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19 results on '"Choi, Wooram"'

9. The Preventive Effect of Specific Collagen Peptides against Dexamethasone-Induced Muscle Atrophy in Mice

Author

Oh, Jieun, primary, Park, Sang Hee, additional, Kim, Dong Seon, additional, Choi, Wooram, additional, Jang, Jiwon, additional, Rahmawati, Laily, additional, Jang, Won Young, additional, Lim, Hyun Kyung, additional, Hwang, Ji Yeon, additional, Gu, Ga Rin, additional, Geum, Jeong-Ho, additional, Choi, Su-Young, additional, Kim, Ji Hye, additional, and Cho, Jae Youl, additional

Published
2023
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11. Guettarda crispiflora Vahl Methanol Extract Ameliorates Acute Lung Injury and Gastritis by Suppressing Src Phosphorylation

Author

Lee, Dahae, primary, Kim, Ji Won, additional, Lee, Chae Young, additional, Oh, Jieun, additional, Hwang, So Hyun, additional, Jo, Minkyeong, additional, Kim, Seung A, additional, Choi, Wooram, additional, Noh, Jin Kyoung, additional, Yi, Dong-Keun, additional, Song, Minkyung, additional, Kim, Han Gyung, additional, and Cho, Jae Youl, additional

Published
2022
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13. Deep learning implemented structural defect detection on digital images

Author

Polyzois, Dimos (Civil Engineering) Wang, Yang (Computer Science) Lattanzi, David (George Mason University), Cha, Young-Jin (Civil Engineering), Choi, Wooram, Polyzois, Dimos (Civil Engineering) Wang, Yang (Computer Science) Lattanzi, David (George Mason University), Cha, Young-Jin (Civil Engineering), and Choi, Wooram

Abstract

Periodical inspection is the dominant form of structural health monitoring (SHM). However, civil engineering societies in North America have expressed the common consent that the current inspection practice is not sufficient to ensure infrastructure safety. Moreover, the increasing number of aged infrastructures will require an advanced form of inspection systems. The processes of vision-based methods for identifying damage using image processing algorithms (IPAs) are similar to human inspections because both use visual information. The outcomes of vision-based methods are much more intuitive than systems with traditional contact sensors. Accordingly, researchers have proposed a variety of different methods. For example, early research adopted IPAs directly into damage detection problems. The results from IPAs are intuitive but require manual decision-making processes. Further attempts have been made to establish automated decision-making systems using machine learning algorithms (MLAs). However, real-life applications are rare. The unavailability is mainly rooted in the fact that IPAs were developed and tested in controlled circumstances, while real-world situations often cannot be controlled. Mobile units with cameras have attracted great attention in the SHM discipline. This type of inspection can improve accessibility to infrastructures but still lacks automated damage detection. Even if IPAs and MLAs are integrated, the combined system (mobile units, IPAs, and MLAs) will likely be invalid in practice because this system inherits the limitations of IPAs. To overcome these challenges, IPAs should be replaced by advanced computer vision techniques. In this thesis, deep learning (DL) is considered the key for surpassing the current state of vision-based approaches. Deep learning models are capable of learning features from raw data. Instead of manually developing IPAs, feeding raw data that were collected in uncontrolled environments and leading a machine to learn

Published
2020

15. STR-906: COMPUTER-IMAGE-BASED LOOSENED BOLT DETECTION USING SUPPORT VECTOR MACHINES

Author

Cha, Young-Jin, You, Kisung, and Choi, Wooram

Subjects
Structural Engineering
Abstract

Despite many contact-sensor-based methods have been proposed to monitor and detect structural defects, there are still difficulties compensating for environmental effects and malfunctions of attached sensors, which are main reasons for transmitting false signals. Moreover, regardless of releasing correct or incorrect signals, it eventually leads to human-conducted on-site inspections. In light of these shortcomings, vision-based inspections are considered as potential approach to overcome the explained issues. A number of vision-based methods for detecting damages from images have been developed. However, there are only a few vision-based methods for detecting loosened bolts. Thus, a computer-vision method for detecting loosened bolts is proposed. This study includes two algorithms. The first one is a preprocessing to crop bolt images from bolted-joint images. The second algorithm is a feature extraction by integrating previously proposed algorithms in computer-vision. To accomplish an automated inspection, linear support vector machine is trained and used as a classifier. The robustness of the proposed is verified by the experimental validation; 22 bolt images are used to build a classifier, and 40 bolt images are tested.

Published
2016

18. Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types.

Author

Cha, Young‐Jin, Choi, Wooram, Suh, Gahyun, Mahmoudkhani, Sadegh, and Büyüköztürk, Oral

Subjects
*COMPUTER vision, *STRAINS & stresses (Mechanics), *CRACK propagation (Fracture mechanics), *INSPECTION & review, *STEEL corrosion
Abstract

Abstract: Computer vision‐based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real‐time simultaneous detection of multiple types of damages, a Faster Region‐based Convolutional Neural Network (Faster R‐CNN)‐based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R‐CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R‐CNN is evaluated and demonstrated using 11 new 6,000 × 4,000‐pixel images taken of different structures. Its performance is also compared to that of the traditional CNN‐based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real‐time damage detection on video using the trained networks is developed. [ABSTRACT FROM AUTHOR]

Published
2018
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19. Syringaresinol derived from Panax ginsengberry attenuates oxidative stress-induced skin aging via autophagy

Author

Choi, Wooram, Kim, Hyun Soo, Park, Sang Hee, Kim, Donghyun, Hong, Yong Deog, Kim, Ji Hye, and Cho, Jae Youl

Abstract

In aged skin, reactive oxygen species (ROS) induces degradation of the extracellular matrix (ECM), leading to visible aging signs. Collagens in the ECM are cleaved by matrix metalloproteinases (MMPs). Syringaresinol (SYR), isolated from Panax ginsengberry, has various physiological activities, including anti-inflammatory action. However, the anti-aging effects of SYR via antioxidant and autophagy regulation have not been elucidated.

Published
2021
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