• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.50-58
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• 2013

Purpose: The purpose of this study was to evaluate measurement properties of self-report questionnaires for studies published in Korean nursing journals. Methods: Of 424 Korean nursing articles initially identified, 168 articles met the inclusion criteria. The methodological quality of the measurements used in the studies and interpretability were assessed using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. It consists of items on internal consistency, reliability, measurement error, content validity, construct validity including structural validity, hypothesis testing, cross-cultural validity, and criterion validity, and responsiveness. For each item of the COSMIN checklist, measurement properties are rated on a four-point scale: excellent, good, fair, and poor. Each measurement property is scored with worst score counts. Results: All articles used the classical test theory for measurement properties. Internal consistency (72.6%), construct validity (56.5%), and content validity (38.2%) were most frequently reported properties being rated as 'excellent' by COSMIN checklist, whereas other measurement properties were rarely reported. Conclusion: A systematic review of measurement properties including interpretability of most instruments warrants further research and nursing-focused checklists assessing measurement properties should be developed to facilitate intervention outcomes across Korean studies.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.1-8
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• 2011

A mobile device is a handheld device, typically having a display screen with touch input and/or a miniature keyboard and accessible to the Internet. Because mobile devices are innovative and rapidly changing products, consumers are confronted with a decision-making problem to select the best device. This paper proposes an Analytic Hierarchy Process method to assess mobile devices and shows a method to select best mobile devices when purchasing. For this paper, the mobile devices considered as in the AHP model included net-book, tablet PC, smart phone, and camera phone. We derived 6 main criteria and 15 sub-criteria to synthesize the overall priority of criteria and mobile devices. The criterion of convenience in carrying showed the highest rank in the criteria. Although smart phones had the highest priority, the four mobile devices did not show significant differences in consumer preference.

• Journal of The Korean Astronomical Society
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• v.28 no.2
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• pp.139-146
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• 1995

The bright part of the halo luminosity function is derived from a sample of the 233 NLTT propermotion stars, which are selected by the 220 km/ see of cutoff velocity in transverse to rid the contamination by the disk stars and corrected for the stars omitted in the sample by the selection criterion. It is limited to the absolute magnitude range of $M_v=4-8$, but is based on the largest sample of halo stars up to now. This luminosity function provides a number density of $2.3{\cdot}10^{-5}pc^{-3}$ and a mass density of $2.3{\cdot}10^{-5}M_{o}pc^{-3}$ for 4 < $M_v$ < 8 in the solar neighborhood. These are not sufficient for disk stability. The kinematics of the sample stars are < U > = - 7 km/sec, < V > = - 228 km/sec, and < W > = -8 km/sec with (${\sigma_u},{\sigma_v},{\sigma_w}$) = (192, 84, 94) km/sec. The average metallicity of them is [Fe/H] = $- 1.7{\pm}0.8$. These are typical values for halo stars which are selected by the high cutoff velocity. We reanalyze the luminosity function for a sample of 57 LHS proper-motion stars. The newly derived luminosity function is consistent with the one derived from the NLTT halo stars, but gives a somewhat smaller number density for the absolute magnitude range covered by the LF from NLTT stars. The luminosity function based on the LHS stars seems to have a dip in the magnitude range corresponding to the Wielen Dip, but it also seems to have some fluctuations due to a small number of sample stars.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.360-366
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• 2003

Although multilayer perceptrons (MLPs) present several advantages against other pattern recognition methods, MLP-based speaker verification systems suffer from slow enrollment speed caused by many background speakers to achieve a low verification error. To solve this problem, the quantitative discriminative cohort speakers (QnDCS) method, by introducing the cohort speakers method into the systems, reduced the number of background speakers required to enroll speakers. Although the QnDCS achieved the goal to some extent, the improvement rate for the enrolling speed was still unsatisfactory. To improve the enrolling speed, this paper proposes the qualitative DCS (QlDCS) by introducing a qualitative criterion to select less background speakers. An experiment for both methods is conducted to use the speaker verification system based on MLPs and continuants, and speech database. The results of the experiment show that the proposed QlDCS method enrolls speakers in two times shorter time than the QnDCS does over the online error backpropagation(EBP) method.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.231-238
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• 2002

As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.4
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• pp.434-439
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• 2008

