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1,005 results on '"Park, Jong Hoon"'

1. How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological Signals

Author

Park, Jong Hoon, Chen, Lawrence, Higgins, Ian, Zheng, Zhaobo, Mehrotra, Shashank, Salubre, Kevin, Mousaei, Mohammadreza, Willits, Steven, Levedahl, Blain, Buker, Timothy, Xing, Eliot, Misu, Teruhisa, Scherer, Sebastian, and Oh, Jean

Subjects
Computer Science - Human-Computer Interaction
Abstract

Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popular in development, research, and operation. When compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique challenges as they combine many maneuvers from both types of aircraft. Pilot workload is a critical factor for safe and efficient operation of VTOLs. In this work, we conduct a user study to collect multimodal data from 28 pilots while they perform a variety of VTOL flight tasks. We analyze and interpolate behavioral patterns related to their performance and perceived workload. Finally, we build machine learning models to estimate their workload from the collected data. Our results are promising, suggesting that quantitative and accurate VTOL pilot workload monitoring is viable. Such assistive tools would help the research field understand VTOL operations and serve as a stepping stone for the industry to ensure VTOL safe operations and further remote operations., Comment: 8 pages, 7 figures

Published
2024

2. Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction

Author

Park, Jong Hoon, Dalwankar, Gauri Pramod, Bartsch, Alison, George, Abraham, and Farimani, Amir Barati

Subjects
Computer Science - Machine Learning
Abstract

Accurately determining fluid viscosity is crucial for various industrial and scientific applications. Traditional methods of viscosity measurement, though reliable, often require manual intervention and cannot easily adapt to real-time monitoring. With advancements in machine learning and computer vision, this work explores the feasibility of predicting fluid viscosity by analyzing fluid oscillations captured in video data. The pipeline employs a 3D convolutional autoencoder pretrained in a self-supervised manner to extract and learn features from semantic segmentation masks of oscillating fluids. Then, the latent representations of the input data, produced from the pretrained autoencoder, is processed with a distinct inference head to infer either the fluid category (classification) or the fluid viscosity (regression) in a time-resolved manner. When the latent representations generated by the pretrained autoencoder are used for classification, the system achieves a 97.1% accuracy across a total of 4,140 test datapoints. Similarly, for regression tasks, employing an additional fully-connected network as a regression head allows the pipeline to achieve a mean absolute error of 0.258 over 4,416 test datapoints. This study represents an innovative contribution to both fluid characterization and the evolving landscape of Artificial Intelligence, demonstrating the potential of deep learning in achieving near real-time viscosity estimation and addressing practical challenges in fluid dynamics through the analysis of video data capturing oscillating fluid dynamics., Comment: 12 pages, 7 figures

Published
2023

4. AI Vision Use Case for Digital Twin WIP Tracking in Heavy Industry

Author

Choi, Jang Won, Cho, SungJae, Li, Binglu, Park, Jong-Hoon, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Alfnes, Erlend, editor, Romsdal, Anita, editor, Strandhagen, Jan Ola, editor, von Cieminski, Gregor, editor, and Romero, David, editor

Published
2023
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11. Integrative transcriptomic and genomic analyses unveil the IFI16 variants and expression as MASLD progression markers

Author

Kim, Doyoon, primary, Shah, Masaud, additional, Kim, Jang Hyun, additional, Kim, JungMo, additional, Baek, Yang-Hyun, additional, Jeong, Jin-Sook, additional, Han, Sang-Young, additional, Lee, Yong Sun, additional, Park, Gaeul, additional, Cho, Jin-Han, additional, Roh, Young-Hoon, additional, Lee, Sung-Wook, additional, Choi, Gi-Bok, additional, Park, Jong Hoon, additional, Yoo, Kyung Hyun, additional, Seong, Rho Hyun, additional, Lee, Yeon-Su, additional, and Woo, Hyun Goo, additional

Published
2024
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14. Hypomethylation of ATP1A1 Is Associated with Poor Prognosis and Cancer Progression in Triple-Negative Breast Cancer.

Author

Kim, Yesol, Ko, Je Yeong, Kong, Hyun Kyung, Lee, Minyoung, Chung, Woosung, Lim, Sera, Son, Dasom, Oh, Sumin, Park, Jee Won, Kim, Do Yeon, Lee, Minju, Han, Wonshik, Park, Woong-Yang, Yoo, Kyung Hyun, and Park, Jong Hoon

Subjects
BREAST cancer prognosis, CANCER invasiveness, BREAST tumors, TUMOR markers, DNA methylation, ADENOSINE triphosphatase, CELL lines, GENE expression profiling, DISEASE progression
Abstract

Simple Summary: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, but effective biomarkers has yet been developed. Here, we identified ATP1A1 as prognostic marker and therapeutic target via integrated analysis of DNA methylome and transcriptome of TNBC. These data will provide new insight into not only developing specific biomarker for TNBC but also understanding TNBC pathogenesis. Dysregulated DNA methylation in cancer is critical in the transcription machinery associated with cancer progression. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, but no treatment targeting TNBC biomarkers has yet been developed. To identify specific DNA methylation patterns in TNBC, methyl-binding domain protein 2 (MBD) sequencing data were compared in TNBC and the three other major breast cancer subtypes. Integrated analysis of DNA methylation and gene expression identified a gene set showing a correlation between DNA methylation and gene expression. ATPase Na+/K+-transporting subunit alpha 1 (ATP1A1) was found to be specifically hypomethylated in the coding sequence (CDS) region and to show increased expression in TNBC. The Cancer Genome Atlas (TCGA) database also showed that hypomethylation and high expression of ATP1A1 were strongly associated with poor survival in patients with TNBC. Furthermore, ATP1A1 knockdown significantly reduced the viability and tumor-sphere formation of TNBC cells. These results suggest that the hypomethylation and overexpression of ATP1A1 could be a prognostic marker in TNBC and that the manipulation of ATP1A1 expression could be a therapeutic target in this disease. [ABSTRACT FROM AUTHOR]

Published
2024
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17. A Global Definition of Patient Blood Management

Author

Shander, Aryeh, Hardy, Jean-Francois, Ozawa, Sherri, Farmer, Shannon L., Hofmann, Axel, Frank, Steven M., Kor, Daryl J., Faraoni, David, Freedman, John, Gombotz, Hans, Hamdorf, Jeffrey, Isbister, James, Javidroozi, Mazyar, Ji, Hongwen, Kim, Tae-Yop, Kim, Young-Woo, Krishnamoorthy, Ananthi, Leahy, Michael F., Park, Jong-Hoon, Raphael, Jacob, Reagor, Jim, Samama, Charles-Marc, Sathar, Jameela, Thomson, Jackie, Tibi, Pierre, Trentino, Kevin, and Lasocki, Sigismond

Published
2022
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35. Supplementary Figure 2 from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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36. Supplementary Table 2 from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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37. Supplementary Table 4 from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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38. Supplementary Table 3 from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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39. Supplementary Figure 1 from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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40. Data from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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41. Supplementary Table 1 from Integrated Analysis of Transcriptomes of Cancer Cell Lines and Patient Samples Reveals STK11/LKB1–Driven Regulation of cAMP Phosphodiesterase-4D

Author

He, Ningning, primary, Kim, Nayoung, primary, Song, Mee, primary, Park, Choa, primary, Kim, Somin, primary, Park, Eun Young, primary, Yim, Hwa Young, primary, Kim, Kyunga, primary, Park, Jong Hoon, primary, Kim, Keun Il, primary, Zhang, Fan, primary, Mills, Gordon B., primary, and Yoon, Sukjoon, primary

Published
2023
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