• Journal of Digital Convergence
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• v.20 no.5
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• pp.513-523
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• 2022

Generally, people act to use Information Technology on the premise of their experience in using various Information Technology. Namely, users perceive Information Technology through their User Experience, and these User Experiences can be said to impact Continuous Use Intention of the Information Technology. Based on this condition, the present study was proceeded to investigate the User Satisfaction's Mediating effect in the relationship between User Experience and Continuous Use Intention of the OTT services. For this purpose, 195 male and female college students' survey data were used for analysis, verified through the SPSS Process Macro. As a result of the analysis, not only the Reliability experience for the OTT services but also the Usability, Convenience, and Pleasurable experiences' User Experiences were verified that Use Satisfaction was Fully Mediating in relation to Continuous Use Intention. This result gives an implication that the importance of User Experience and Use Satisfaction in maintaining customers through continuous use of OTT services are worth noting. Overall, the present study increased the customer utility for OTT services and confirmed the importance of User Experience and Use Satisfaction in order to increase the competitiveness of the company. Also, it proposed a direction for an in-depth future research to develop this study.

• Korean Journal of Culture and Social Issue
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• v.28 no.2
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• pp.163-186
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• 2022

The study was conducted to reveal the influence of variables causing post-traumatic growth and suggest ways to utilize it in the counseling and clinical field. Data from 208 youths in Korea were collected and analyzed using the SPSS 25.0 and AMOS 26,0 statistic programs. This study took the perspective that post-traumatic growth was affected by the balanced- time-perspective and social-connectedness and tried to examine the influence and relation of the two variables. A frequency-analysis was performed to identify the demographic characteristics and the trends of the variables, and a Pearson's -correlation analysis was conducted to examine the relations between variables. A hierarchical- regression- analysis was performed to examine the influence of the major variables, And path-analysis was carried out to verify the research model, and the indirect effect was confirmed by using the bootstrapping method. First, religion, among all demographic variables, showed a significant effect on the post-traumatic growth. Second, the balanced-time-perspective and social-connectedness had a significant effect on post-traumatic growth. Third, the balanced time perspective influenced social-connectedness and through this process, the path explaining how post traumatic growth occurs was revealed. This suggests that the both balanced time perspective and social connectedness are important for inducing post- traumatic growth, and the utility of the variables in the counseling and clinical field.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.204-226
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• 2023

One of the most important types of Customer Empowerment Strategy (CES) is select empowerment, where firms allow customers to vote on a product to be marketed. However, there is limited research on the advantages and disadvantages of select empowerment. In particular, there are few studies on the composition of a voting system. This study analyzes customer participation behavior, such as willingness to vote and strategic voting (i.e., voting for candidates not based on utility orders), under the different voting systems: 1) the number of votes per customer (single or multiple), and 2) the number of final choices (single or multiple). Uncertainty is proposed as a mediator that links the voting system difference and customer participation. Two research hypotheses are tested using multiple linear regression analysis and a natural effects model based on data from two online experiments. As a result, the multiple voting system (i.e., multiple winners are selected by customer votes) shows a direct positive effect on willingness to vote and strategic voting behavior. In addition, the result shows that uncertainty insignificantly mediates the relationship between the voting system and customer participation. Academic and managerial contributions are discussed with several future research directions.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.69-74
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• 2023

Smart museums based on the growth of online exhibition can be seen as in line with the movement of the 4th Industrial Revolution. By combining art and technologies, they enable viewers to experience culture and art. This study examined the cases of the Dunhuang Museum, the Palace Museum, and the China Arts and Crafts Master Museum to assess or identify how China is leading by accepting the technology of the fourth industry and applying the technology. In common, Chinese smart museums are widely used for collecting enviromental data, establishing integrated digital applications, and preserving collections, services, management, and exhibitions through VR, and AR. Through the case of the Chinese Smart Museum, this study identified the online exhibition as a space that exists in another dimension rather than an image replica with excellent operational utility. Therefore, online exhibitions are the best medium to expand the space, and viewers can explorethe museum's exhibition room and engage with all the contents of the museum without visiting the museum in person. Through the online exhibition of smart museums, visitors and viewers can be transformed into more active cultural consumers and develop collective capabilities.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.411-416
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• 2023

