• Journal of Korean Academy of Nursing
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• v.50 no.3
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• pp.474-486
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• 2020

Purpose: The purpose of this study was to develop and validate a Korean version of the Self-Care for Aspiration Pneumonia Prevention (SCAPP-K) scale in older adults at risk of dysphasia. Methods: The Hertz and Baas model of scale development and validation was used. In the development stage, items were generated via literature review and interviews with medical experts, older adults, and caregivers. Ten experts assessed the items for content validity. Subsequently, 12 older adults participated in a pilot test to determine the comprehensibility and appropriateness of the SCAPP-K scale. The validation stage involved a cross-sectional survey with 203 older adults for exploratory factor analysis (EFA) and 200 older adults for confirmatory factor analysis (CFA) and to determine convergent and discriminant validity. To test the validity and reliability of the scale, EFA using principal component analysis with varimax rotation and CFA were conducted, and convergent and discriminant validity as well as internal consistency reliability were determined. Results: As a result of EFA, three self-care factors (knowledge, resources, behaviors) with 21 items were validated. The CFA and convergent and discriminant validity indicated the applicability of the three-factor self-care scale. The reliability of the SCAPP-K scale was acceptable, with Cronbach's α=.87~.91. Conclusion: The SCAPP-K scale has acceptable validity and reliability and can contribute to clinical practice, research, and education to improve self-care for the prevention of aspiration pneumonia in older adults at risk of dysphasia.

• Analytical Science and Technology
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• v.33 no.1
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• pp.23-32
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• 2020

Methamphetamine (MA) is the most common and available drug of abuse in Korea and its primary metabolite is amphetamine (AP). Detection of AP derivatives, such as MA, AP, phentermine (PT), MDA, MDMA, and MDEA by the use of immunoassay screening is not reliable and accurate due to cross-reactivity and insufficient specificity/sensitivity. Therefore, the analytical process accepted by most urine drug-testing programs employs the two-step method with an initial screening test followed by a more specific confirmatory test if the specimen screens positive. In this study, a gas chromatography-mass spectrometric (GC-MS) method was developed and validated for confirmation of MA and AP in human urine. Urine sample (500 µL) was added with N-isopropylbenzylamine as internal standard and ethyl chloroformate as a derivatization reagent, and then extracted with 200 µL of ethyl acetate. Extracted samples were analysed with GC-MS in the SIM/ Scan mode, which were screened by Cobas c311 analyzer (Roche/Hitachi) to evaluate the efficiency as well as the compatibility of the GC-MS method. Qualitative method validation requirements for selectivity, limit of detection (LOD), precision, accuracy, and specificity/sensitivity were examined. These parameters were estimated on the basis of the most intense and characteristic ions in mass spectra of target compounds. Precision and accuracy were less than 5.2 % (RSD) and ±14.0 % (bias), respectively. The LODs were 3 ng/mL for MA and 1.5 ng/mL for AP. At the screening immunoassay had a sensitivity of 100% and a specificity of 95.1 % versus GC-MS for confirmatory testing. The applicability of the method was tested by the analysis of spiked urine and abusers' urine samples.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.225-231
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• 2016

A military vehicle is driven at different usage modes with the army application and servicing conditions. For practical durability validation, DT(Development Test) on a new military vehicle should be run up to the durability target kilometers on test courses in the specified proving ground. Driving velocities with test courses at the endurance mode of DT are established definitely. However, OT(Operational Test) and initial endurance test of production car can't be performed only in the DT courses due to the development period limit. Therefore, this paper focuses on the method to analyze the relative road damages between the endurance test in DT and other endurance test. Road load acquisition tests on KLTV(Korean Light Tactical Vehicle) were implemented at 15 driving modes in off-road and cross-country courses of two tests. Wheel accelerations were processed through band-pass filter, and then the main frequency and maximum power of the signals were computed by PSD analysis. Finally, using the proving ground optimization based on RDS(Relative Damage Spectrum) characterization, the damage factors between off-roads of test courses were determined.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.393-405
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• 2020

