| Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning J Hong, D Lee, ER Jeong, Y Yi Applied energy 278, 115646, 2020 | 168 | 2020 |
| Accurate remaining range estimation for electric vehicles J Hong, S Park, N Chang 2016 21st Asia and South Pacific design automation conference (ASP-DAC), 781-786, 2016 | 72 | 2016 |
| Deep learning approaches to detect atrial fibrillation using photoplethysmographic signals: algorithms development study S Kwon, J Hong, EK Choi, E Lee, DE Hostallero, WJ Kang, B Lee, ... JMIR mHealth and uHealth 7 (6), e12770, 2019 | 69 | 2019 |
| Detection of atrial fibrillation using a ring-type wearable device (CardioTracker) and deep learning analysis of photoplethysmography signals: prospective observational proof … S Kwon, J Hong, EK Choi, B Lee, C Baik, E Lee, ER Jeong, BK Koo, S Oh, ... Journal of Medical Internet Research 22 (5), e16443, 2020 | 53 | 2020 |
| Parameterized slot scheduling for adaptive and autonomous TSCH networks J Jung, D Kim, J Hong, J Kang, Y Yi IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops …, 2018 | 22 | 2018 |
| End-to-end sleep staging using nocturnal sounds from microphone chips for mobile devices J Hong, HH Tran, J Jung, H Jang, D Lee, IY Yoon, JK Hong, JW Kim Nature and Science of Sleep, 1187-1201, 2022 | 10 | 2022 |
| Real-time detection of sleep apnea based on breathing sounds and prediction reinforcement using home noises: Algorithm development and validation VL Le, D Kim, E Cho, H Jang, RD Reyes, H Kim, D Lee, IY Yoon, J Hong, ... Journal of Medical Internet Research 25, e44818, 2023 | 5 | 2023 |
| Confidence-based framework using deep learning for automated sleep stage scoring JK Hong, T Lee, RD Delos Reyes, J Hong, HH Tran, D Lee, J Jung, ... Nature and Science of Sleep, 2239-2250, 2021 | 5 | 2021 |
| On self-configuring IoT with dual radios: A cross-layer approach J Jung, J Hong, Y Yi IEEE Transactions on Mobile Computing 21 (11), 4064-4077, 2021 | 5 | 2021 |
| Minimum-energy driving speed profiles for low-speed electric vehicles D Baek, J Hong, N Chang 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 435-435, 2016 | 5 | 2016 |
| Prediction of sleep stages via deep learning using smartphone audio recordings in home environments: model development and validation HH Tran, JK Hong, H Jang, J Jung, J Kim, J Hong, M Lee, JW Kim, ... Journal of Medical Internet Research 25, e46216, 2023 | 3 | 2023 |
| Accuracy of 11 wearable, nearable, and airable consumer sleep trackers: Prospective multicenter validation study T Lee, Y Cho, KS Cha, J Jung, J Cho, H Kim, D Kim, J Hong, D Lee, ... JMIR mHealth and uHealth 11 (1), e50983, 2023 | 2 | 2023 |
| SLEEP STAGING USING END-TO-END DEEP LEARNING MODEL BASED ON NOCTURNAL SOUND FOR SMARTPHONES J Hong, H Tran, J Jeong, H Jang, IY Yoon, JK Hong, JW Kim Sleep 45, A156-A156, 2022 | 1 | 2022 |
| In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography SC Han, D Kim, CS Rhee, SW Cho, VL Le, ES Cho, H Kim, IY Yoon, ... JAMA Otolaryngology–Head & Neck Surgery 150 (1), 22-29, 2024 | | 2024 |
| 0950 Sound-based Sleep Staging at Home Using Smartphone via Deep Learning H Tran, JK Hong, H Jang, J Jung, J Kim, J Hong, M Lee, JW Kim, ... Sleep 46 (Supplement_1), A418-A419, 2023 | | 2023 |
| Sound-Based Sleep Staging By Exploiting Real-World Unlabeled Data JM Kim, D Kim, E Cho, HH Tran, J Hong, D Lee, JK Hong, IY Yoon, ... ICLR 2023 Workshop on Time Series Representation Learning for Health, 2023 | | 2023 |
