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10,536 results on '"Kim, Dong Hyun"'

1. Persistent quantum advantage with definite photon-number states in lossy multiple-phase estimation

Author

Namkung, Min, Kim, Dong-Hyun, Hong, Seongjin, Lee, Changhyoup, and Lim, Hyang-Tag

Subjects
Quantum Physics
Abstract

Multiple-phase estimation exploiting quantum states has broad applications in novel sensing and imaging technologies. However, the unavoidable presence of lossy environments in practical settings often diminishes the precision of phase estimations. To address this challenge, we propose an optimal multiple-phase estimation scheme that is inherently robust against photon loss, ensuring a persistent quantum advantage across all levels of photon loss. The scheme employs a multi-mode definite photon-number (DPN) state with weights optimized for given levels of photon loss. We theoretically demonstrate that the DPN state can sustain quantum enhancement in estimation precision under all levels of photon loss, compared to the classical benchmark that employs a coherent state input. The proposed scheme using DPN states generalizes earlier studies employing NOON states, which are only optimal when photon loss is small. We believe that our study, demonstrating persistent robustness to photon loss, paves the way for significant advancements in quantum-enhanced sensing technologies, enabling practical applications and quantum advantages in real-world scenarios., Comment: 11 pages, 5 figures, supplementary note

Published
2024

2. Optimal multiple-phase estimation with multi-mode NOON states against photon loss

Author

Namkung, Min, Kim, Dong-Hyun, Hong, Seongjin, Kim, Yong-Su, Lee, Changhyoup, and Lim, Hyang-Tag

Subjects
Quantum Physics, Physics - Optics
Abstract

Multi-mode NOON states can quantum-enhance multiple-phase estimation in the absence of photon loss. However, a multi-mode NOON state is known to be vulnerable to photon loss, and its quantum-enhancement can be dissipated by lossy environment. In this work, we demonstrate that a quantum advantage in estimate precision can still be achieved in the presence of photon loss. This is accomplished by optimizing the weights of the multi-mode NOON states according to photon loss rates in the multiple modes, including the reference mode which defines the other phases. For practical relevance, we also show that photon-number counting via a multi-mode beam-splitter achieves the useful, albeit sub-optimal, quantum advantage. We expect this work to provide valuable guidance for developing quantum-enhanced multiple-phase estimation techniques in lossy environments., Comment: 10 pages, 7 figures

Published
2024
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3. Synthesis of adhesion promoter for lead frame bonding and application of epoxy composite materials

Author

Kim, Eun ji, Kim, Jung Soo, Kim, Hae Chan, Kwon, Miyeon, Chang, Young-Wook, and Kim, Dong Hyun

Subjects
Composite materials -- Production processes, Chemical synthesis -- Methods, Engineering and manufacturing industries, Science and technology
Abstract

Polymer adhesion promoters were formulated to improve the adhesion strength of lead frame/epoxy composites. The poly(itaconic acid-co-acrylamide) CIAcAAM) was synthesized via free radical polymerization to improve the interfacial adhesion between Ni and Cu lead frames and epoxy composites. IAcAAM was synthesized by setting itaconic acid (IA) and acrylamide (AAM) at molar ratios of 7:3, 5:5. and 3:7. The interfacial adhesion mechanism was investigated by introducing IAcAAM with three different comonomer molar ratios. The adhesion strength between EMC and Cu and Ni substrates to which each IAcAAM was applied was measured using a universal testing machine, and the effect of IAcAAM with various LA:AAM molar ratios on the surface energy of EMC was closely studied. As a result, the adhesion properties of Ni lead frame/epoxy composite and Cu lead frame/epoxy composite were improved as small amounts of all IAcAAM samples were added to the epoxy mixture. We confirmed that IAcAAM with an appropriate molar ratio can be used in the EMC process and has the potential to increase the adhesion of epoxy composites and metal lead frames. Highlights * A polymeric adhesive promoter was synthesized to enhance the adhesion strength of lead frame/epoxy composites. * Poly(itaconic acid-co-acrvlamide) (IAcAAM) was copolymeriz,ed from itaconic acid and acrylamide for this purpose. * The adhesion strength of lead frame/epoxy composites was compared for different IAcAAM monomer contents and with a commercial adhesive promoter. * IAcAAM showed superior adhesion strength in the Ni lead frame/epoxy composite material. * The highest adhesion strength was achieved at a monomer composition of 5:5. KEYWORDS adhesion promoter, adhesive strength, epoxy composite, epoxy molding compound, lead frame, 1 | INTRODUCTION An epoxy molding compound (EMC) is a composite material composed ol' ingredients such as silica, epoxy resin, phenolic resin, carbon black, and flame retardants. This compound is [...]

