• Smart Structures and Systems
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• v.6 no.5_6
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• pp.481-504
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• 2010

Wireless smart sensor networks (WSSNs) have been proposed by a number of researchers to evaluate the current condition of civil infrastructure, offering improved understanding of dynamic response through dense instrumentation. As focus moves from laboratory testing to full-scale implementation, the need for multi-hop communication to address issues associated with the large size of civil infrastructure and their limited radio power has become apparent. Multi-hop communication protocols allow sensors to cooperate to reliably deliver data between nodes outside of direct communication range. However, application specific requirements, such as high sampling rates, vast amounts of data to be collected, precise internodal synchronization, and reliable communication, are quite challenging to achieve with generic multi-hop communication protocols. This paper proposes two complementary reliable multi-hop communication solutions for monitoring of civil infrastructure. In the first approach, termed herein General Purpose Multi-hop (GPMH), the wide variety of communication patterns involved in structural health monitoring, particularly in decentralized implementations, are acknowledged to develop a flexible and adaptable any-to-any communication protocol. In the second approach, termed herein Single-Sink Multi-hop (SSMH), an efficient many-to-one protocol utilizing all available RF channels is designed to minimize the time required to collect the large amounts of data generated by dense arrays of sensor nodes. Both protocols adopt the Ad-hoc On-demand Distance Vector (AODV) routing protocol, which provides any-to-any routing and multi-cast capability, and supports a broad range of communication patterns. The proposed implementations refine the routing metric by considering the stability of links, exclude functionality unnecessary in mostly-static WSSNs, and integrate a reliable communication layer with the AODV protocol. These customizations have resulted in robust realizations of multi-hop reliable communication that meet the demands of structural health monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.232-241
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• 2019

This study examines one of the conventional and controversial issues in modern finance. Specifically, this study identifies financial determinants of corporate R&D intensity for firms belonging to Korean Chaebols. Empirical estimation procedures are applied to derive more robust results of each hypothesis test. Static panel data, Tobit regression and stepwise regression models are employed to obtain significant financial factors of R&D expenditures, while logit, probit and complementary log-log regression models are used to detect financial differences between Chaebol firms and their counterparts not classified as Chaebols. Study results found the level of R&D intensity in the prior fiscal year, market-value based leverage ratio and firm size empirically showed their significance to account for corporate R&D intensity in the first hypothesis test, whereas the majority of explanatory variables had important power on a relative basis. Assuming that the current circumstances in the domestic capital market may necessitate gradual changes of Korean Chaebols in terms of their socio-economic function, the results of this study are expected to contribute to identifying financial antecedents that can be beneficial to attain optimal level of corporate R&D expenditures for Chaebol firms on a virtuous cycle.

• Applied Chemistry for Engineering
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• v.30 no.4
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• pp.435-444
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• 2019

In this study, two types of nonionic saccharide biosurfactants, GP-6 and GP-7, were prepared from coconut oil and the structure of resulting products was investigated by FT-IR, $^1H-NMR$ and $^{13}C-NMR$ spectrophotometer. The interfacial properties of GP-6 and GP-7 were found to be excellent from interfacial property measurements such as critical micelle concentration, static and dynamic surface tensions, interfacial tension, emulsification power, wetting property and foam stability. Detergency test evaluated by using a Terg-o-tometer showed moderately good detergency compared to that of conventional surfactants used in detergent formulations. Biodegradability, acute oral toxicity, acute dermal irritation and acute eye irritation tests revealed that both surfactants possess excellent mildness and superior environmental compatibility indicating the potential applicability to detergent products formulations. In particular, GP-6 can be considered as a strong candidate in detergent formulations since it is more surface active, mild and readily biodegradable than GP-7.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.172-178
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• 2019

Although the needs for providing resilient PNT information are increasing, threats due to the intentional RFI or space weather change are challenging to resolve. eLoran, which is a terrestrial navigation system that use a high-power signal is considered as a best back-up navigation system. Depending on the user's environment in the eLoran system, the user may use one of E-field or H-field antennas. H-field antenna, which has no restriction on setting stable ground and is relatively resistant to noise of general electronic equipment, is composed of two loops, and shows anisotropic gain pattern due to the different measurement at the two loops. Therefore, the H-field antenna's phase estimation value of signal varies depending on its direction even at the static environment. The error due to the direction of the signal should be eliminated if the user want to estimate the own position more precisely. In this paper, a method to compensate the error according to the geometric distribution between the H-field antenna and the transmitting station is proposed. A model was developed to compensate the directional error of H-field antenna based on the signal generated from the eLoran signal simulator. The model is then used to the survey measurement performed in the land area and verify its performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.352-362
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• 2019

The study investigates one of the long-standing, but still controversial issues in modern finance from the international and domestic perspectives. That is, financial components and differences on corporate profitability are identified and compared under the primary hypotheses. Empirical research settings include the sample data as KOSPI-listed chaebol firms, time reference covering the post-era of the global financial turmoil and two differently defined profitability indices measured by the market- and the book-value bases. A majority of total 7 explanatory variables except firm size and leverage ratio reveal their statistically significant power to explain profitability indices for the chaebol firms in the first hypothesis. The results are generally compatible with those obtained from their counterparts of non-chaebol firms. In the second hypothesis applying multinomial logistic model, the chaebol firms are classified into three groups according to the level of profitability. It is then confirmed that variables to represent the market-valued debt ratio, business risk and growth potential are financially discriminating factors among the three groups. The study may provide a new vision to identify financial factors of corporate profitability for Korean chaebol firms after the global financial crisis, which can enhance the benefits of interested parties at the government or corporate level in a virtuous cycle.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.485-492
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• 2021

