Applying deep learning-based regional feature recognition from macro-scale image to assist energy saving and emission reduction in industrial energy systems

Chen, Siliang; Zhu, Xu; Chen, Kang; Liu, Zexu; Li, Pengcheng; Liang, Xinbin; Jin, Xinqiao; Du, Zhimin

doi:10.1016/j.jare.2022.07.003

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…This is because our proposed multi-task model depends on features from both image modal and text modal. Without utilising pretrained weights, we build two image classification models, ConvNeXt [22] and ResNet50 [23], for the image modal. Glove and FastText are two common examples of non-context-based models, whereas BERT is a well-known context-based language model, indicating that the performance of these two models largely depends on lengthy reliance in a context.…”

Section: A Implementation Details and Experimental Resultsmentioning

confidence: 99%

Handling Big Tabular Data of Ict Supply Chains: A Multi-Task, Machine-Interpretable Approach

Kohale,

Sharma

2023

AJCT

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The essential details of ICT devices are frequently distilled into large tabular data sets that are distributed throughout supply chains as a result of the features of Information and Communications Technology (ICT) goods. With the explosion of electronic assets, it is crucial to automatically analyse tabular structures. We develop a Table Structure Recognition (TSR) work and a Table Cell Type Classification (CTC) task to convert the tabular data in electronic documents into a machine-interpretable format and give layout and semantic information for information extraction and interpretation. For the TSR job, complicated table structures are represented using a graph. Table cells are divided into three groups-Header, Attribute, and Data-based on how they work for the CTC job. Then, utilising the text modal and picture modal characteristics, we provide a multi-task model to accomplish the two tasks concurrently. Our test findings demonstrate that, using the ICDAR2013 and UNLV datasets, our suggested strategy can beat cuttingedge approaches.

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Section: A Implementation Details and Experimental Resultsmentioning

confidence: 99%