• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.204-217
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• 2018

This study proposes a cost-effective method to monitor coastal environments using e-Navigation-implemented domestic and international ferries, and to analyze big data of records such as wind, temperature, salinity, waves, and currents that are gathered through e-Navigation system. First, we present the concept and architecture of e-Navigation operation system based on the General Information Center on Maritime Safety and Security. Then, the marine observation system that can be applied to ferries operating in our nation's territory is discussed. Analytical methods, such as spatio-temporal mixed effects model, ensemble method, and meshfree method, in handling real-time big data obtained by the e-Navigation observing system are then explained in detail. This study will support the implementation of the Korean e-Navigation project that focuses on the safety of small vessels such as coasters and fishing vessels.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.78-84
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• 2019

The purpose of this study is to introduce the concept of the Smart Sewer System and to analyze the economic ripple effect when smart sewer is built all over Korea. The research method is the input-output model based on the assumption that the smart sewerage will be constructed throughout the Korea from 2021 to 2040. Estimation results show that the production-induced effect reaches 343.73 trillion Korean won, the added value-induced effect is 155.867 trillion Korean won, and the employment-induced effect is estimated by 25,118,470, indicating that the smart sewer project leads to being considerably large in the nation-wide economy. In addition, the increase of social welfare by smart sewer is expected to be realized through the improvement of both the environment improvement and the national health. Therefore, the smart sewer project should be implemented without delay by planning a concrete road map and putting it into effect with a budget.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.265-272
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• 2019

In this paper, a vision-based deep learning algorithm and image processing method are proposed to detect bolt-loosening in steel connections. To achieve this objective, the following approaches are implemented. First, a bolt-loosening detection method that includes regional convolutional neural network(RCNN)-based deep learning algorithm and Hough line transform(HLT)-based image processing algorithm are designed. The RCNN-based deep learning algorithm is developed to identify and crop bolts in a connection image. The HLT-based image processing algorithm is designed to estimate the bolt angles from the cropped bolt images. Then, the proposed vision-based method is evaluated for verifying bolt-loosening detection in a lab-scale girder connection. The accuracy of the RCNN-based bolt detector and HLT-based bolt angle estimator are examined with respect to various perspective distortions.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.593-605
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• 2019

Significant literature exists on the area under the ROC curve (AUC) and the volume under the ROC surface (VUS) which are statistical measures of the discriminant power of classification models. Whereas the partial AUC is restricted on the false positive rate, the two-way partial AUC is restricted on both the false positive rate and true positive rate, which could be more efficient and accurate than partial AUC. The two-way partial AUC was suggested as more efficient and accurate than the partial AUC. Partial VUS as well as the three-way partial VUS were also developed for the ROC surface. A proposed AUC is expressed in this paper with probability and integration using two truncated distribution functions restricted on both the false positive rate and true positive rate. It is also found that this AUC has a relation with the two-way partial AUC. The three-way partial VUS for the ROC surface is also related to the VUS using truncated distribution functions. These AUC and VUS are represented and estimated in terms of Mann-Whitney statistics. Their parametric and non-parametric estimation methods are explored based on normal distributions and random samples.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.149-158
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• 2019

In this study, the concept of emotional labor and AI(appreciative inquiry) are connected to examine how acceptance of AI, which is related to changes in hospital organizational culture, affects nurses' emotional labor, organizational commitment, and job satisfaction. For this purpose, the data collected from 156 nurses working at a major hospital were analyzed using structural equation modeling. The findings of this study are as follows: First, nurses' acceptance of AI had a significant positive effect on deep acting but not on surface acting. Second, deep acting had a significant positive effect on organizational commitment, but surface acting did not. Third, nurses' organizational commitment had a significant positive effect on job satisfaction. Fourth, deep acting significantly mediated the relationship between nurses' acceptance of AI and organizational commitment, but surface acting did not. Fifth, deep acting and organizational commitment significantly mediated the relationship between nurses' acceptance of AI and job satisfaction, but surface acting and organizational commitment did not. Theoretical and practical implications are provided based on the relationships between the variables found in this study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.369-380
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• 2019

