• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.559-564
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• 2023

It is known that change in the bone strength of cortical bone constituting the outer shell of long bones such as the tibia or radius due to aging and osteoporosis is a risk factor for fracture. In this study, the group velocity of time-reversed Lamb waves generated in tibial cortical bone in vivo was measured using a time reversal method, and the correlations of the group velocity with the cortical bone thickness (cTh) and cortical bone mineral density (cBMD) closely related to the bone strength were investigated. It was found that the group velocity of time-reversed Lamb waves measured in the right tibia of 7 subjects showed a very high correlation, r = 0.90 (p < 0.0001), with the cTh and a relatively low correlation, r = 0.69 (p < 0.0001), with the cBMD. A limitation of this in vivo study is that the group velocity of time-reversed Lamb waves was measured for a normal group consisting of only 7 healthy adults. In the future, if the clinical usefulness of the time-reversed Lamb wave is demonstrated by follow-up studies on normal and osteoporotic groups consisting of a large number of healthy adults and osteoporotic patients, respectively, it is expected to improve the reliability of quantitative ultrasound technology for osteoporosis diagnosis. In addition, it is necessary to expand the skeletal site for measuring the group velocity of time-reversed Lamb waves not only to the tibia but also to the femur or radius.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.455-460
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• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.425-434
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• 2021

Objective: To investigate the potential value of ¹⁸F-fluorodeoxyglucose (FDG) PET/CT in predicting the survival of patients with primary tracheal malignant tumors. Materials and Methods: An analysis of FDG PET/CT findings in 37 primary tracheal malignant tumor patients with a median follow-up period of 43.2 months (range, 10.8-143.2 months) was performed. Cox proportional hazards regression analyses were used to assess the associations between quantitative ¹⁸F-FDG PET/CT parameters, other clinic-pathological factors, and overall survival (OS). A risk prognosis model was established according to the independent prognostic factors identified on multivariate analysis. A survival curve determined by the Kaplan-Meier method was used to assess whether the prognosis prediction model could effectively stratify patients with different risks factors. Results: The median survival time of the 37 patients with tracheal tumors was 38.0 months, with a 95% confidence interval of 10.8 to 65.2 months. The 3-year, 5-year and 10-year survival rate were 54.1%, 43.2%, and 16.2%, respectively. The metabolic tumor volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake value, age, pathological type, extension categories, and lymph node stage were included in multivariate analyses. Multivariate analysis showed MTV (p = 0.011), TLG (p = 0.020), pathological type (p = 0.037), and extension categories (p = 0.038) were independent prognostic factors for OS. Additionally, assessment of the survival curve using the Kaplan-Meier method showed that our prognosis prediction model can effectively stratify patients with different risks factors (p < 0.001). Conclusion: This study shows that ¹⁸F-FDG PET/CT can predict the survival of patients with primary tracheal malignant tumors. Patients with an MTV > 5.19, a TLG > 16.94 on PET/CT scans, squamous cell carcinoma, and non-E1 were more likely to have a reduced OS.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1269-1280
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• 2022

Objective: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD). Materials and Methods: We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters. Results: Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023). Conclusion: MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.215-224
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• 2024

The purpose of this study was to examine research trends related to neonatal simulation practice education of domestic nursing students. It was a descriptive research study. For literature collection, a total of 17 journals were selected as a result of a search using ('Newborn Simulation') AND ('Nursing Student' OR 'Nursing College Student' OR 'Student Nurse') in 6 domestic electronic databases. The research results showed that it started with 7 journals from 2011 to 2015 and decreased slightly to 5 journals from 2016 to 2020 and 5 journals from 2021 to 2023. The research design was mostly quantitative with a total of 16 journals(94%). Among them, there were 15 intervention journals(88%), 1 descriptive research journals(6%), and 1 mixed method journals(6%). The key topics in simulation practice were high-risk newborns with 9 journals(52%), respiratory distress syndrome in neonatal intensive care units appeared with 3 journals(18%), neonatal care with 3 journals(18%), normal newborn care with 1 journal(6%), and neonatal emergency airway care with 1 journals(6%). The main outcome variables were clinical performance, accounting for 5 journals(19.2%), followed by practice satisfaction 3 journals(11.5%). clinical competency and practice satisfaction were found to have significant positive effects. In conclusion, various research methods are required, such as expansion of nursing students' neonatal simulation practice education, repeated research, and qualitative research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.407-417
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• 2024

