publications

2023

1. 

   Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

   Vu Linh Le, Daewoo Kim, Eunsung Cho, and 7 more authors

   Journal of Medical Internet Research, 2023

   Bib HTML

   @article{le2023real,
     title = {Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation},
     author = {Le, Vu Linh and Kim, Daewoo and Cho, Eunsung and Jang, Hyeryung and Reyes, Roben Delos and Kim, Hyunggug and Lee, Dongheon and Yoon, In-Young and Hong, Joonki and Kim, Jeong-Whun},
     journal = {Journal of Medical Internet Research},
     volume = {25},
     pages = {e44818},
     year = {2023},
     publisher = {JMIR Publications Toronto, Canada},
   }

2021

1. ICTC

   Dynamic graph neural network for super-pixel image classification

   Le Vu Linh, and  others

   In 2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021

   Abs Bib

   Convolutional Neural Networks (CNN) have achieved a huge success in computer vision tasks. In spite of the fact that some CNN models can out-perform human in many specific tasks, the traditional CNN models can only handle images with fixed dimensions, i.e. width and height. Current state-of-the-art CNN models overcome the issue by resizing input images to maintain the consistency of dimensions among inputs. This approach has the main drawback of over up-sampling or over down-sampling with images whose dimensions are far too different to the standard of the models. Moreover, most of CNN models are built over-parameterized, which means most of them are heavier than necessary. In this paper, we aim to make use of graph neural networks to broaden the very new research field of applying the networks on visual tasks. We propose a position-aware dynamic graph propagation scheme to handle super-pixel images created by popular super-pixel segmentation algorithms. Although our model did not out-perform state-of-the-art traditional CNN models on all datasets due to the inevitable information loss of segmentation step, we achieved a huge improvement in accuracy compared to existing graph neural network methods and achieved state-of-the-art accuracy on Superpixel MNIST-75 dataset by 23% error rate drop; we also reduced the number of parameters by 99% compared to VGG16 model.

   @inproceedings{le2021dynamic,
     title = {Dynamic graph neural network for super-pixel image classification},
     author = {Linh, Le Vu and others},
     booktitle = {2021 International Conference on Information and Communication Technology Convergence (ICTC)},
     pages = {1095--1099},
     year = {2021},
     organization = {IEEE}
   }

2. ICTC

   Neural architecture search for computation offloading of dnns from mobile devices to the edge server

   KyungChae Lee, Heejae Kim, Chan-Hyun Youn, and 1 more author

   In 2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021

   Bib

   @inproceedings{lee2021neural,
     title = {Neural architecture search for computation offloading of dnns from mobile devices to the edge server},
     author = {Lee, KyungChae and Kim, Heejae and Youn, Chan-Hyun and others},
     booktitle = {2021 International Conference on Information and Communication Technology Convergence (ICTC)},
     pages = {134--139},
     year = {2021},
     organization = {IEEE}
   }