When a extreme loading such as blast is applied to prestressed concrete (PSC) structures and infrastructures for an instantaneous time, serious property damages and human casualties occur. However, a existing design procedure for PSC structures such as prestressed containment vessel (PCCV) and gas storage tank do not consider a protective design for extreme internal blast scenario. Particularly, an internal blast is much more dangerous than that of external blast. Therefore, verification of the internal blast loading is required. In this paper, the internal blast resistance capacity of PSC member is evaluated by performing internal blast tests on RC and bi-directional PSC scaled down specimens. The applied internal blast loads were 22.68, 27.22, and 31.75 kg (50, 60, and 70 lbs) ANFO explosive charge at 1,000 mm standoff distance. The data acquisitions include blast pressure, deflection, strain, crack patterns, and prestressing force. The test results showed that it is possible to predict the damage area to the structure when internal blast loading occurs in PCCV structures.

• Journal of Industrial Convergence
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• v.16 no.2
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• pp.1-14
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• 2018

The interconnectedness of all things is continuously expanding. For example, bluetooth low energy (BLE) beacons are wireless radio transmitters that can send an identifier to nearby receivers and trigger a number of applications, from proximity marketing to indoor location-based service. iBeacon technology which is one of the newest technologies in the smart tourism field, is reckoned as being very useful for travelers in enhancing the experience with visiting places. However, there is consequently not much existing research yet about the connection between iBeacon technology and tourism destination. Considering that, this study analyzes the adoption of iBeacon in tourism destination, this study examine the interrelationships and feedback structures of key factors in iBeacon adoption. To serve the purpose, this study used system dynamics approach to develop a model of iBeacon adoption in tourism destination. The analysis results showed that the concept of 'Social Influences' is one of the significant predictors for individual's intention behavior to accept iBeacon, and word of mouth (WOM), subjective norm, privacy concern, and perceived usefulness are key factors influencing the iBeacon adoption.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.568-580
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• 2020

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.

• Environmental and Resource Economics Review
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• v.29 no.4
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• pp.419-442
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• 2020

The majority of the natural gas demand in South Korea is mainly determined by the heating demand. Accordingly, there is a distinct seasonality in which the gas demand increases in winter and decreases in summer. Moreover, the degree of sensitiveness to temperature on gas demand has changed over time. This study firstly introduces changing temperature response function (TRF) to capture effects of changing seasonality. The temperature effect (TE), estimated by integrating temperature response function with daily temperature density, represents for the amount of gas demand change due to variation of temperature distribution. Also, this study presents an innovative way in forecasting daily temperature density by employing functional principal component analysis based on daily max/min temperature forecasts for the five big cities in Korea. The forecast errors of the temperature density and gas demand are decreased by 50% and 80% respectively if we use the proposed forecasted density rather than the average daily temperature density.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.494-504
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• 2021

The spread of Over-The-Top (OTT) service, which represents Netflix, and the social distancing caused by COVID-19, acted as an overall bad news for domestic multiplex movie theaters. In addition to this, the phenomenon of digital shifting was added, and the need for domestic offline movie theaters to seek a new market for growth emerged. This study focused on the concept of consumer value perception amid this problem consciousness, and attempted to investigate the relationship between the brand image of multiplex movie theaters and purchase satisfaction according to the type of consumer value perception. After data was sampled through a questionnaire survey to a total of 350 subjects, the results of empirical analysis according to the study model are as follows. Among the types of value perception of offline movie viewers, practicality had the strongest influence on brand image construction, and self-faithfulness had the strongest influence on purchase satisfaction of offline movie watching. In addition, the brand image of offline movie theaters had a positive(+) effect on the purchase satisfaction of moviegoers. Based on this, this study suggested a new survival strategy in the new normal era of offline Multiplex Cinemas.