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3,477 results on '"Lee, Sang Hun"'

1. Diamond molecular balance: Revolutionizing high-resolution mass spectrometry from MDa to TDa at room temperature

Author

Lee, Donggeun, Jeon, Seung-Woo, Yi, Chang-Hwan, Kim, Yang-Hee, Choi, Yeeun, Lee, Sang-Hun, Cha, Jinwoong, Shim, Seung-Bo, Suh, Junho, Kim, Il-Young, Kang, Dongyeon Daniel, Jung, Hojoong, Jeong, Cherlhyun, Ahn, Jae-pyoung, Park, Hee Chul, Han, Sang-Wook, and Kim, Chulki

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract

The significance of mass spectrometry lies in its unparalleled ability to accurately identify and quantify molecules in complex samples, providing invaluable insights into molecular structures and interactions. Here, we leverage diamond nanostructures as highly sensitive mass sensors by utilizing a self-excitation mechanism under an electron beam in a conventional scanning electron microscope (SEM). The diamond molecular balance (DMB) exhibits an exceptional mass resolution of 0.36 MDa, based on its outstanding mechanical quality factor and frequency stability, along with an extensive dynamic range from MDa to TDa. This positions the DMB at the forefront of molecular balances operating at room temperature. Notably, the DMB demonstrates its ability to measure the mass of a single bacteriophage T4 by precisely locating the analyte on the device. These findings highlight the groundbreaking potential of the DMB as a revolutionary tool for mass spectrometry at room temperature., Comment: 16 pages, 4 figures

Published
2024

2. False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy

Author

Oh, Jiyong, Lee, Junhaeng, Byun, Woongchan, Kong, Minsang, and Lee, Sang Hun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this study aims to overcome the limitations of ground-truth sampling and improve the performance of 3D object detection models by developing a new augmentation technique called false-positive sampling. False-positive sampling involves retraining the model using point clouds that are identified as false positives in the model's predictions. We propose an algorithm that utilizes both ground-truth and false-positive sampling and an algorithm for building the false-positive sample database. Additionally, we analyze the principles behind the performance enhancement due to false-positive sampling. Our experiments demonstrate that models utilizing false-positive sampling show a reduction in false positives and exhibit improved object detection performance. On the KITTI and Waymo Open datasets, models with false-positive sampling surpass the baseline models by a large margin.

Published
2024

22. A robust multivariate structure of interindividual covariation between psychosocial characteristics and arousal responses to visual narratives

Author

Kim, Jinyoung, Bae, Eunseong, Kim, Yeonhwa, Lim, Chae Young, Hur, Ji-Won, Kwon, Jun Soo, and Lee, Sang-Hun

Subjects
Social and Personality Psychology, Psychology, Clinical Research, Neurosciences, Basic Behavioral and Social Science, Mental Health, Behavioral and Social Science, Mind and Body, Mental health, Good Health and Well Being, Adolescent, Adult, Arousal, Emotions, Female, Humans, Individuality, Male, Narration, Personality, Psychometrics, Surveys and Questionnaires, Young Adult, General Science & Technology
Abstract

People experience the same event but do not feel the same way. Such individual differences in emotion response are believed to be far greater than those in any other mental functions. Thus, to understand what makes people individuals, it is important to identify the systematic structures of individual differences in emotion response and elucidate how such structures relate to what aspects of psychological characteristics. Reflecting this importance, many studies have attempted to relate emotions to psychological characteristics such as personality traits, psychosocial states, and pathological symptoms across individuals. However, systematic and global structures that govern the across-individual covariation between the domain of emotion responses and that of psychological characteristics have been rarely explored previously, which limits our understanding of the relationship between individual differences in emotion response and psychological characteristics. To overcome this limitation, we acquired high-dimensional data sets in both emotion-response (8 measures) and psychological-characteristic (68 measures) domains from the same pool of individuals (86 undergraduate or graduate students) and carried out the canonical correlation analysis in conjunction with the principal component analysis on those data sets. For each participant, the emotion-response measures were quantified by regressing affective-rating responses to visual narrative stimuli onto the across-participant average responses to those stimuli, while the psychological-characteristic measures were acquired from 19 different psychometric questionnaires grounded in personality, psychosocial-factor, and clinical-problem taxonomies. We found a single robust mode of population covariation, particularly between the 'accuracy' and 'sensitivity' measures of arousal responses in the emotion domain and many 'psychosocial' measures in the psychological-characteristics domain. This mode of covariation suggests that individuals characterized with positive social assets tend to show polarized arousal responses to life events.

