• Journal of Distribution Science
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• v.20 no.6
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• pp.21-32
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• 2022

Purpose: This study aims to test the influence of brand relationship, price, content, brand awareness, and electronic Word-Of-Mouth (eWOM) on willingness to pay for the subscription fee of Disney+ Hotstar. As the latest streaming service provider in Indonesia, Disney + Hotstar under Disney Media and Entertainment Distribution has actively conducted strategies to strengthen the brand and attract consumers. Research design, data and methodology: Structural Equation Modelling with WarpPLS approach was used to assess the proposed model gathering data from 316 people who have ever known about Disney+ Hotstar through an online survey using measurement items from previous literature. Results: Most responses were obtained from millennial generations. Findings demonstrated that brand relationships, price, content, and brand awareness positively influenced willingness to pay for the subscription fee whereas eWOM showed a negative and insignificant influence on the willingness to pay for the subscription fee. Conclusions: The most significant factor towards willingness to pay a for subscription fee is price, followed by brand awareness, brand relationship, and content. The result of this study may be used as a guide for professionals in the streaming service industry to better implement their strategies in influencing people to have the willingness to subscribe.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.47.1-47.1
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• 2020

Multi-band photometry provides an advantage in being able to perform taxonomic classification analysis on a large number of asteroids in a much shorter period of time than spectroscopy. We observed main-belt asteroids using Korea Microlensing Telescope Network (KMTNet) in CTIO during the summer seasons in the southern hemisphere, mostly in December 2015, 2016 and 2017 with two visible photometric systems, SDSS (g, r, i, and z), and Johnson-Cousins (B, V, R, and I). Targets were selected for the asteroids which had already been classified based on Bus-Binzel taxonomy (Bus & Binzel, 2002) and DeMeo taxonomy (DeMeo et al. 2009). Not only the targets but also numerous serendipitously observed asteroids were identified. In summary, 6817 and 5456 known objects, including 307 and 233 already classified asteroids were observed with SDSS and Johnson-Cousins systems, respectively. Using principal component analysis, the three major asteroid complexes and a class, S-, C-, and X-complexes and V class are found to be well separated in the principal component plane (spectral slope and 1 micron absorption depth) with both filter systems. We will present and discuss the results of our newly proposed three-dimensional color taxonomy for asteroids using the whole dataset (Roh et al., to be submitted).

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2698-2703
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• 2024

A bilateral comparison was conducted between the International Atomic Energy Agency (IAEA) and the Korea Research Institute of Standards and Science (KRISS) to measure the absorbed dose to water in accelerator photon beams. KRISS served as a linking laboratory to compare the IAEA standard with the key comparison reference value (KCRV) of the BIPM.RI(I)-K6 program, in which KRISS participated in 2017. Two ionization chambers from the IAEA were used as transfer instruments for the comparison. Both laboratories measured the calibration coefficients of these instruments and calculated the ratios. The ratio of the KRISS standard to the KCRV was applied to obtain the degree of equivalence of the IAEA, along with its uncertainty. The largest deviation of the IAEA measurement from the KCRV was 3.4 mGy/Gy, significantly smaller than the expanded uncertainty of 10.7 mGy/Gy (k = 2, 95% level of confidence). This study demonstrates the equivalence of IAEA's measurement standard for accelerator photon beams to other primary standard dosimetry laboratories. It provides evidence for the satisfactory operation of IAEA's quality management system and enhances the international credibility of the IAEA SSDL network, particularly in high-energy accelerator photon beams from linear accelerators.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.197-205
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• 2002

The objectives of this Paper is to implement a diagnostic classifier of differential laryngeal diseases from acoustic signals acquired in a noisy room. For this Purpose, the voice signals of the vowel /a/ were collected from Patients in a soundproof chamber and got mixed with noise. Then, the acoustic Parameters were analyzed, and hierarchical neural networks were applied to the data classification. The classifier had a structure of five-step hierarchical neural networks. The first neural network classified the group into normal and benign or malign laryngeal disease cases. The second network classified the group into normal or benign laryngeal disease cases The following network distinguished polyp. nodule. Palsy from the benign laryngeal cases. Glottic cancer cases were discriminated into T1, T2. T3, T4 by the fourth and fifth networks All the neural networks were based on multilayer perceptron model which classified non-linear Patterns effectively and learned by an error back-propagation algorithm. We chose some acoustic Parameters for classification by investigating the distribution of laryngeal diseases and Pilot classification results of those Parameters derived from MDVP. The classifier was tested by using the chosen parameters to find the optimum ones. Then the networks were improved by including such Pre-Processing steps as linear and z-score transformation. Results showed that 90% of T1, 100% of T2-4 were correctly distinguished. On the other hand. 88.23% of vocal Polyps, 100% of normal cases. vocal nodules. and vocal cord Paralysis were classified from the data collected in a noisy room.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.20-26
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• 2018

This study was conducted to develop a device for measuring the air flow by space variation through monitoring program, which acquires data by each point from each environmental sensor located in the greenhouse. The distribution of environmental factors(air temperature, humidity, wind speed, etc.) in the greenhouse is arranged at 12 points according to the spatial variation and a large number of measurement points (36 points in total) on the X, Y and Z axes were selected. Considering data loss and various greenhouse conditions, a bit rate was at 125kbit/s at low speed, so that the number of sensors can be expanded to 90 within greenhouse with dimensions of 100m by 100m. Those system programmed using MATLAB and LabVIEW was conducted to measure distributions of the air flow along the greenhouse in real time. It was also visualized interpolated the spatial distribution in the greenhouse. In order to verify the accuracy of CFD modeling and to improve the accuracy, it will compare the environmental variation such as air temperature, humidity, wind speed and $CO_2$ concentration in the greenhouse.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2221-2231
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• 2017

