• Journal of Environmental Science International
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• v.30 no.12
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• pp.1101-1111
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• 2021

In this study, among the W-S-R(Wiper-Signal-Rainfall) relationship methods used to produce sensor-based rain information in real time, we sought to produce actual rainfall information by applying machine learning techniques to account for the effects of wiper operation. To this end, we used the gradient descent and threshold map methods for pre-processing the cumulative value of the difference before and after wiper operation by utilizing four sensitive channels for optical sensors which collected rain sensor data produced by five rain conditions in indoor artificial rainfall experiments. These methods produced rainfall information by calculating the average value of the threshold according to the rainfall conditions and channels, creating a threshold map corresponding to the 4 (channel) × 5 (considering rainfall information) grid and applying Optima Rainfall Intensity among the big data processing techniques. To verify these proposed results, the application was evaluated by comparing rainfall observations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.139-146
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• 2022

Recently, solving industrial and social problems and creating new values based on big data and AI is being discussed as the main policy goal. The new government also set the digital platform government as a national task in order to achieve new value creation based on big data and AI. However, studies that summarize and diagnose discussions over the past five years are insufficient. Therefore, this study diagnoses the discussions over the past 5 years using the 4th industrial revolution as a keyword. After collecting news editorials from 2017 to 2022 by applying the text mining technique, 9 major topics were discovered. In conclusion, this study provided implications for the government's task to prepare for the future society.

• Journal of Korea Entertainment Industry Association
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• v.14 no.5
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• pp.1-14
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• 2020

This study conducted a topic modeling and semantic network analysis of 'korean wave' and its meaning in Korean society from 2000 to 2019 by applying an agenda setting theory. For this purpose, a total of 197,992 newspaper articles which reported 'korean wave' issues were analyzed by applying topic modeling and semantic network analysis. As a result, first, the word 'korean wave' mainly appeared in korean-related regions in the korean press. culture and economy. second, a total of 9 topics related to korean wave issues appeared. This was followed by 'broadcast', 'export', 'domestic and foreign affairs', 'education', 'beauty and fashion', 'music and performance', 'tourism', 'media(platform)', and 'region'. Lastly, korean wave was mainly discussed at the cultural and economic ares. In addition, it was clustered into five characteristics: 'cultural hallyu', 'business hallyu', 'education', 'environment', and 'geography'.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.177-182
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• 2023

Korea is currently an aging society with a population of about 15 percent over the age of 65. Accordingly, the government is currently working on a number of measures. However, the problem that is rapidly increasing rather than decreasing is the traffic accident of the elderly. It has increased so much that we can check it out in multiple media right away. An average of 110 elderly people die or are injured in traffic accidents a day, or about 40,000 a year. The National Police Agency reported a 25 percent increase in elderly traffic accidents from five years ago. This paper analyzes traffic accidents of senior citizens through the Big Data analysis and R programming language to present the main causes of traffic accidents of senior citizens and areas where measures are needed to prevent them.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.95-103
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• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.17-29
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• 2024

The Gyeongin Ara Waterway is a regional development project designed to transport cargo by sea and to utilize the surrounding waterfront area to enjoy tourism and leisure. It is being used as a space for demonstration projects for urban air transportation (UAM), which has recently been attracting attention, and various efforts are being made at the local level to strengthen cultural and tourism functions and revitalize local food. This study examined the perception and trends of tourism consumers on the Gyeongin Ara Waterway before and after the outbreak of COVID-19. The research method utilized semantic network analysis based on social network analysis. As a result of the study, first, before the outbreak of COVID-19, key words such as bicycle, Han River, riding, Gimpo, Seoul, hotel, cruise ship, Korea Water Resources Corporation, emotion, West Sea, weekend, and travel showed a high frequency of appearance. After the outbreak of COVID-19, keywords such as cafe, discovery, women, Gimpo, restaurant, bakery, observatory, La Mer, and cruise ship showed a high frequency of appearance. Second, the results of the degree centrality analysis showed that before the outbreak of COVID-19, there was increased interest in accommodations for tourism, such as Marina Bay and hotels. After the outbreak of COVID-19, interest in food such as specific bakeries and cafes such as La Mer was found to be high. Third, due to the CONCOR analysis, five keyword clusters were formed before the outbreak of COVID-19, and the number of keyword clusters increased to eight after the outbreak of COVID-19.