Published
2022

23. SBNet: Segmentation-based Network for Natural Language-based Vehicle Search

Author

Lee, Sangrok, Woo, Taekang, and Lee, Sang Hun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2.10, I.5.1, I.4.8
Abstract

Natural language-based vehicle retrieval is a task to find a target vehicle within a given image based on a natural language description as a query. This technology can be applied to various areas including police searching for a suspect vehicle. However, it is challenging due to the ambiguity of language descriptions and the difficulty of processing multi-modal data. To tackle this problem, we propose a deep neural network called SBNet that performs natural language-based segmentation for vehicle retrieval. We also propose two task-specific modules to improve performance: a substitution module that helps features from different domains to be embedded in the same space and a future prediction module that learns temporal information. SBnet has been trained using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three unique natural language descriptions each and tested 530 unique vehicle tracks and their corresponding query sets. SBNet achieved a significant improvement over the baseline in the natural language-based vehicle tracking track in the AI City Challenge 2021., Comment: 7 pages, 4 figures, CVPR Workshop Paper

Published
2021
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24. Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification

Author

Lee, Sangrok, Woo, Taekang, and Lee, Sang Hun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2.10, I.5.1, I.4.8
Abstract

Vehicle re-identification helps in distinguishing between images of the same and other vehicles. It is a challenging process because of significant intra-instance differences between identical vehicles from different views and subtle inter-instance differences between similar vehicles. To solve this issue, researchers have extracted view-aware or part-specific features via spatial attention mechanisms, which usually result in noisy attention maps or otherwise require expensive additional annotation for metadata, such as key points, to improve the quality. Meanwhile, based on the researchers' insights, various handcrafted multi-attention architectures for specific viewpoints or vehicle parts have been proposed. However, this approach does not guarantee that the number and nature of attention branches will be optimal for real-world re-identification tasks. To address these problems, we proposed a new vehicle re-identification network based on a multiple soft attention mechanism for capturing various discriminative regions from different viewpoints more efficiently. Furthermore, this model can significantly reduce the noise in spatial attention maps by devising a new method for creating an attention map for insignificant regions and then excluding it from generating the final result. We also combined a channel-wise attention mechanism with a spatial attention mechanism for the efficient selection of important semantic attributes for vehicle re-identification. Our experiments showed that our proposed model achieved a state-of-the-art performance among the attention-based methods without metadata and was comparable to the approaches using metadata for the VehicleID and VERI-Wild datasets., Comment: 15 pages, 6 figures

Published
2021
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25. A Study on Mode Awareness of Multi-level Automated Vehicles

Author

Eom, Hwisoo, Zhong, Yupeng, Hou, Xintong, Lee, Sang Hun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Stephanidis, Constantine, editor, Antona, Margherita, editor, Ntoa, Stavroula, editor, and Salvendy, Gavriel, editor

Published
2023
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30. User interface for in-vehicle systems with on-wheel finger spreading gestures and head-up displays

Author

Lee, Sang Hun and Yoon, Se-One

Subjects
Computer Science - Human-Computer Interaction, H.5.2
Abstract

Interacting with an in-vehicle system through a central console is known to induce visual and biomechanical distractions, thereby delaying the danger recognition and response times of the driver and significantly increasing the risk of an accident. To address this problem, various hand gestures have been developed. Although such gestures can reduce visual demand, they are limited in number, lack passive feedback, and can be vague and imprecise, difficult to understand and remember, and culture-bound. To overcome these limitations, we developed a novel on-wheel finger spreading gestural interface combined with a head-up display (HUD) allowing the user to choose a menu displayed in the HUD with a gesture. This interface displays audio and air conditioning functions on the central console of a HUD and enables their control using a specific number of fingers while keeping both hands on the steering wheel. We compared the effectiveness of the newly proposed hybrid interface against a traditional tactile interface for a central console using objective measurements and subjective evaluations regarding both the vehicle and driver behaviour. A total of 32 subjects were recruited to conduct experiments on a driving simulator equipped with the proposed interface under various scenarios. The results showed that the proposed interface was approximately 20% faster in emergency response than the traditional interface, whereas its performance in maintaining vehicle speed and lane was not significantly different from that of the traditional one., Comment: This paper was published in the Journal of Computational Design and Engineering (Oxford University Press) in December 2020 https://academic.oup.com/jcde/article/7/6/700/5859941

