• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.649-651
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• 2021

In Korea, there are around 30,000 cardiac arrest patients annually. Gradually the number is increasing. Against this background, CPR education and publicity programs were expanded nationwide, but the rate of witness CPR by the general public was 4.4%, which is significantly lower than the 20%~70% rate in other countries. Therefore, in this paper, we analyzed the factors affecting the performance of CPR by witnesses who discovered cardiac arrest patients. Based on the results, an application planning and development study was conducted to provide users with correct cardiorespiratory response tips and step-by-step CPR guidelines to help users effectively assist in increasing the rate of CPR by general eyewitnesses.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.385-390
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• 2023

The purpose of this study is to identify the antecedents and consequences of perceived fairness of assessment in an online class, such as Google classroom. Based on an extensive literature review, a research model and research questions were designed. We found that the perceived interactivity of contents and social presence were found to be antecedents of perceived fairness of assessment. Further, perceived fairness of assessment was found to have a substantial influence on class satisfaction and achievement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.704-706
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• 2022

In this study, we analyzed the correlation between the amount of photovoltaic power generation and the factors of meteorological changes. A total of 52,561 data were used in the correlation analysis from January 2018 to January 2020, and the variables used in the correlation analysis were time, horizontal plane scattering solar radiation, direct solar radiation, wind velocity, and relative humidity. The temperature was used. Based on this data, we used the Google Colab platform to analyze the correlation, and the analysis revealed whether there was a correlation between solar power and meteorological change factors.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.419-420
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• 2023

최근 인공지능 기술과 로봇 기술의 발전으로 인해 비서 로봇을 만드는 기술적인 가능성이 커지면서 업무 자동화를 위해 많은 기업에서 도입하고 있다. 특히 인구 고령화가 진행되면서 노동력 부족이 심각한 문제로 대두되고 있다. 현재 비서로봇은 정형화된 대화는 잘 처리하지만 비정형화된 대화에 대해서는 한계가 있다. 본 논문은 앞선 문제를 해결하기 위해 비정형화된 대화도 가능하면서 사용자가 원하는 행동을 실행할 수 있는 보편화된 비서 로봇을 선보인다. 음성인식 모듈과 구글 어시스턴트를 활용하여 마이크를 통해 비서 로봇에게 스케줄 관리, 날씨 등을 질문하고, 스피커를 통해 대답을 듣는 등 비정형화된 의사소통을 할 수 있으며, 비서 로봇에게 원하는 행동을 지시하여 행동을 구현시킬 수 있는 비서로봇을 제안한다.

• Journal of information and communication convergence engineering
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• v.22 no.3
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• pp.221-230
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• 2024

The aim of this study is to investigate the automatic recognition and analysis of Jeju marine-life images using artificial intelligence (AI) technology. The dataset of marine-life images was prepared using tools such as Python, TensorFlow, and Google Colab (Google Colaboratory). We also developed models by training deep learning AI in image recognition to automatically recognize the species found in these images and extract their associated information, such as taxonomy, characteristics, and distribution. This study is innovative in that it uses deep learning technology combined with imagerecognition technology for marine biodiversity research. In addition, these results will lead to the development of the marine-life industry in Jeju by supporting marine environment monitoring and marine resource conservation. Furthermore, this study is anticipated to contribute to academic advancement, specifically in the study of marine species diversity.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.4
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• pp.85-99
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• 2024

The study examined perception of library personnel towards social media utilization for information service delivery in kwara state university library. It adopted the descriptive survey design with population consisting of 27 professional librarians in three selected university libraries in kwara State. Total enumerative sampling techniques were used because the population of the study is manageable by the researcher. Percentage and mean were used in analyzing the data. Primary data solicited would be cleaned, coded and entered into the Statistical Package for Social Sciences (SPSS) version 20 software for quantitative analysis. The findings of the study revealed the services rendered by library professional in university libraries in Kwara State, are Current Awareness Services (CAS), Photocopying services, Technical Services, Circulation/borrowing services, Internet services, and Bibliographic verification services. Majority of the respondent indicated Myspace, Google plus, Flickr, Telegram, Twitter, WhatsApp and Facebook are the social media platforms used for service delivery by librarians. Also, majority of the respondents in university libraries indicated Google plus, LinkedIn, Myspace, Skype, WhatsApp, Flickr, WeChat, Twitter and Facebook as social media space use for information service delivery effectively and Bandwidth problem, Limited fund, Lack of maintenance culture, Lack of Awareness, Copyright Issue, Technophobia are the major challenges inhibiting the use of social media for service delivery by the library staff. Lastly, the study concluded with recommendations.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.363-375
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• 2024

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu'sthresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes,shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1109-1133
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• 2013

In an effort to discover new technologies and to forecast social changes of technologies, a number of technology life-cycle models have been developed and employed. The hype cycle, a graphical tool developed by a consulting firm, Gartner, is one of the most widely used models for the purpose and it is recognised as a practical one. However, more research is needed on theoretical frames, relations and empirical practices of the model. In this study, hype cycle comparisons in Korean and global search websites were performed by means of web-search traffic which is proposed as an empirical measurement of public expectation, analysed in a specific product or country in previous researches. First, search traffic and market share for new cars were compared in Korea and the U.S. with a view to identifying differences between the hype cycles in the two countries about the same product. The results show the similarity between the two countries with the statistical significance. Next, comparative analysis between search traffic and supply rate for several products in Korea was conducted to check out their patterns. According to the analysis, all the products seem to be at the "Peak of inflated expectations" in the hype cycles and they are similar to one another in the hype cycle. This study is of significance in aspects of expanding the scope of hype cycle analysis with web-search traffic because it introduced domestic web-search traffic analysis from Naver to analyse consumers' expectations in Korea by comparison with that from Google in other countries. In addition, this research can help to explain social phenomina more persuasively with search traffic and to give scientific objectivity to the hype cycle model. Furthermore, it can contribute to developing strategies of companies, such as marketing strategy.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.147-157
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• 2018

Since Google launched a prediction service for influenza-like illness(ILI), studies on ILI prediction based on web search data have proliferated worldwide. In this regard, this study aims to build short-term predictive models for ILI in Korea using ILI and web search data and measure the performance of the said models. In these proposed ILI predictive models specific to Korea, ILI surveillance data of Korea CDC and Korean web search data of Google and Naver were used along with the ARIMA model. Model 1 used only ILI data. Models 2 and 3 added Google and Naver search data to the data of Model 1, respectively. Model 4 included a common query used in Models 2 and 3 in addition to the data used in Model 1. In the training period, the goodness of fit of all predictive models was higher than 95% ($R^2$). In predictive periods 1 and 2, Model 1 yielded the best predictions (99.98% and 96.94%, respectively). Models 3(a), 4(b), and 4(c) achieved stable predictability higher than 90% in all predictive periods, but their performances were not better than that of Model 1. The proposed models that yielded accurate and stable predictions can be applied to early warning systems for the influenza pandemic in Korea, with supplementary studies on improving their performance.