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Park, Tae-Ju;Song, Teuk-Seob
- Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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- 2021.05a
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- pp.561-562
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- 2021
CNN (Convolutional Neural Network) is widely used to recognize various images. In this presentation, a single digit handwritten by humans was recognized by applying the CNN technique of deep learning. The deep learning network consists of a convolutional layer, a pooling layer, and a platen layer, and finally, we set an optimization method, learning rate and loss functions.
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Phan, Kim Ngan;Lee, Guee-Sang;Yang, Hyung-Jeong;Kim, Soo-Hyung
- Annual Conference of KIPS
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- 2021.11a
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- pp.1001-1004
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- 2021
Pain is an unpleasant experience for the patient. The recognition and assessment of pain help tailor the treatment to the patient, and they are also challenging in the medical. In this paper, we propose an approach for pain recognition through a deep neural network applied to pre-processed physiological. The proposed approach applies the idea of shortcut connections to concatenate the spatial information of a convolutional neural network and the temporal information of a recurrent neural network. In addition, our proposed approach applies the attention mechanism and achieves competitive performance on the BioVid Heat Pain dataset.
https://doi.org/10.3745/PKIPS.y2021m11a.1001 인용 PDF

Asep Haryono;Grafika Jati;Wisnu Jatmiko
- ETRI Journal
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- v.46 no.2
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- pp.307-322
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- 2024
Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conventional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multi-branch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.
https://doi.org/10.4218/etrij.2022-0446 인용 PDF