Published
2024
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9. Surface control of Ni-Al2O3 dry reforming of methane catalyst by composition segregation

Author

Kim, Min-Jae, Kim, Jeongmin, Kim, Yong Jun, Youn, Jae-Rang, Kim, Dong Hyun, Shapiro, David, Guo, Jinghua, and Lee, Kyubock

Subjects
Chemical Engineering, Engineering, Inorganic Chemistry, Chemical engineering
Abstract

Catalyst surface control is of great importance considering that a catalytic reaction initially starts with surface atoms’ interaction with reactant molecules. In the present study, we synthesized the Ni-Al2O3 dry reforming of methane (DRM) catalyst via the spray-pyrolysis-assisted evaporation-induced self-assembly (EISA) method in order to systematically investigate catalyst surface change as controlled by composition segregation. The present results showed that segregation in the Ni-Al2O3 catalyst had successfully occurred by the post-annealing process and that the segregated Ni nanoparticles were located on the catalyst surface with simultaneous reduction to metal. The size of the surface Ni nanoparticles was highly dependent on the post-annealing temperature, whereas the electronic properties did not consistently align with the trend of particle-size growth, indicating that the particle-growth mechanism underwent alterations from segregation to sintering as the temperature was increased. In other words, when the reduction temperature for metal segregation is excessively high, particle-growth on the external catalyst surface, as induced by post-annealing, is primarily attributable to particle sintering rather than to metal segregation. This phenomenon directly results in decreased Ni surface density. The catalytic DRM reaction revealed that due to sintering, the catalytic performance did not align with the trend of Ni particle-size growth, and the conversion of CH4/CO2 was closely associated with Ni surface density. This information will offer valuable insight to future research focused on development of Ni-based catalysts for DRM reactions.

Published
2024

13. Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning

Author

Kim, Jun-Ho, Noh, Young, Lee, Haejoon, Lee, Seul, Kim, Woo-Ram, Kang, Koung Mi, Kim, Eung Yeop, Al-masni, Mohammed A., and Kim, Dong-Hyun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of CMBs is a time-consuming and error-prone process because of their sparse and tiny structural properties. The detection of CMBs is commonly affected by the presence of many CMB mimics that cause a high false-positive rate (FPR), such as calcification and pial vessels. This paper proposes a novel 3D deep learning framework that does not only detect CMBs but also inform their anatomical location in the brain (i.e., lobar, deep, and infratentorial regions). For the CMB detection task, we propose a single end-to-end model by leveraging the U-Net as a backbone with Region Proposal Network (RPN). To significantly reduce the FPs within the same single model, we develop a new scheme, containing Feature Fusion Module (FFM) that detects small candidates utilizing contextual information and Hard Sample Prototype Learning (HSPL) that mines CMB mimics and generates additional loss term called concentration loss using Convolutional Prototype Learning (CPL). The anatomical localization task does not only tell to which region the CMBs belong but also eliminate some FPs from the detection task by utilizing anatomical information. The results show that the proposed RPN that utilizes the FFM and HSPL outperforms the vanilla RPN and achieves a sensitivity of 94.66% vs. 93.33% and an average number of false positives per subject (FPavg) of 0.86 vs. 14.73. Also, the anatomical localization task further improves the detection performance by reducing the FPavg to 0.56 while maintaining the sensitivity of 94.66%., Comment: 16 pages, 10 figures,3 tables