Published
2021
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34. StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification

Author

Lee, Sangrok, Park, Eunsoo, Yi, Hongsuk, and Lee, Sang Hun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, I.2.10, I.5.1, I.4.8
Abstract

Vehicle re-identification aims to obtain the same vehicles from vehicle images. This is challenging but essential for analyzing and predicting traffic flow in the city. Although deep learning methods have achieved enormous progress for this task, their large data requirement is a critical shortcoming. Therefore, we propose a synthetic-to-real domain adaptation network (StRDAN) framework, which can be trained with inexpensive large-scale synthetic and real data to improve performance. The StRDAN training method combines domain adaptation and semi-supervised learning methods and their associated losses. StRDAN offers significant improvement over the baseline model, which can only be trained using real data, for VeRi and CityFlow-ReID datasets, achieving 3.1% and 12.9% improved mean average precision, respectively., Comment: 7 pages, 2 figures, CVPR Workshop Paper (Revised)

Published
2020
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43. Multi-group analysis using generalized additive kernel canonical correlation analysis

Author

Bae, Eunseong, Hur, Ji-Won, Kim, Jinyoung, Kwon, Jun Soo, Lee, Jongho, Lee, Sang-Hun, and Lim, Chae Young

Subjects
Information and Computing Sciences, Machine Learning
Abstract

Multivariate analysis has been widely used and one of the popular multivariate analysis methods is canonical correlation analysis (CCA). CCA finds the linear combination in each group that maximizes the Pearson correlation. CCA has been extended to a kernel CCA for nonlinear relationships and generalized CCA that can consider more than two groups. We propose an extension of CCA that allows multi-group and nonlinear relationships in an additive fashion for a better interpretation, which we termed as Generalized Additive Kernel Canonical Correlation Analysis (GAKCCA). In addition to exploring multi-group relationship with nonlinear extension, GAKCCA can reveal contribution of variables in each group; which enables in-depth structural analysis. A simulation study shows that GAKCCA can distinguish a relationship between groups and whether they are correlated or not. We applied GAKCCA to real data on neurodevelopmental status, psychosocial factors, clinical problems as well as neurophysiological measures of individuals. As a result, it is shown that the neurophysiological domain has a statistically significant relationship with the neurodevelopmental domain and clinical domain, respectively, which was not revealed in the ordinary CCA.