The amount of salt to make seed sorting solution of the specific gravity of 1.13 was reconsidered and determined as 3.8 kg salt in 18 L water, which is lower amount than currently used. Five rice cultivars were examined. Percent germination and seedling emergence were not similar. Seedling emergence rate of Japonica varieties, Nampyungbyeo and Daerypbyeo-1 were 87% and 95% under specific gravity of 1.13, respectively. Seedling emergence rate of Tongil type variety, Dasanbyeo was as high as 67% in specific gravity of 1.06. Seedling emergence rate of waxy rice, Hwasunchalbyeo and Aranghangchalbyeo were examined. Seedling emergence rate was 94% in both cultivars in specific gravity of 1.04. Seedling emergence rate was same in specific gravity of 1.08 which is generally used for selecting seed currently. Early growth (plant height, leaf number, and dry weight) were not significantly different by specific gravity within species. In all cultivars except waxy rice, highest seedling emergence rate was observed in specific gravity of 1.13 which is currently used for selection and decreased as specific gravity is lowed. However, considering total amount of seeds in each group of specific gravity, amount of seed in lower specific gravity group is relatively small and total seedling emergence rate within variety dose not show big difference. However, if seeds with low speicific gravity are produced due to the bad grain filling condition and consequently total seed content of low specific gravity increases, results will be differnt. Reduction in total growth and yield could occur. It will be important to comply with the seed sorting criterion of 1.13 for Japonica, 1.06 for Tongil, and 1.04 for waxy rice variety to ensure the maximum rice growth and yield.

• Animal Bioscience
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• v.34 no.2
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• pp.185-191
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• 2021

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses. Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model. Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model). Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

• Journal of Sensor Science and Technology
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• v.30 no.3
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• pp.139-147
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• 2021

Numerical analysis was performed using FIMMWAVE to optimize the specifications of Si₃N₄/SiO₂ slot and ridge-slot optical waveguides based on confinement factor and effective mode area. The optimized specifications were confirmed based on sensitivity in terms of the refractive index of the analyte. The specifications of the slot optical waveguide, i.e., the width of the slot and the width and height of the rails, were optimized to 0.2 ㎛, 0.46 ㎛, and 0.5 ㎛ respectively. When the wavelength was 1.55 ㎛ and the refractive index of the slot was 1.3, the confinement factor and effective mode area of 0.2024 and 2.04 ㎛², respectively, were obtained based on the optimized specifications. The thickness of the ridge and the refractive index of the slot were set to 0.04 ㎛ and 1.1, respectively, to optimize the ridge-slot optical waveguide, and the confinement factor and effective mode area were calculated as 0.1393 and 2.90 ㎛², respectively. When the confinement coefficient and detection degree of the two structures were compared in the range of 1 to 1.3 of the analyte index, it was observed that the confinement coefficient and sensitivity were higher in the ridge-slot optical waveguide in the region with a refractive index less than 1.133, but the reverse situation occurred in the other region. Therefore, in the implementation of the integrated optical biochemical sensor, it is possible to propose a selection criterion for the two parameters depending on the value of the refractive index of the analyte.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.65-75
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• 2021

This study explores multiple variables of an OTT service for discovering hidden relationship between rating and the other variables of each successful and failed content, respectively. In order to extract key variables that are strongly correlated to the rating across the contents, this work analyzes 170 Netflix original dramas and 419 movies. These contents are classified as success and failure by using the rating site IMDb, respectively. The correlation between the contents, which are classified via rating, and variables such as violence, lewdness and running time are analyzed to determine whether a certain variable appears or not in each successful and failure content. This study employs a regression analysis to discover correlations across the variables as a main analysis method. Since the correlation between independent variables should be low, check multicollinearity and select the variable. Cook's distance is used to detect and remove outliers. To improve the accuracy of the model, a variable selection based on AIC(Akaike Information Criterion) is performed. Finally, the basic assumptions of regression analysis are identified by residual diagnosis and Dubin Watson test. According to the whole analysis process, it is concluded that the more director awards exist and the less immatatable tend to be successful in movies. On the contrary, lower fear tend to be failure in movies. In case of dramas, there are close correlations between failure dramas and lower violence, higher fear, higher drugs.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.127-140
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• 2022

Recently, accelerators are attracting attention as a new startup incubator. This study empirically analyzed the relative importance and priority of decision-making factors when a startup selects an accelerator using the AHP technique. As a result, startup founders recognized the importance of decision-making factors in the order of funding factors, program factors, and accelerator characteristic factors. there was a difference in the relative importance of detailed factors between startup founders and accelerator workers, depending on the location of the business site and the presence or absence of sales performance. This suggests that accelerators need to strengthen the financial support capabilities and improve customized services to meet the needs of startups. This study can be used as a decision-making criterion when a startup selects an accelerator, and can be used as basic data for additional research on the accelerator program.