• Journal of Family Resource Management and Policy Review
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• v.19 no.1
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• pp.163-180
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• 2015

The purpose of this study was to explore a typology of time allocation, investigate determinants of time allocation types, and analyze differences in time use satisfaction by the types of time use of the elderly. The data source for this research was the 2009 Time Use Survey conducted by the Korea National Statistical office (KNSO). The 4,699 time diaries (3,552 for weekday, 1,147 for Sunday) completed by the elderly over the age of 60 were analyzed using mean, standard deviation, chi-square, cluster analysis, ANOVA analysis, Duncan test, and multinomial logistic regression analysis. Time allocation of the elderly was classified into four types: personal care oriented, work oriented, leisure oriented, and balanced type. Gender, age, education, employment status, income, and the presence of spouse were identified as determinants for each type. According to the types of time allocation, time use satisfaction was different on week days.

• Journal of the Optical Society of Korea
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• v.1 no.1
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• pp.60-64
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• 1997

An image processing algorithm is presented that can identify the orientation as well as the left/right side (parity) of the digitized radiographs. The orientation was found by computing the mean square deviation between the sampled gray values along the center and their best-fit linear regression relations. The parity was determined by comparing the area difference between two thresholded images of the left and the right side around the heart, which is assumed to be around the center of the image. This method was tested with 86 images with their orientations intentionally rotated. The rotation was limited to multiples of 90 degrees, as this was the way the rotation is most likely to happen in the clinical environment. We obtained positive responses for 85 out of 86 images subject to the screening.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1275-1283
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• 2006

Condensation heat transfer coefficients in a 7.92 mm inside diameter copper smooth tube were obtained experimentally for R22, R134a, and R410A. Working conditions were in the range of $30-40^{\circ}C$ condensation temperature, $95-410 kg/m^2s$ mass flux, and 0.15-0.85 vapor quality. The experimental data were compared with the eight existing correlations for an annular flow regime. Based on the heat-momentum analogy, a condensation heat transfer coefficients correlation for the annular flow regime was developed. The Breber et al. flow regime map was used to discern flow pattern and the Muller-Steinhagen & Heck pressure drop correlation was used for the term of the proposed correlation. The proposed correlation provided the best predicted performance compared to the eight existing correlations and its root mean square deviation was less than 8.7%.

• Nuclear Engineering and Technology
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• v.28 no.3
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• pp.331-338
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• 1996

In the thermal-hydraulic design, the effect of fuel rod bow is quantified tv the rod bow DNBR penalty which is a key design parameter to assure the coolability of fuel assembly in the pressurized water reactor. In this work, a computer program for the evaluation of the rod bow DNBR penalty based on Westinghouse methodology is developed and its application procedure is proposed. The computer simulation is based on the Monte-Carlo method. The qualification of developed computer program is performed by a comparison of calculational result with that given by Westinghouse's document. A new application procedure is built using batch mean and batch standard deviation. The normality of sample population generated by the batch calculation is confirmed by means of a chi-square test for goodness of fit. On the view point of statistics it is effected that the more reliable design value may be produced by the new application procedure.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1203-1210
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• 2021

In this paper, we developed the inspection system of electric vehicle pouch battery using image processing. Line scan cameras are used for acquiring the all parts of the pouch battery, and several steps of image processing for extracting significant dimensions(User Required Position) of the battery. In image processing, edge lines, node points, dimension lines, etc. were extracted using Preprocessor, Square Edge Detection, and Size Detection algorithms. This is used to measure the dimensions of the location requested by the user on the pouch battery. For verification of the inspection system, the dimensions of three pouch batteries produced in the same process were measured, and the mean and standard deviation were obtained to confirm the precision.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.4
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• pp.285-294
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• 2018

In this study, we implemented the data quality monitoring algorithm which is the previous step for real-time Global Navigation Satellite System (GNSS) correction generation and compared Global Positioning System (GPS) and BeiDou System (BDS). Signal Quality Monitoring (SQM), Data QM, and Measurement QM (MQM) that are well known in Ground Based Augmentation System (GBAS) were used for quality monitoring. SQM and Carrier Acceleration Ramp Step Test (CARST) of MQM result were divided by satellite elevation angle and analyzed. The data which are judged as abnormal are removed and presented as Root Mean Square (RMS), standard deviation, average, maximum, and minimum value.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

• Computers and Concrete
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• v.30 no.1
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• pp.33-42
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• 2022

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.

• Advances in nano research
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• v.15 no.2
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• pp.105-111
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• 2023

Integrin αvβ3 is one of the receptors expressed in cancer cells. RGD peptides have the potential to target integrin αvβ3 (receptor), which can increase drug delivery efficiency. In this study, 55 different RGD dimer motifs were investigated. At first, the binding energy between RGD peptides and the receptor was calculated using molecular docking. Then, three RGD peptides with the strongest binding energy with the receptor were selected, and their dynamic adsorption on the receptor was simulated by molecular dynamics (MD). The obtained results showed that a sequence that has RGD at the beginning and end with tryptophan (TRP) has strong Lennard-Jones (LJ) and electrostatic interactions with Integrin αvβ3 and has changed the conformation of receptor significantly, which analyzed by root mean square deviation (RMSD) and radius of gyration.

• Computers and Concrete
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• v.32 no.6
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.