The objective of this study is to predict and reduce potential damage caused by chlorine gas leaks, a hazardous material, when vehicles transporting it overturn due to accidents or other incidents. The goal is to forecast the anticipated damages caused by chlorine toxicity levels (ppm) and to design effective response strategies for mitigating them. To predict potential damages, we conducted quantitative assessments using the ALOHA program to calculate the toxic effects (ppm) and damage distances resulting from chlorine leaks, taking into account potential negligence of drivers during transportation. The extent of damage from toxic gas leaks is influenced by various factors, including the amount of the leaked hazardous material and the meteorological conditions at the time of the leak. Therefore, a comprehensive analysis of damage distances was conducted by examining various scenarios that involved variations in the amount of leakage and weather conditions. Under intermediate conditions (leakage quantity: 5 tons, wind speed: 3 m/s, atmospheric stability: D), the estimated distance for exceeding the AEGL-2 level of 2 ppm was calculated to be 9 km. This concentration poses a high risk of respiratory disturbance and potential human casualties, comparable to the toxicity of hydrogen chloride. In particular, leaks in urban areas can lead to significant loss of life. In the event of a leakage incident, we proposed a plan to minimize damage by implementing appropriate response strategies based on the location and amount of the leak when an accident occurs.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.529-540
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• 2024

In industrial sites, experiential virtual training contents are partially used to replace high-risk and high-cost training, and virtual training contents development is also becoming active along with the increasing demand for non-face-to-face industries. Existing studies mainly focused on quantitative research through surveys, and only measured the change in users' learning commitment. This study attempted to investigate the effect of the combination of theoretical education and virtual training on the improvement of actual job performance in a dual vocational training environment by conducting an experimental study. This study studied whether the combination of theoretical education and virtual training can improve the performance of vocational training in dual vocational training (comparative work and learning) in which companies and schools participate. The results of pre- and post-evaluation of vocational training using traditional vocational training and virtual training contents were compared with 24 vocational training trainees. As a result of the study, it was demonstrated that the outcome of virtual training education was higher than that of traditional vocational training, and the combination of virtual reality-based education was more effective in theoretical education. This study suggests that the virtual training content presents a new paradigm for industrial safety education, and through the interview results of trainees, it was confirmed that virtual training can lead to a change in attitude toward safety beyond just knowledge transfer. This contributes to the prevention of safety accidents in industrial sites and provides important implications for improving the quality of vocational training.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• Journal of Preventive Medicine and Public Health
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• v.32 no.3
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• pp.361-373
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• 1999

Objectives: Receive Operating Characteristic(ROC) curve with the area under the ROC curve(AUC) is one of the most popular indicator to evaluate the criterion validity of the measurement tool. This study was conducted to develop a standardized questionnaire to discriminate workers at high-risk of work-related musculoskeletal disorders using ROC analysis. Methods: The diagnostic results determined by rehabilitation medicine specialists in 370 persons(89 shipyard CAD workers, 113 telephone directory assistant operators, 79 women with occupation, and 89 housewives) were compared with participant's own replies to 'the questionnair on the worker's subjective physical symptoms'(Kwon, 1996). The AUC's from four models with different methods in item selection and weighting were compared with each other. These 4 models were applied to 225 persons, working in an assembly line of motor vehicle, for the purpose of AUC reliability test. Results: In a weighted model with 11 items, the AUC was 0.8155 in the primary study population, and 0.8026 in the secondary study population(p=0.3780). It was superior in the aspects of discriminability, reliability and convenience. A new questionnaire of musculoskeletal disorder could be constructed by this model. Conclusion: A more valid questionnaire with a small number of items and the quantitative weight scores useful for the relative comparisons are the main results of this study. While the absolute reference value applicable to the wide range of populations was not estimated, the basic intent of this study, developing a surveillance fool through quantitative validation of the measures, would serve for the systematic disease prevention activities.