The wind turbines with a rated capacity of 50kW or less are generally considered as small class. Small wind turbines are an attractive alternative for off-grid power system and electric home appliances, both as stand-alone application and in combination with other energy technologies such as energy storage system, photovoltaic, small hydro or diesel engines. The research objective is to develop the 50kW scale wind turbine blades in ways that resemble as closely as possible with the construction and methods of utility scale turbine blade manufacturing. The mold process based on wooden form is employed to create a hollow, multi-piece, lightweight design using carbon fiber and fiberglass with an epoxy based resin. A hand layup prototyping method is developed using high density foam molds that allows short cycle time between design iterations of aerodynamic platforms. A production process of five blades is manufactured and key components of the blade are tested by IEC 61400-23 to verify the appropriateness of the design. Also, wind system with developed blades is tested by IEC 61400-12 to verify the performance characteristics. The results of blade and turbine system test showed the available design conditions for commercial operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.160-168
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• 2021

This study attempts to identify how incivility and clinical learning environment affects clinical practice stress among nursing students. This study enrolled 204 nursing students in the third- and fourth-grades, having practical clinical experience. Data were collected from April 1 to April 30, 2019, by the self-reported method. Data analysis was achieved by applying descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and multiple regression analysis, using the SPSS 22.0 program. Our results showed that the average values for incivility experienced by nursing students was 2.58, clinical learning environment was 2.79, and clinical practice stress was 3.33. Incivility experienced and clinical practice stress appeared to be a static correlation, whereas the clinical learning environment and clinical practice stress, and incivility experienced and clinical learning environment, were identified as amulet correlations. Incivility experienced and clinical learning environment exerted significant influences on clinical practice stress, where the explanatory power was confirmed at 22.5%. Based on the results of this study, we propose the necessity to develop and apply various programs to effectively manage the clinical practice stress of nursing students.

• Journal of Korean Ophthalmic Optics Society
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• v.21 no.3
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• pp.259-264
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• 2016

Purpose: To establish the cause of decrease in body stability and to analyze the effects on sensory organs maintaining static balance according to the induced astigmatic blur. Methods: Twenty subjects (10 males, 10 females; mean age, $23.40{\pm}2.70years$) were participated in this study. To induce myopic simple astigmatism, the axis directions of cylindrical lenses were placed $180^{\circ}$ on both eyes (with-the-rule), $90^{\circ}$ on both eyes (against-the-rule), and $45^{\circ}$ on both eyes (oblique). Cylindrical lenses of +0.50, +1.00, +1.50, +2.00, +3.00, +4.00, and +5.00 D were used to increase astigmatic blur in each astigmatism types. General stability (ST) and sway power (SP) in frequencies by each sensory organs were analyzed using the TETRAX biofeedback system. Results: ST in the all astigmatism types were raised with increase of astigmatic blur compared to full corrected condition, but a significant difference only showed in the induced oblique astigmatism. According to the results of correlation analysis between ST and SP in the each frequencies with increase of astigmatic blur, the causes of increased ST in the induced oblique astigmatism showed to have a high correlation in order of somatosensory system (high-medium frequency), central nervous system (high frequency), peripheral vestibular system (low-medium frequency), and visual system (low frequency). Conclusions: The visual information by uncorrected oblique astigmatism may disturb the normal functions of all sensory organs maintaining body balance, consequently, the body stability can be reduced. Therefore, optimal correction of astigmatism can play an important role for reducing the instability of body balance.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.63-76
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• 2018

The purpose of this study was to investigate the effect of contact type textile electrode structure on heart activity signal acquisition for smart healthcare. In this study, we devised six contact type textile electrodes whose electrode size and configuration were manipulated for measuring heart activity signals using computerized embroidery. We detected heart activity signals using a modified lead II and by attaching each textile electrode to the chest band in four healthy male subjects in a standing static posture. We measured the signals four times repeatedly for all types of electrodes. The heart activity signals were sampled at 1 kHz using a BIOPAC ECG100, and the detected original signals were filtered through a band-pass filter. To compare the performance of heart activity signal acquisition among the different structures of the textile electrodes, we conducted a qualitative analysis using signal waveform and size as parameters. In addition, we performed a quantitative analysis by calculating signal power ratio (SPR) of the heart activity signals obtained through each electrode. We analyzed differences in the performance of heart activity signal acquisition of the six electrodes by performing difference and post-hoc tests using nonparametric statistic methods on the calculated SPR. The results showed a significant difference both in terms of qualitative and quantitative aspects of heart activity signals among the tested contact type textile electrodes. Regarding the configurations of the contact type textile electrodes, the three-dimensionally inflated electrode (3DIE) was found to obtain better quality signals than the flat electrode. However, regarding the electrode size, no significant difference was found in performance of heart signal acquisition for the three electrode sizes. These results suggest that the configuration method (flat/3DIE), which is one of the two requirements of a contact type textile electrode structure for heart activity signal acquisition, has a critical effect on the performance of heart activity signal acquisition for wearable healthcare. Based on the results of this study, we plan to develop a smart clothing technology that can monitor high-quality heart activity without time and space constraints by implementing a clothing platform integrated with the textile electrode and developing a performance improvement plan.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.