Various geotechnical information is required to evaluate the stability of the ground and a foundation once liquefaction occurs due to earthquakes, such as the soil strength and groundwater level. The results of the Standard Penetration Test (SPT) conducted in Korea are registered in the National Geotechnical Information Portal System. If geotechnical information for a non-drilled area is needed, geostatistics can be applied. This paper is about the feasibility of obtaining ground information by the Empirical Bayesian Kriging (EBK) method and the Inverse Distance Weighting Method (IDWM). Esri's ArcGIS Pro program was used to estimate these techniques. The soil strength parameter of the drilling area and the level of groundwater obtained from the standard penetration test were cross-validated with the results of the analysis technique. In addition, Multichannel Analysis of Surface Waves (MASW) was conducted to verify the techniques used in the analysis. The Buk-gu area of Pohang was divided into 1.0 km×1.0 km and 110 zones. The cross-validation for the SPT N value and groundwater level through EBK and IDWM showed that both techniques were suitable. MASW presented an approximate section area, making it difficult to clearly grasp the distribution pattern and groundwater level of the SPT N value.

• Journal of the Korean Chemical Society
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• v.58 no.6
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.17-26
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• 2012

This paper presents an image processing-based validation method for unrecognizable numbers in severely distorted license plate images which have been degraded by various factors including low-resolution, low light-level, geometric distortion, and periodic noise. Existing vehicle license plate recognition (LPR) methods assume that most of the image degradation factors have been removed before performing the recognition of printed numbers and letters. If this is not the case, conventional LPR becomes impossible. The proposed method adopts a novel approach where a set of reference number images are intentionally degraded using the same factors estimated from the input image. After a series of image processing steps, including geometric transformation, super-resolution, and filtering, a comparison using cross-correlation between the intentionally degraded reference and the input images can provide a successful identification of the visually unrecognizable numbers. The proposed method makes it possible to validate numbers in a license plate image taken under low light-level conditions. In the experiment, using an extended set of test images that are unrecognizable to human vision, the proposed method provides a successful recognition rate of over 95%, whereas most existing LPR methods fail due to the severe distortion.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• The Korean Journal of Food And Nutrition
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• v.28 no.5
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• pp.871-879
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• 2015

24-hour recall is the dietary assessment method most frequently used to evaluate dietary intake; however, accuracy is an issue when using this method, especially in large-scale studies. The purpose of this study was to assess the validity of dietary intake estimation using one serving size. Estimates of energy and nutrients taken in over a 24-hr period based on actual intake amount (24HRAI) and based on estimates of one serving size (24HRSS) were compared. Data were analyzed using a paired t-test, Pearson's correlation coefficients, and a cross-classification method. In male subjects, intake levels of energy, fat, vitamin C, vitamin $B_1$, Zn, and total food measured using 24HRAI were significantly higher than those measured using 24HRSS. In female subjects, intake of carbohydrates, fiber, fat, vitamin A, vitamin C, vitamin B complexes, various minerals, and total food measured using 24HRAI were significantly lower than those measured using 24HRSS. Energy-adjusted Pearson's correlation coefficients revealed that intake of all nutrients showed a significant positive relationship between the two measurement methods in both males and females. Cross-classification analysis revealed that 50.5~67.6% of women and 40.3~71% of men were classified in the same quartile of intake of each nutrient when comparing data from 24HRAI and 24HRSS. We conclude that using one serving size in 24-hr recall analysis was valid and therefore may be used in studies to assess food consumption in the general adult population. Also, this method can be used to classify energy and nutrient intake into quartile, which is useful in examining the association between diet and chronic diseases.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.421-428
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• 2014

Like Alzheimer's disease, Parkinson's Disease(PD) is one of the most common neurodegenerative brain disorders. PD results from the deterioration of dopaminergic neurons in the brain region called the substantia nigra. Currently there is no cure for PD, but diagnosing in its early stage is important to provide treatments for relieving the symptoms and maintaining quality of life. Unlike many diagnosis methods of PD which use a single biomarker, we developed a diagnosis method that uses both biochemical biomarkers and imaging biomarkers. Our method uses ${\alpha}$-synuclein protein levels in the cerebrospinal fluid and diffusion tensor images(DTI). It achieved an accuracy over 91.3% in the 10-fold cross validation, and the best accuracy of 72% in an independent testing, which suggests a possibility for early detection of PD. We also analyzed the characteristics of the brain fiber pathways of Parkinson's disease patients and normal elderly people.

• Journal of the Korean Geotechnical Society
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• v.33 no.6
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• pp.27-36
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• 2017

Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.