• Korean Journal of Culture and Social Issue
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• v.10 no.1
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• pp.1-30
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• 2004

Study 1 composed of three experiments examined differences in person perception between computer-mediated (or CMC) and face-to-face (or FTF) communications. In Experiment 1, each of 17 groups of 4-5 discussed a donation entrance system through CMC and a week later a college-based university system through FTF communication mode for half an hour. Then members of a given group rated each other along with self-ratings on five personality traits. Results indicated that in FTF than CMC condition, ratings of others were more positive and also self-peer agreement and meta-accuracy were larger in spite of no large difference in inter-judge agreement between two communication modes. In Experiment 2, 17 groups of 4 in each of the CMC and FTF condition discussed a college-based university system for an hour. Then group members rated each other on another five trait dimensions. Although ratings of others were more positive in FTF than CMC condition, there no systematic differences in two types of agreement and meta-accuracy between the two communication modes. In Experiment 3, 17 groups of 4 in each of the CMC and FTF condition discussed a donation entrance system for an hour and then group members rated each other on five trait dimensions different from those used in Experiment 1 and 2. The findings replicated Experiment 1. Study 2 examined gender differences in person perception in CMC. Fifteen dyads for each of the man-man, man-woman, and man-woman conditions communicated for an half hour in CMC and then rated each other along with self ratings on 25 personality trait dimensions. Results indicated that participants rated their partners more negatively for extorversion, agreeableness and culture factors, which was due mainly to woman's negative evaluations for their male partners. Also, self-peer agreement was the largest in the man-man communication condition. These findings were discussed in relation to differences between CMC and FTF communication modes.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.819-829
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• 2020

This empirical research is aimed at testing the relationship of the big five personality traits namely openness to experience, extraversion, consciousness, agreeableness, neuroticism, and risk aversion with the investment intention of individual investors belonging to Balochistan, Pakistan. The primary data is collected through a self-administered questionnaire (a structured form that consists of a series of closed-ended and open-ended questions) from a sample of 397 active individual investors belonging to different districts of the province. The data is empirically analyzed by applying the Partial Least Square (PLS) path modeling technique by using the estimation package available in Smart-PLS. The findings of this study suggest that all the variables are statistically significant with investors' investment intention with risk aversion as the strongest predictor. Moreover, openness to experience, extraversion, consciousness, agreeableness, and risk are significantly and positively related to an investor's investment intention, whereas neuroticism is negatively related to an investor's investment intention. The results extended by this study can be used by financial planners and investment bankers to channelize the available financial resources in diversified portfolios. The results will help financial planners to make available diverse investment alternatives for investors in Balochistan, thus catering to their unique needs. Academia must offer courses on contemporary finance paradigm based on behavioral finance to enable future business graduates to make wise financial decisions.

• Science of Emotion and Sensibility
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• v.19 no.3
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• pp.15-26
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• 2016

This study was performed to investigate whether aesthetic preference is related to the personality of an individual or not. Even though prior studies have reported that aesthetic preference for a certain style of art is associated with a personality factor, it is more important to focus on impressions and feelings about paintings than the style of art. The present study tried to examine how positive, negative, and neutral feelings about paintings are related to a personality factor, and that familiarity has an effect on aesthetic preference. After participants answered a Big-Five personality questionnaire, they then rated the preference for and familiarity about paintings which implied emotions. The results showed that individuals with higher scores of neuroticism preferred negative paintings. A preference for negative paintings is hard to explain, but this could be explained by results of this study. A hypothesis that familiar paintings would be more preferred is supported by the data. Aesthetic preference has both objectivity and the subjectivity. This study explained subjectivity through individual differences, and investigate art from a psychological point of view rather than conservative methods that sort paintings into art history.