In this paper, we surveyed previous cases, network protocols (such as Wi-Fi, Zigbee, Z-wave, and WirelessHart) and propagation characteristics on the application of maintaining equipments for instrumentation and control (I&C) using wireless communication techniques inside the nuclear power plant (NPP). In addition, we compared and analyzed the difference of detailed regulations with respect to the electromagnetic interference (EMI) and radio frequency interference (RFI) in the Regulatory Guide 1.180 rev. 1 (RG. 1.180) for adopting the wireless communication techniques inside the NPP, and other regulations, such as MIL-STD 461E and IEC 61000-4, that are recognized in the KINS/RG-N03.09 (Rev. 2). Furthermore, we investigated evaluating factors about electromagnetic properties by considering indoor environments, wave scattering, shielding effectiveness, and the indoor wave attenuation model that were not included in the current electromagnetic compatibility regulation.

• Geophysics and Geophysical Exploration
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• v.14 no.4
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• pp.298-304
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• 2011

We conducted magnetic survey using IBRV (Ice Breaker Research Vessel) ARAON of KORDI (Korea Ocean Research and Development Institute), ROV (Remotely Operated Vehicle) of Oceaneering Co. and three components vector magnetometer, at Apr., 2011 in the western slope of the caldera of TA25 seamount, the Lau Basin, the southwestern Pacific. The depth ranges of the survey area are from about 900 m to 1200 m, below sea level. For the deep sea magnetic survey, we made the nation's first small deep sea three components magnetometer of Korea. The magnetometer sensor and the data logger was attached with the upper part and lower part of ROV, respectively. ROV followed the planning tracks at 25 ~ 30 m above seafloor using the altimeter and USBL (Ultra Short Base Line) of ROV. The three components magnetometer measured the X (North), Y (East) and Z (Vertical) vector components of the magnetic field of the survey area. A motion sensor provided us the data of pitch, roll, yaw of ROV for the motion correction of the magnetic data. The data of the magnetometer sensor and the motion sensor were recorded on a notebook through the optical cable of ROV and the network of ARON. The precision positions of magnetic data were merged by the post-processing of USBL data of ROV. The obtained three components magnetic data are entirely utilized by finding possible hydrothermal vents of the survey area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1336-1347
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• 1991

In this paper, The PC interface for packet terminal of ISDN is designed and implemented in order to build packet communication networks which share computer resources and exchange informations between computer in the ISDN environment. The PC interface for packet terminal of ISDN constitutes S interface handler part which controls functions of ISDN layer1 and layer 2, constitutes packet handler part which controls services of X.25 protocol in the packet level.Where, The function of ISDN layer1 provides rules of electrical and mechanical characteristics, services for ISDN layer 2. The function of ISDN layer 2 provides function of LAPD procedure, services for X.25 The X.25 specifies interface between DCE and DTE for terminals operrating in the packet mode. The S interface handler part is orfanized by Am 79C30 ICs manufactured by Advanecd Micro Devices. ISDN packet handler part is organiged by AmZ8038 for FIFO for the purpose of D channel. The common signal procedure for D channel is controlled by Intel's 8086 microprocessor. The S interface handler part is based on ISDN layer1,2 is controlled by mail box in order to communicate between layers. The ISDN packet handler part is based on module in the X.25 lebel. The communication between S interface handler part and ISDN packet handler part is organized by interface controller.

• Journal of Korean Academy of Nursing
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• v.46 no.4
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• pp.475-489
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• 2016

Purpose: This study was a systematic review and meta-analysis designed to investigate the effects of stimulation-oriented interventions for behavioral problems among people with dementia. Methods: Based on the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), a literature search was conducted using seven electronic databases, gray literature, and other sources. Methodological quality was assessed using the Scottish Intercollegiate Guidelines Network (SIGN) for randomized controlled trials (RCTs). Data were analyzed using R with the 'meta' package and the Comprehensive Meta-Analysis (CMA 2.0) program. Results: Sixteen studies were included for meta-analysis to investigate the effect of stimulation-oriented interventions. The quality of individual studies was rated as '++' for eight studies and '+' for the rest. The effect sizes were analyzed according to three subgroups of interventions (light, music, and others); Hedges' g=0.04 (95% CI: -0.38~0.46), -0.23 (95% CI: -0.56~0.10), -0.34 (95% CI: -0.34~0.00), respectively. To explore the possible causes of heterogeneity ($I^2=62.8%$), meta-regression was conducted with covariates of sample size, number of sessions, and length of session (time). No moderating effects were found for sample size or number of sessions, but session time showed a significant effect (Z=1.96, 95% CI: 0.00~0.01). Finally, a funnel plot along with Egger's regression test was performed to check for publication bias, but no significant bias was detected. Conclusion: Based on these findings, stimulation-oriented interventions seem to have a small effect for behavioral problems among people with dementia. Further research is needed to identify optimum time of the interventions for behavioral problems among dementia pateints.