Published
2020

46. More Than 50 Percent Reduction in LDL Cholesterol in Patients With Target LDL <70 mg/dL After a Stroke

Author

Amarenco, Pierre, Lavallée, Philippa C., Kim, Jong S., Labreuche, Julien, Charles, Hugo, Giroud, Maurice, Lee, Byung-Chul, Mahagne, Marie-Hélène, Meseguer, Elena, Nighoghossian, Norbert, Steg, Philippe Gabriel, Vicaut, Éric, Bruckert, Eric, Kim, Jong S, Touboul, Pierre-Jean, Leys, Didier, Béjot, Yannick, Pico, Fernando, Touzé, Emmanuel, Ducrocq, Gregory, Abtan, Jérémy, Varenne, Olivier, Touboul, Pierre-Jean, Kemmel, Agnes, Syana, Fausta, Ledra, Manele, Nagasara, Tharani, Ledjeroud, Mervette, Samia, Bahous, Hadia, Hafirassou, Hazare, Benyoub, Jaghouni, Ikrame El, Yelles, Nessima, Zemouri, Sofia, Ladjeroud, Mervette, Kerai, Salim, In, YunJeong, Hobeanu, Cristina, Guidoux, Celine, Cabrejo, Lucie, Lapergue, Bertrand, Sabben, Candice, Gonzalez-Valcarcel, Jaime, Rigual, Ricardo, Sirimarco, Gaia, Martin-Bechet, Anna, Viedma, Elena, Avram, Ioan, Samson, Yves, Rosso, Charlotte, Crozier, Sophie, Leder, Sara, Léger, Anne, Deltour, Sandrine, Mutlu, Gurkan, Yger, Marion, Zavanone, Chiara, Baronnet, Flore, Pires, Christine, Lapergue, Bertrand, Wang, Adrien, Evrard, Serge, Tchikviladze, Maya, Bourdain, Frédéric, Lopez, Delphine, Pico, Fernando, de la Tour, Laetitia Bayon, Chadenat, Marie-Laure, Duong, Duc Long, Genty, Solène, Hirel, Catherine, Mutlu, Gurkan, Nifle, Chantal, Servan, Jérôme, Stanciu, Daniela, Sudacevschi, Veronica, Tir, Mélissa, Troussière, Anne-Cécile, Yeung, Jennifer, Zeghoudi, Anne-Céline, Tidafi-Bayou, Ikram, Lachaud, Sylvain, Cho, Tae-Hee, Mechtouff, Laura, Ritzenthaller, Thomas, Derex, Laurent, Albanesi, Carlo, Ong, Elodie, Benoit, Amandine, Berhoune, Nadia, Felix, Sandra, Esteban-Mader, Maud, Sibon, Igor, Kazadi, Annabelle, Rouanet, François, Renou, Pauline, Debruxelles, Sabrina, Poli, Mathilde, Sagnier, Sharmila, Mas, Jean-Louis, Domigo, Valérie, Lamy, Catherine, Bodiguel, Eric, Grimaud, Jérôme, Bohotin, Valentin, Obadia, Michael, Sabben, Candice, Morvan, Erwan, Rodier, Gilles, Vadot, Wilfried, Hénon, Hilde, Cordonnier, Charlotte, Dumont, Frédéric, Bodenant, Marie, Lucas, Christian, Moulin, Solène, Dequatre, Nelly, Alamowitch, Sonia, Muresan, Jean-Paul, Drouet, Thomas, Gallea, Magalie, Dalloz, Marie-Amélie, Delorme, Stephen, Yger, Marion, Béjot, Yannick, Loisel, Philippe, Bonnin, Carine, Bernigal, Virginie, Osseby, Guy Victor, Hervieu-BègueMarsac, Marie, Garnier, Pierre, Accassat, Sandrine, Epinat, Magali, Varvat, Jérôme, Marinescu, Doïna, Triquenot-Bagan, Aude, Ozkul- Wermester, Ozlem, Philippeau, Frédéric, Olaru, Angel, Vieillart, Anne, Lannuzel, Annie, Demoly, Alice, Wolff, Valérie, Diaconu, Mihaela, Bataillard, Marc, Montoro, Francisco Macian, Faugeras, Frédéric, Gimenez, Laeticia, Abdallah-Lebeau, Françoise, Timsit, Serge, Viakhireva-Dovganyuk, Irina, Tirel-Badets, Anne, Merrien, François-Mathias, Goas, Philippe, Rouhart, François, Jourdain, Aurore, Guillon, Benoit, Hérissson, Fanny, Sevin-Allouet, Mathieu, Nasr, Nathalie, Olivot, Jean-Marc, Lecluse, Alderic, Marc, Guillaume, Touzé, Emmanuel, de la Sayette, Vincent, Apoil, Marion, Lin, Li, Cogez, Julien, Guettier, Sophie, Godefroy, Olivier, Lamy, Chantal, Bugnicourt, Jean-Marc, Taurin, Grégory, Mérienne, Marc, Gere, Julien, Chessak, Anne-Marie, Habet, Tarik, Ferrier, Anna, Bourgois, Nathalie, Minier, Dominique, Caillier-Minier, Marie, Contégal- Callier, Fabienne, Vion, Philippe, Vaschalde, Yvan, Amrani, Mohammed El, Emilie, Zuber, Mathieu, Bruandet, Marie, Join- Lambert, Claire, Garcia, Pierre-Yves, Serre, Isabelle, Faucheux, Jean-Marc, Radji, Fatia, Leca-Radu, Elena, Debroucker, Thomas, Cumurcuc, Rodica, Cakmak, Serkan, Peysson, Stéphane, Ellie, Emmanuel, Bernady, Patricia, Moulin, Thierry, Montiel, Paola, Revenco, Eugeniu, Decavel, Pierre, Medeiros, Elisabeth, Bouveret, Myriam, Louchart, Pierre, Vaduva, Claudia, Couvreur, Grégory, Sartori, Eric, Carpentier, Alnajar, Levasseur, Michèle, Louchart, Pierre, Faucheux, Jean-Marc, Neau, Philippe, Vandamme, Xavier, Meresse, Isabelle, Stantescu, Bataillard, Marc, Ozsancak, Canan, Beauvais, Katell, Auzou, Pascal, Amevigbe, Joséphine, Vuillemet, Francis, Dugay-Arentz, Marie-Hélène, Carelli, Gabriela, Martinez, Mikel, Maillet-Vioud, Marcel, Escaillas, Jean-Pierre, Chapuis, Stéphane, Tardy, Jean, Manchon, Eric, Varnet, Olivier, Kim, Yong-Jae, Chang, Yoonkyung, Song, Tae-Jin, Kim, Jong Sung, Han, Jung-Hoon, Noh, Kyung Chul, Lee, Eun-Jae, Kang, Dong-Wha, Kwon, Sun Uck, Kwon, Boseoung, Park, Seongho, Lee, Dongwhane, Kwon, Hyuk Sung, Jeong, Daeun, Lee, MinHwan, Kim, Joonggoo, Lee, Hanbin, Nam, Hyo Jung, Lee, Sang Hun, Kim, Bum Joon, Cha, Jae-kwan, Kim, DaeHyun, Kim, Rae Young, Sohn, Sang Wuk, Shim, Dong-Hyun, Lee, Hyungjin, Nah, Hyun-Wook, Sung, Sang Min, Lee, Kyung Bok, Yoon Lee, Jeong, Yoon, Jee Eun, Kim, Eung-Gyu, Seo, Jung Hwa, Kim, Yong-Won, Hwang, Yangha, Park, Man Seok, Kim, Joon-Tae, Choi, Kang-Ho, Nam, Hyo Suk, Heo, Ji Hoe, Kim, Young Dae, Hwang, In Gun, Park, Hyung Jong, Kim, Kyoung Sub, Baek, Jang Hyun, Song, Dong Beom, Yoo, Joon Sang, Park, Jong-Moo, Kwon, Ohyun, Lee, Woong-Woo, Lee, Jung-Ju, Kang, Kyusik, Kim, Byung Kun, Lim, Jae-Sung, Oh, Mi Sun, Yu, Kyung-Ho, Hong, Bora, Jang, Mihoon, Jang, Seyoung, Jin, Jung Eun, Kim, Jei, Jeong, Hye Seon, Hong, Keun Sik, Park, Hong Kyun, Cho, Yong Jin, Bang, Oh Young, Seo, Woo-Keun, and Chung, Jongwon

