• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6A
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• pp.535-542
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• 2010

Precise estimation of a structure's dynamic characteristics is indispensable for ensuring stable dynamic responses during lifetime especially for the structures which can experience resonance such as railway bridges. In this paper, the results of forced vibration tests of different excitation methods (vibration exciter and impact hammer) are compared to examine the differences and the cause of differences of extracted natural frequencies. Consequently a natural frequency modification method is suggested to eliminate effects of non-structural disturbance factors. Also, sequential forced vibration tests are performed before and after track construction according to the construction stage of a railway bridge, and the variation of natural frequencies are examined. Effect of added mass of vibration exciter and variation of support condition due to the level of excitation force are concluded as the major cause of natural frequency differences. Thus eliminating these effects can enhance the reliability of the extracted natural frequencies. Construction of track affects not only the mass of structure but also the stiffness of the structure. Also, the amount of increase in stiffness varies according to the level of structural deflection. Therefore, reasonable estimation of the level of structural response during operation is important for precise natural frequency calculation at design phase.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

• Smart Media Journal
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• v.13 no.4
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• pp.114-123
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• 2024

This paper proposes AR stone tower content, an experiential content based on wearable augmented reality (AR). Although wearable augmented reality is gaining attention, the acceptance of the technology is still focused on specialized applications such as industrial sites. On the other hand, the proposed AR stone tower content is based on the material of 'stone tower' so that general users can relate to it and easily participate in it, and it is organized to utilize space in a moving environment and find and stack stones based on natural hand gestures. The proposed AR stone tower content was implemented in the HoloLens 2 environment and evaluated by general users through a pilot exhibition in a small art museum. The evaluation results showed that the overall satisfaction with the content averaged 3.85, and the content appropriateness for the stone tower material was very high at 4.15. In particular, users were highly satisfied with content comprehension and sound, but somewhat less satisfied with object recognition, body adaptation, and object control. The above user evaluations confirm the resonance and positive response to the material, but also highlight the difficulties of the average user in experiencing and interacting with the wearable AR environment.

• Journal of the Korean Society of Radiology
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• v.82 no.3
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• pp.654-669
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• 2021

Purpose To evaluate the accuracy of MRI in predicting the pathological complete response (pCR) and the residual tumor size of breast cancer after neoadjucant chemotherapy (NAC), and to determine the factors affecting the accuarcy. Materials and Methods Eighty-eight breast cancer patients who underwent surgery after NAC at our center between 2010 and 2017 were included in this study. pCR was defined as the absence of invasive cancer on pathological evaluation. The maximum diameter of the residual tumor on post-NAC MRI was compared with the tumor size of the surgical specimen measured pathologically. Statistical analysis was performed to elucidate the factors affecting pCR and the residual tumor size-discrepancy between the MRI and the pathological measurements. Results The pCR rate was 10%. The diagnostic accuracy of MRI and the area under the curve for predicting pCR were 90.91% and 0.8017, respectively. The residual tumor sizes obtained using MRI and pathological measurements showed a strong correlation (r = 0.9, p < 0.001), especially in patients with a single mass lesion (p = 0.047). The size discrepancy between MRI and the pathological measurements was significantly greater in patients with the luminal type (p = 0.023) and multifocal tumors/non-mass enhancement on pre-NAC MRI (p = 0.047). Conclusion MRI is an accurate tool for evaluating pCR and residual tumor size in breast cancer patients who receive NAC. Tumor subtype and initial MRI features affect the accuracy of MRI.

• Journal of Life Science
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• v.30 no.4
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• pp.359-369
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• 2020

Although many studies on immune modulatory materials have used RAW 264.7 cells, few have used T cell-derived TK-1 cell lines. Moreover, although some studies have investigated the efficacy of plant-derived β-sitosterol, few have examined the immunomodulatory activity of its analogue, daucosterol. In this study, β-sitosterol and daucosterol were isolated from D. batatas and identified by nuclear magnetic resonance spectroscopy. To evaluate the immune-enhancing or inhibitory effects of the isolated phytosterols, the expression levels of the inflammatory response genes COX-2, TNF-α, IL-6, and iNOS were analyzed by RT-PCR. The relative expression levels of TNF-α and iNOS in RAW 264.7 cells were increased more than threefold with β-sitosterol treatment comparing to those of untreated control. In the case of TK-1 cells, the expression level of TNF-α was decreased and the expression level of iNOS was increased in a β-sitosterol concentration-dependent manner. The expression levels of COX-2, TNF-α, and IL-6 increased by approximately 0.7-1.2 times in RAW 264.7 cells treated with daucosterol compared to those of untreated control, but iNOS expression decreased by 0.8-0.18 times. In the case of daucosterol-treated TK-1 cells, the expression levels of TNF-α, IL-6, and iNOS were markedly reduced from those of TK-1 cells treated only with lipopolysaccaride. As a conclusion, β-sitosterol treatment increased TNF-α and iNOS expression levels in RAW 264.7 cells, thus exerting an immune- boosting effect. However, in TK-1 cells, iNOS expression increased while TNF-α expression decreased, indicating an immunosuppressive activity of β-sitosterol. Daucosterol appears to exert an immunosuppressive effect in both macrophages and T cell lines by inhibiting iNOS expression in RAW 264.7 cells and greatly inhibiting the expression of TNF-α, IL-6, and iNOS in TK-1 cells.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.178-186
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• 2009

The quality control is needed to ensure the accuracy of medical information and achieved by evaluating the performance of and maintaining the system and practicing various measurements and evaluations. The Korean Institute for Accreditation of Medical Image, therefore, have held educational program for quality control of special medical equipments. The major of programs participants, however, are radiology specialists with only small number of radiological technologists from some hospitals, furthermore, the follow-up education and the share of information between participants and non-participants are insufficient in general, thus, the knowledge level of radiological technologists, regardless of their participation, is relatively low. This study carried out the questionnaire research for the 500 radiological technologists registered in Korean Society of MRI Technology, on the basis of 2008, and performed analysis for five months from May to Oct., 2008. The questionnaires were delivered by post to each radiological technologists and the response rate was 36%(n=180). The results of this revealed that the 86.7% of respondents felt the necessity of inspection on quality management, while only the 27.8% completed the educational program for manager of special medical equipment. and only the half(53.9%) had the knowledge about inspection on quality management. The completion of educational program had no correlations with sex, age, size of occupying hospital, the number of radiological technologists in occupying site and MRI laboratory, career year of general radiologist and in MRI laboratory, and the presence of biomedical engineering department in occupying hospital. The 78.0% of participants at the educational program for quality management held by the Korean Institute for Accreditation of Medical Image had the knowledge about inspection on quality management(p<.05) whereas the 43.9% of the hospitals held such program and the 54.4% of radiological technologists from those hospitals had related knowledge, which indicated that such programs held by hospitals had not effects on the knowledge level of radiological technologists. This indicates also that the contents, methods, and other conditional factors of educational programs are important for the outcome of them.