Published
2023

24. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Author

Chen, Zhishan, Guo, Xingyi, Tao, Ran, Huyghe, Jeroen R., Law, Philip J., Fernandez-Rozadilla, Ceres, Ping, Jie, Jia, Guochong, Long, Jirong, Li, Chao, Shen, Quanhu, Xie, Yuhan, Timofeeva, Maria N., Thomas, Minta, Schmit, Stephanie L., Díez-Obrero, Virginia, Devall, Matthew, Moratalla-Navarro, Ferran, Fernandez-Tajes, Juan, Palles, Claire, Sherwood, Kitty, Briggs, Sarah E. W., Svinti, Victoria, Donnelly, Kevin, Farrington, Susan M., Blackmur, James, Vaughan-Shaw, Peter G., Shu, Xiao-Ou, Lu, Yingchang, Broderick, Peter, Studd, James, Harrison, Tabitha A., Conti, David V., Schumacher, Fredrick R., Melas, Marilena, Rennert, Gad, Obón-Santacana, Mireia, Martín-Sánchez, Vicente, Oh, Jae Hwan, Kim, Jeongseon, Jee, Sun Ha, Jung, Keum Ji, Kweon, Sun-Seog, Shin, Min-Ho, Shin, Aesun, Ahn, Yoon-Ok, Kim, Dong-Hyun, Oze, Isao, Wen, Wanqing, Matsuo, Keitaro, Matsuda, Koichi, Tanikawa, Chizu, Ren, Zefang, Gao, Yu-Tang, Jia, Wei-Hua, Hopper, John L., Jenkins, Mark A., Win, Aung Ko, Pai, Rish K., Figueiredo, Jane C., Haile, Robert W., Gallinger, Steven, Woods, Michael O., Newcomb, Polly A., Duggan, David, Cheadle, Jeremy P., Kaplan, Richard, Kerr, Rachel, Kerr, David, Kirac, Iva, Böhm, Jan, Mecklin, Jukka-Pekka, Jousilahti, Pekka, Knekt, Paul, Aaltonen, Lauri A., Rissanen, Harri, Pukkala, Eero, Eriksson, Johan G., Cajuso, Tatiana, Hänninen, Ulrika, Kondelin, Johanna, Palin, Kimmo, Tanskanen, Tomas, Renkonen-Sinisalo, Laura, Männistö, Satu, Albanes, Demetrius, Weinstein, Stephanie J., Ruiz-Narvaez, Edward, Palmer, Julie R., Buchanan, Daniel D., Platz, Elizabeth A., Visvanathan, Kala, Ulrich, Cornelia M., Siegel, Erin, Brezina, Stefanie, Gsur, Andrea, Campbell, Peter T., Chang-Claude, Jenny, Hoffmeister, Michael, Brenner, Hermann, Slattery, Martha L., Potter, John D., Tsilidis, Kostas K., Schulze, Matthias B., Gunter, Marc J., Murphy, Neil, Castells, Antoni, Castellví-Bel, Sergi, Moreira, Leticia, Arndt, Volker, Shcherbina, Anna, Bishop, D. Timothy, Giles, Graham G., Southey, Melissa C., Idos, Gregory E., McDonnell, Kevin J., Abu-Ful, Zomoroda, Greenson, Joel K., Shulman, Katerina, Lejbkowicz, Flavio, Offit, Kenneth, Su, Yu-Ru, Steinfelder, Robert, Keku, Temitope O., van Guelpen, Bethany, Hudson, Thomas J., Hampel, Heather, Pearlman, Rachel, Berndt, Sonja I., Hayes, Richard B., Martinez, Marie Elena, Thomas, Sushma S., Pharoah, Paul D. P., Larsson, Susanna C., Yen, Yun, Lenz, Heinz-Josef, White, Emily, Li, Li, Doheny, Kimberly F., Pugh, Elizabeth, Shelford, Tameka, Chan, Andrew T., Cruz-Correa, Marcia, Lindblom, Annika, Hunter, David J., Joshi, Amit D., Schafmayer, Clemens, Scacheri, Peter C., Kundaje, Anshul, Schoen, Robert E., Hampe, Jochen, Stadler, Zsofia K., Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Edlund, Christopher K., Gauderman, W. James, Shibata, David, Toland, Amanda, Markowitz, Sanford, Kim, Andre, Chanock, Stephen J., van Duijnhoven, Franzel, Feskens, Edith J. M., Sakoda, Lori C., Gago-Dominguez, Manuela, Wolk, Alicja, Pardini, Barbara, FitzGerald, Liesel M., Lee, Soo Chin, Ogino, Shuji, Bien, Stephanie A., Kooperberg, Charles, Li, Christopher I., Lin, Yi, Prentice, Ross, Qu, Conghui, Bézieau, Stéphane, Yamaji, Taiki, Sawada, Norie, Iwasaki, Motoki, Le Marchand, Loic, Wu, Anna H., Qu, Chenxu, McNeil, Caroline E., Coetzee, Gerhard, Hayward, Caroline, Deary, Ian J., Harris, Sarah E., Theodoratou, Evropi, Reid, Stuart, Walker, Marion, Ooi, Li Yin, Lau, Ken S., Zhao, Hongyu, Hsu, Li, Cai, Qiuyin, Dunlop, Malcolm G., Gruber, Stephen B., Houlston, Richard S., Moreno, Victor, Casey, Graham, Peters, Ulrike, Tomlinson, Ian, and Zheng, Wei

Published
2024
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