Published
2023
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47. Hypothalamus-specific NSCs derived from hPSCs ameliorate age-associated dysfunction upon transplantation into aged mouse hypothalamus

Author

Sulistio, Yanuar Alan, primary, Pieknell, Kelvin, additional, Hong, Sebin, additional, Lee, Yuna, additional, Kim, Min Soo, additional, Park, Woong-Yang, additional, Han, Kyung Yeon, additional, Hong, Taeui, additional, Park, Kyu-Sang, additional, Yoon, Seul Gi, additional, Kim, Il Yong, additional, Seong, Je Kyung, additional, Lee, Tae Yong, additional, Kim, Min Sung, additional, and Lee, Sang-Hun, additional

Published
2024
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49. Ultrathin All‐Solid‐State MoS2‐Based Electrolyte Gated Synaptic Transistor with Tunable Organic–Inorganic Hybrid Film

Author

Oh, Jungyeop, primary, Park, Seohak, additional, Lee, Sang Hun, additional, Kim, Sungkyu, additional, Lee, Hyeonji, additional, Lee, Changhyeon, additional, Hong, Woonggi, additional, Cha, Jun‐Hwe, additional, Kang, Mingu, additional, Jin, Jun Hyup, additional, Im, Sung Gap, additional, Kim, Min Ju, additional, and Choi, Sung‐Yool, additional

Published
2024
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