• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.233-239
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• 2005

In the present study, numerical simulation has been performed to investigate the characteristics of the mist flow through the fire suppression nozzles. The commercial CFD software, FLUENT with the proper modeling was applied in both the internal and external flow region of the spray nozzles. Applications were done to the full cone nozzle for the operation range of the low pressure and high flow-rate. Numerical validation was proved by the comparison of the experimental data. Parametric study of the key design factors was tried to improve the performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.577-583
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• 1994

The purpose of this paper is to study the force control law which can be implemented on a non-modified robot system. The external force control algorithm proposed in this paper can be designed by means of a classical and modern control law. We showed the validation and the possibility of muti-dimensional force control idea through the simulation and experiments. Also, the Iterative learning control is studied for compensating errors due to thr disturbances and nonlinear effects. The previous information(control input, error) was used to determine the control input of next trial. The experimental result show the vaidity of this algorithm.

• Transactions on Electrical and Electronic Materials
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• v.16 no.3
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• pp.107-111
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• 2015

Our work is based on the development of a numerical model to develop a methodology for predicting the aging and breakdown in insulation due to the dynamics of space charge packets. The model of bipolar charge transports is proposed to simulate space charge dynamic for high DC voltage in law-density polyethylene (LDPE), taking into account the trapping and detrapping of recombination phenomena, this model has been developed and experimentally validation. Theoretical formulation of the physical problem is based on the Poisson, the continuity and the transport equations as well as on the appropriate models for injection. Numerical results provide temporal and local distributions of the electric field, the space charge density for the different kinds of charges, conduction and displacement current densities, and the external current.

• Journal of the Korean Institute of Gas
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• v.20 no.4
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• pp.33-43
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• 2016

Grinding, weld deposition, type A sleeve, type B sleeve, composite sleeve, hot tapping and clamp are used as the method to repair the buried pipelines in the United States, UK and Europe. In the event of defect to the pipeline, they have repaired the pipeline through the fitness-for-service assessments. In addition, they have guidelines for the possible repair methods to apply to each type of damage, which is occurred due to the 3rd party construction or corrosion. According to the KGS FS551, Safety Validation in Detail including ECDA(External Corrosion Direct Assessment) as one method of integrity management should be carried out for the old pipeline which supply natural gas as the middle pressure in Korea. Where a defect on the pipelines is found, on the result of Safety Validation in Detail, the pipelines should be repaired or replaced by new piping. However, there are no guidelines or regulations regarding the repair and reinforcement of pipeline, so that, cutting the damaged pipeline and replacing it as a segment of new pipe is the only way in Korea until now. We have suggested pipeline repair methods including type A, B sleeve, composite sleeve, after the survey of foreign repair method and standards including the method of United States and the United Kingdom, and after analysis of the results on pipeline repair test including type A, type B sleeve and composite sleeve.

• The Korean Journal of Pain
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• v.33 no.2
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.880-886
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• 2014

Many railway signalling functions have increasingly depended on computer software with recent development in computing technology, leading to evolution into more flexible and intelligent railway signalling system. Meanwhile, software programs are likely to have many errors and the cost incurred by such errors has increased. Especially, if fatal software error occurs during railway operation, it may result in loss of lives. So the software verification and validation have become more important. It is needed for software functional safety tool to support these, but most commercial tools depend on direct access to the system's memory, resulting in many difficulties in application. Owing to such difficulties and complexity, they are rarely used in railway signalling system software validation. In this study, a new testing tool for software functional testing through an external interface that can be easily used in functional testing of software was developed. Such testing tool allows development and analysis of test cases for black-box testing through analysis of actually used interface protocols, leading to increased user convenience.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.57 no.1
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• pp.29-34
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• 2012

The objective of this study was to rapidly evaluate fatty acids in a collection of millet (Panicum miliaceum subsp. miliaceum) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour ($n$=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination ($R^2$) of 0.89, 0.89, 0.89, and 0.92 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values ($r^2$=0.64, 0.90, 0.79, and 0.89 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). Standard deviation/standard errors of cross-validation (SD/SECV) values were close to 3 (2.62, 2.40, 1.85, and 2.23 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic and total fatty acids characterizing millet germplasm. Among the samples, IT153514 showed an especially high content of fatty acids ($48.14mg\;g^{-1}$), whereas IT123909 had a very low content ($34.44mg\;g^{-1}$).

• Korean Journal of Breeding Science
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• v.42 no.5
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• pp.448-454
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• 2010

The objective of this study was to rapidly evaluate fatty acids in a collection of foxtail millet (Setaria italica (L.) P. Beauv) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour (n=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination ($R^2$) of 0.91, 0.89, 0.98 and 0.98 for strearic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values ($r^2=0.97$, 0.91, 0.99 for oleic, linoleic, and total fatty acids, respectively). Standard deviation/standard error of cross-validation (SD/SECV) values were greater than 3 (3.11, 5.45, and 7.50 for oleic, linoleic, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic, linolenic, and total fatty acids characterizing foxtail millet germplasm. Among the samples, IT153491 showed an especially high content of fatty acids ($84.06mg\;g^{-1}$), whereas IT188096 had a very low content ($29.92mg\;g^{-1}$).

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.