalities present in the retina. One of the major reasons for blindness is the accumulation of fluids in the various layers of the retina called retinal cysts. Accurate estimation of the type of cyst and its volume is important for effective treatment planning. In this paper, we propose attention assisted convolutional neural network-based architecture to detect and quantify three types of retinal cysts namely the intra-retinal cyst, sub-retinal cyst and pigmented epithelial detachment from the OCT images of the human retina. The proposed architecture has an encoder-decoder structure with an attention and a multi-scale module. The qualitative and quantitative performance of the model is evaluated on the publicly available RETOUCH retinal OCT fluid detection challenge data set. The proposed model outperforms the state-of-the-art methods in terms of precision, recall, and dice coefficient. Furthermore, the proposed model is computationally efficient due to its less number of model parameters. © Springer Nature Switzerland AG 2024.