• Journal of Korean Foot and Ankle Society
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• v.18 no.3
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• pp.108-114
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• 2014

Purpose: Ligament reattachment technique using a suture anchor appears to show satisfactory functional outcomes and mechanical stability compared with conventional bone tunnel technique. This study was prospectively conducted in order to evaluate functional outcomes of modified Brostrom procedures using the suture bridge technique for chronic ankle instability in athletes. Materials and Methods: Twenty eight athletes under 30 years of age were followed for more than two years after undergoing the modified Brostrom procedure using the suture bridge technique. Functional evaluation consisted of the foot and ankle outcome score (FAOS), foot and ankle ability measure (FAAM) score. Range of motion and time to return to exercise were evaluated using a periodic questionnaire. Talar tilt angle and anterior talar translation were measured through stress radiographs for evaluation of mechanical stability. Results: FAOS improved significantly from preoperative mean 59.4 points to 91.4 points (p<0.001). Daily living and sport activity scores of FAAM improved significantly from preoperative mean 50.5, 32.5 points to 94.8, 87.3 points, respectively (p<0.001). Talar tilt angle and anterior talar translation improved significantly from preoperative mean $16.8^{\circ}$, 13.5 mm to $4.2^{\circ}$, 4.1 mm at final follow-up (p<0.001). Times to return to exercise were as follows: mean 10.2 weeks in jogging, 15.4 weeks in spurt running, 13.1 weeks in jumping, 11.5 weeks in walking on uneven ground, 9.1 weeks in standing on one leg, 7.2 weeks in tip-toeing gait, 8.4 weeks in squatting, and 10.6 weeks in descending stairs. Conclusion: Modified Brostrom procedure using the suture bridge technique showed satisfactory functional outcomes for chronic ankle instability in athletes. Optimal indication and cost-effectiveness of the suture bridge technique will be studied in the future.

• Environmental and Resource Economics Review
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• v.28 no.4
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• pp.527-556
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• 2019

In Korea, nearly 100 billion won is spent annually under the name of energy voucher on 600,000 households for the last five years, and this is a unique case and hard to monitor worldwide. Therefore, no studies have been conducted to assess impacts of the energy voucher on energy consumption and cost burden alleviation for beneficiaries. This paper aims to demonstrate the effectiveness of energy vouchers in terms of energy expense. The propensity score matching was conducted on samples of low-income households based on the Korea Welfare Panel. Then, simple Difference-In-Differences and Fixed-Effect Difference-In-Differences models were applied to estimate the effect of energy vouchers. In results, the beneficiaries of energy vouchers would spend an additional 4,371~4,870 won per month on energy consumption. The ratio is equivalent to 51.9~57.7 percent of the aid, which is also the highest when compared with 23~56 percent of U.S. Food Stamp. In terms of energy welfare, voucher payment could become one of the best management practices. However, identifying the blind spots as non-reciprocal households and expanding the differential support mechanism that reflects the energy consumption environment should be solved in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.626-631
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• 2019

The policy-making and technological development for the supply expansion of eco-friendly automobiles has been continuing, but the internal combustion engines still accounts for about 95%. Also, in order to meet the stricter emission regulations of internal combustion engines based on fossil fuels, the proportion of after-treatments for vehicles and (ocean going) vessels is gradually increasing. This study is a basic study for the post-Euro-VI exhaust response of CNG buses, and it is to investigate the basic characteristics according to Pd substitution transition metal effect, catalyst volume effect and space velocity. A catalysts was prepared and tested using a model gas reactor. The NGOC catalyst with 3Pd exhibited the highest catalytic activity with 22% at $300^{\circ}C$, 48% at $350^{\circ}C$ and about 75% at $500^{\circ}C$. 3Co NGOC containing 3wt% of transition metal was excellent in oxidation ability, and it was small in size of 2nm, and the degree of catalyst dispersion was improved and de-NO/CO conversion was high. The volume of the NGOC-LNT-SCR catalyst system was optimal in the combination of 1.5+0.5+0.5 with a total score of 165, considering $de-CH_4/NOx$ performance and catalyst cost. For SV $14,000h^{-1}$, the $CH_4$ reduction performance was the highest at about 20%, while the SV $56,000h^{-1}$ was the lowest at about 5%. If the space velocity is small, the flow velocity decreases and the time remaining in the catalyst volume become long, so that the harmful gas was reduced.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.745-752
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• 2009

Management of social infrastructure has been advanced from facility management (FM) to asset management (AM), which adopts the aggressive and proactive methods in predicting the deterioration of infrastructure, prevents failures, and eventually saves maintenance cost. Infrastructure asset management is not a simple engineering technique, but it is a new paradigm evolved from facility management practices. To implement the infrastructure asset management successfully, organizational reform is very important. This paper suggests critical success factors and key performance indicators to implement the infrastructure asset management for facility managers of government owned social infrastructures such as roads and bridges. Reorganizing the facility management group requires new vision, objectives, strategies for the paradigm-changing asset management. This paper uses Balanced Score Card (BSC) which is a proven method in measuring and setting new objectives for an organization. Once the performance indicators are reviewed repeatedly by facility managers through experts workshops, developed BSC can be used in practice. This paper discusses the development of robust BSC scoring method through in depth literature reviews and investigation of asset management practices of domestic and international cases.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.295-302
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• 2023

Software requirements written in natural language may have different meanings from the stakeholders' viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because the efficient design is possible only when appropriate architectural tactics for each quality attribute are selected. As a result, although many natural language processing models have been studied for the classification of requirements, which is a high-cost task, few topics improve classification performance with the imbalanced quality attribute datasets. In this study, we first show that the classification model can automatically classify the Korean requirement dataset through experiments. Based on these results, we explain that data augmentation through EDA(Easy Data Augmentation) techniques and undersampling strategies can improve the imbalance of quality attribute datasets, and show that they are effective in classifying requirements. The results improved by 5.24%p on F1-score, indicating that handling imbalanced data helps classify Korean requirements of classification models. Furthermore, detailed experiments of EDA illustrate operations that help improve classification performance.

• 한국노년학
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• v.25 no.2
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• pp.195-209
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• 2005

The purpose of this study was to identify the factors influencing file-up family stress in the caregivers of patients with dementia. Data was collected by questionnaires from 102 families with a member having a dementia, at neurology departments of hospitals, temporary shelter for dementia patient, and nursing homes for the elderly. The data was analyzed using descriptive statistics, pearson correlation coefficients, and multiple regression. In results, the score of file-up stress showed a significantly negative correlation with the score of level of family hardiness(r=-.200, p=.026), social support(r=-.361, p=.004), relative and friend support(r=-.416, p=.001), and F-COPES(r=-.345, p=.048). The multiple regression analysis revealed that the most powerful predictor of file-up family stress was family cost for patients with dementia. The results contribute to the understanding of Korean family caregivers' perceptions of caregiveing. Further researches should be conducted with the consideration of Korean traditional custom that family should take care of the elderly family members.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1345-1354
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• 2020

Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. Materials and Methods: PubMed MEDLINE and EMBASE were searched using the terms 'cognitive impairment' or 'Alzheimer' or 'dementia' and 'radiomic' or 'texture' or 'radiogenomic' for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. Results: The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. Conclusion: The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

• Korean Journal of Radiology
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• v.23 no.1
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• pp.77-88
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• 2022

Objective: Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods: PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results: External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the "gold standard" (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion: The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.