• Journal of Korean Academy of Nursing
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• v.46 no.6
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• pp.793-803
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• 2016

Purpose: Developmental care has been recognized as a very important component for the development and health promotion of preterm infants. However, research on how to assess developmental nursing competency has not been studied as expected. This study was done to develop and evaluate a new scale to measure nursing competency for developmental support of preterm infants. Methods: Concept analysis was done with using the Hybrid model of Schwartz-Barcott and Kim (2000), from which a preliminary new scale (30 items) was developed. To test the validity and reliability of the new scale being developed, data were collected from 122 NICU nurses at 4 hospitals in 3 cities in the Republic of Korea, from December, 2014 to March, 2015. Results: The final version of the Developmental Support Competency Scale for Nurses (DSCS-N) caring for premature infants was a 4-point Likert type scale, consisting of 19 items, and categorized as 6 factors, explaining 62.5% of the total variance. Each of the factors were named as follows; 'environmental support' (4 items), 'parental support' (3 items), 'interaction' (3 items), 'critical thinking' (3 items), 'professional development' (3 items), and 'partnership' (3 items). The Cronbach's ${\alpha}$ coefficient for the scale was .83 and the reliability of the subscales ranged from .60~.76. Conclusion: The psychometric evaluation of the new scale demonstrated an acceptable validity and reliability. Findings indicate that the DSCS-N can be used as the tool to test the effect of educational programs for nurses and contribute to advance developmental care for preterm infants.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.95-102
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• 2004

The COSMOS is an adaptive traffic control systems that can adjust signal timing parameters in response to various traffic conditions. To evaluate the performance of the COSMOS systems, the field study is only practical option because any evaluation tools are not available. To overcome this limitation, a newly integrated interfacing simulator between a microscopic simulation program and COSMOS was developed. In this paper, a detector module and a signal timing module as well as general feature of the simulator were described. A validation test was performed to verify the accuracy of the data flow within the simulator. It was shown that the accuracy level of information from the simulator was high enough for real application. Several practical comments on further studies were also included to enhance the functional specifications of the simulator.

• Journal of Food Hygiene and Safety
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• v.35 no.3
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• pp.252-260
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• 2020

This study was conducted to improve the standard method for vitamin B₁₂ and biotin contained in foods for special dietary uses to ensure the specificity of the complex matrix properties of foods. For the food code, the test method was improved to determine vitamin B₁₂ and biotin by high-performance liquid chromatography (HPLC)-UV using column-switching after concentration using immunoaffinity column. The immunoaffinity columns contain a gel suspension of monoclonal antibody specific to the vitamin of interest so that it can be used to concentrate the vitamin B₁₂ and biotin and remove interferences from the food extracts. Moreover, validation of advanced new methods was carried out to support the suitability of the proposed analytical procedure (specificity, linearity, detection limits (LOD), quantitative limits (LOQ), accuracy, and precision). The improved analytical method is being used to monitor relevant food items on sale. The results of this study showed that the new analytical method is suitable and appropriate for managing food intended for special dietary uses.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.86-98
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• 2021

In this study, we developed a simulation model for supply chain management of Precast Concrete (PC) based construction. To this end, information on the Factory Production/Site Construction system was collected through literature review and field research, and based on this information, a simulation model was defined by describing the supply chain, entities, resources, and processes. Next, using the Arena simulation software, a simulation model for the PC supply chain was developed by setting model frameworks, data modules, flowchart modules, and animation modules. Finally, verification and validation were performed using five review methodologies such as model check, animation check, extreme value test, average value test, and actual case test to the developed model. As a result, it was found that the model adequately represented the flows and characteristics of the PC supply chain without any logical errors and provided accurate performance evaluation values for the target supply chains. It is expected that the proposed simulation model will faithfully play a role as a performance evaluation platform in the future for developing management techniques in order to optimally operate the PC supply chain.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.92-101
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• 2003

