Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Error Correction

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    Smart Energy Meter Calibration: An Edge Computation Method: Poster
    (Association for Computing Machinery, Inc, 2021) Dubara, H.V.; Parihar, M.; Ramamritham, K.
    Smart meters are the backbone of smart grids. They provide real time electricity consumption data and and are widely used for measuring, monitoring and analyzing energy consumption. Sometimes, they enable users to perform corrective actions. But, to facilitate proper data analysis, it is imperative that data be accurate or have minimum error. This paper presents an edge deployed smart meter error correction algorithm that utilises Clustering (using K-Means algorithm) and Feed-Forward Artificial Neural Networks (ANN). An edge device, a Raspberry Pi Module, connects smart meters to the internet. The algorithm maps (possibly erroneous) readings of our in-house developed meters to readings of calibrated standard off-the-shelf (Schneider) meters. Usage of Clustering with ANN has helped substantially improve the accuracy of the readings from a previously used linear regression designed for the same purpose. An accuracy of 70-75% was achieved while using linear regression, whereas the proposed algorithm obtains accuracy in the range of 84.47-88%. The neural networks are also less complex, making them suitable for deployment in Raspberry Pi 3B based embedded hardware systems. © 2021 ACM.
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    Context Sensitive Tamil Language Spellchecker Using RoBERTa
    (Springer Science and Business Media Deutschland GmbH, 2023) Rajalakshmi, R.; Sharma, V.; Anand Kumar, M.
    A spellchecker is a tool that helps to identify spelling errors in a piece of text and lists out the possible suggestions for that word. There are many spell-checkers available for languages such as English but a limited number of spell-checking tools are found for low-resource languages like Tamil. In this paper, we present an approach to develop a Tamil spell checker using the RoBERTa (xlm-roberta-base) model. We have also proposed an algorithm to generate the test dataset by introducing errors in a piece of text. The spellchecker finds out the mistake in a given text using a corpus of unique Tamil words collected from different sources such as Wikipedia and Tamil conversations, and lists out the suggestions that could be the potential contextual replacement of the misspelled word using the proposed model. On introducing a few errors in a piece of text collected from a Wikipedia article and testing it on our model, an accuracy of 91.14% was achieved for error detection. Contextually correct words were then suggested for these erroneous words detected. Our spellchecker performed better than some of the existing Tamil spellcheckers in terms of both higher accuracy and lower false positives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.