The purpose of this study is to investigate the utilization of major learning tools among radiology science students and assess the accuracy of a conversational artificial intelligence service program, specifically a chatbot, in the context of the national radiologic technologist licensing exam. The survey revealed that 84.3% of radiology science students actively utilize electronic devices during their learning process. In addition, 104 out of 140 respondents said they use search engines as a top priority for efficient data collection while studying. When asked about their awareness of chatbots, 80% of participants responded affirmatively, and 22.9% reported having used chatbots for academic purposes at least once. From 2018 to 2022, exam questions from the first and second periods were presented to the chatbot for answers. The results showed that ChatGPT's accuracy in answering first period questions increased from 48.28% to 60%, while for second period questions, it increased from 50% to 62.22%. Bing's accuracy in answering first period questions improved from 55% to 64.55%, and for second period questions, it increased from 48% to 52.22%. The study confirmed the general trend of radiology science students utilizing electronic devices for learning and obtaining information through the internet. However, conversational artificial intelligence service programs in the field of radiation science face challenges related to accuracy and reliability, and providing perfect solutions remains difficult, highlighting the need for continuous development and improvement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1467-1475
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• 2023

The public-interest direct payment program involves providing direct payments to agricultural producers and rural residents through public funds, premised on performing public functions such as environmental conservation, stable food supply, and maintaining rural communities via agricultural activities. Scientific estimation of crop cultivation areas and production levels is crucial for formulating agricultural policies linked to regulating food supply, which increasingly impacts the national economy. Conducting comprehensive on-site inspections for compliance monitoring of direct payment programs has shown very low efficiency in relation to budget and time. The expansion of areas subject to compliance monitoring and various challenges in on-site inspections necessitate streamlining current monitoring methods and devising effective strategies. As a solution, the application of Remote Sensing technology and spatial information utilization, allowing swift acquisition of necessary information for policies without overall on-site visits, is being discussed as an efficient compliance monitoring method. Therefore, this study evaluated the potential use of remote sensing for improving operational efficiency in monitoring compliance with public-interest direct payment programs. Using satellite images during farming seasons in Gimje and Hapcheon, vegetation indices and spatial variations were utilized to identify cultivated areas, presence of mixed crops, validated against on-site inspection data.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1007-1014
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• 2023

Two-dimensional flow analysis, a fundamental component of hydrodynamics, plays a pivotal role in numerically simulating fluid behavior in rivers and waterways. This modeling approach heavily relies on parameters such as eddy viscosity and roughness coefficient to accurately represent flow characteristics. Therefore, combination of appropriate parameters is very important to accurately simulate flow characteristics. In this study, an automation algorithm was developed and applied to find the optimal combination of parameters. Previously, when applying a two-dimensional flow analysis model, former researchers usually depend on the empirical approach, which causes many difficulties in finding optimal variable values. Using the experimental data, we tracked errors according to the combination of various parameters and applied the algorithm that can determine the optimal combination of parameters with the Python language. The automation algorithm can easily determine the most accurate combination by comparing the flow velocity error values among the two-dimensional flow analysis results among the combinations of 121 (11×11) parameters. In the perspective of utilizing automation algorithm, there is an expected high utility in promptly and straightforwardly determining the optimal combination of parameters with the smallest error.

• Journal of Cardiovascular Imaging
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• v.31 no.1
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• pp.18-23
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• 2023

BACKGROUND: Three-dimensional (3D) transesophageal echocardiogram (TEE) is the gold standard for the diagnosis of degenerative mitral regurgitation (dMR) and preoperative planning for transcatheter mitral valve repair (TMVr). TEE is an invasive modality requiring anesthesia and esophageal intubation. The severe acute respiratory syndrome coronavirus 2 pandemic has limited the number of elective invasive procedures. Multi-detector computed tomographic angiography (MDCT) provides high-resolution images and 3D reconstructions to assess complex mitral anatomy. We hypothesized that MDCT would reveal similar information to TEE relevant to TMVr, thus deferring the need for a preoperative TEE in certain situations like during a pandemic. METHODS: We retrospectively analyzed data on patients who underwent or were evaluated for TMVr for dMR with preoperative MDCT and TEE between 2017 and 2019. Two TEE and 2 MDCT readers, blinded to patient outcome, analyzed: leaflet pathology (flail, degenerative, mixed), leaflet location, mitral valve area (MVA), flail width/gap, anterior-posterior (AP) and commissural diameters, posterior leaflet length, leaflet thickness, presence of mitral valve cleft and degree of mitral annular calcification (MAC). RESULTS: A total of 22 (out of 87) patients had preoperative MDCT. MDCT correctly identified the leaflet pathology in 77% (17/22), flail leaflet in 91% (10/11), MAC degree in 91% (10/11) and the dysfunctional leaflet location in 95% (21/22) of patients. There were no differences in the measurements for MVA, flail width, commissural or AP diameter, posterior leaflet length, and leaflet thickness. MDCT overestimated the measurements of flail gap. CONCLUSIONS: For preoperative TMVr planning, MDCT provided similar measurements to TEE in our study.