The accessibility evaluation of ten web-based loaming content in Korea was performed with the following procedure : (1) A primitive metric of the compliance of those contents to the WCAG (Web Content Accessibility Guidelines) 1.0 was obtained using Bobby, a widely used accessibility checker. (2) SGML validation test was carried out. (3) The contents were rendered with various browsers including a text-mode browser. (4) They were manually checked as to whether they satisfy the accessibility criteria proposed by W3C. Most of the tested contents scored low marks in all the test categories partly because they were apparently developed with little attention paid to web standard conformance, browser compatibility, and device-independence. They also put heavy emphasis on audio-visual effects catering only to the best-equipped users and offering no alternate access route for those in restricted environment. As more information and learning materials are delivered through the Internet, these low accessible contents would lead to a deeper information divide. The accessibility needs to be regarded as an important factor in evaluating the quality of loaming content.

• Journal of Food Hygiene and Safety
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• v.31 no.5
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• pp.327-334
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• 2016

This study was conducted to establish the standard method for the contents of biotin in milk formulas. To optimize the method, we compared several conditions for liquid extraction, purification and instrumental measurement using spiked samples and certified reference material (NIST SRM 1849a) as test materials. LC-MS/MS method for biotin was established using $C_{18}$ column and binary gradient 0.1% formic acid/acetonitrile, 0.1% formic acid/water mobile phase is applied for biotin. Product-ion traces at m/z 245.1 ${\rightarrow}$ 227.1, 166.1 are used for quantitative analysis of biotin. The linearity was over $R^2=0.999$ in range of $5{\sim}60{\mu}g/L$. For purification, chloroform was used as a solvent for eliminating lipids in milk formula. The linearity was over 0.999 in range of 5~60 ng/mL. The detection limit and quantification limit were 0.10, 0.31 ng/mL. The accuracy and precision of LC-MS/MS method using CRM were 103%, 2.5% respectively. Optimized methods were applied in sample analysis to verify the reliability. All the tested milk formulas were acceptable contents of biotin compared with component specification and standards for nutrition labeling. The standard operating procedures were prepared for biotin to provide experimental information and to strengthen the management of nutrient in milk formula.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of Veterinary Clinics
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• v.18 no.4
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• pp.329-333
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• 2001

Evaluation on the diagnostic performances of urease test and polymerase chain reaction (PCR) for detection of Helicobacter species infection in dogs has rarely been performed in research with site-specific situations, although assessing diagnostic tests is an essential part prior to its practical use in a variety of clinical settings. The clinical value of a diagnostic test may be misjudged and comparisons between different tests may yield misleading conclusions when high within-patient correlations are present. We applied a conceptually simple statistical approach to estimate the sensitivity and specificity of urease test and PCR for detection of Helicobacter species infection in dogs. This approach assumes that responses from three different sampling sites within an animal are correlated where unit for statistical analysis is the site rather than the animal. The sensitivity and specificity of urease test was 0.74% (95% confidence interval, 0.64-0.84) and 0.87 (95% CI, 0.67-1.00), respectively. For PCR, the sensitivity was 0.95(95% CI, 0.89-1.00) and specificity 0.90 (95% CI, 0.70-1.00). Two tests were almost equally specific. Urease test, however, has a lower diagnostic accuracy and thus should only be used after careful validation in terms of sensitivity.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.465-473
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• 2017

The purpose of this study was to verify the validity of the developed evaluation method for competition intelligence of sport talented children of of Kim et al.(2015). For this purpose, total 3 times of observation by sports leaders were accomplished for evaluation of competition intelligence. After finishing final observation, the correlation between the sub-factors(practice intelligence, learning intelligence, and training intelligence) and gifted students' physical fitness and KOSTASS were examined. First, as the reliability, the correlations between leaders' observation results were significantly higher than 0.8 in all sub-factors. Second, all sub-factors of competition intelligence showed significant positive correlation with in standing jump, 50m dash and side step test improvement(p<.05). Third, all sub-factors showed no significant correlation with KOSTASS(p>.05). These results showed that the evaluation tool by Kim et al.(2015) didn't not fully reflect the improvement level of basic physical fitness factors of the gifted students. However, the results of correlation between physical fitness factors requiring technical learning process showed that the gymnastic gauging tools included learning process and task acquisition level.