• Journal of Adhesion and Interface
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• v.15 no.1
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• pp.22-30
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• 2014

Self-photoinitiating acrylate (SPIA) which can undergo self-initiation under UV irradiation was synthesized by a Michael addition in the presence of a base catalyst. The SPIA polymerizations were investigated by photo-differential scanning calorimeter (photo-DSC) and surface physical properties such as pendulum hardness and pencil hardness. The results showed that the SPIA can cure upon UV irradiation by itself without a photoinitiator. But we found out that both the curing rate and the conversion were too low for the self-curing reaction of SPIA. In order to improve the SPIA curing properties, we introduced the SPIA/cationic hybrid system and observed the effects of the addition of commercial free radical type monomer and photoinitiator on the curing behaviors. SPIA/cationic hybrid system was the best suitable to improve the SPIA curing properties. The kinetic analysis indicated that the cationic monomer and photoinitiator apparently accelerated the cure reaction and rate of the hybrid SPIA system, mostly due to the synergistic effect of cationic monomer and photoinitiator increasing the mobility of active species and the generation of reactive species (free radical, cation) during the photopolymerization process. The physical properties showed that, unlike typical free radical system, the hybrid systems did not show oxygen inhibition effect because of cationic reaction on the coating surface.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.234-240
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• 2007

The purpose of this paper was to architecture optimal model of the scooped swing in high bar. The scooped swing was modeled to the double pendulum and was simulated with the Lagrange's equation of motion. Lagrange's method based on a energy approaching method was implemented as a equation of motion. The subject was a national man-gymnast(age 18yrs, height 153 cut mass 48 kg) and the high bar of SPIETH company was used to measure the scooped swing. Qualisys system(six MCU-240 cameras, QTM software)was used to capture data for imaging analysis. The solution of a model and data processing were solved in Mathematica5.0. The results were as follows: First model value of maximum bar displacement was longer than experimental value, that is, 0.02 m. Second, both angular pattern of segment1(HAT) had a increasing curve but curve patterns had a different concave and convex me. Third the experimental value of maximum angular angle of segment2(total leg) had larger than model value, that is, $4^{\circ}$. Conclusively, model parameters were quasi-optimized to obtain a quasi-match between simulated and actual performances. It hopes to simulate a human model by means of integrating musculoskeletal and neuromuscular system in the future study.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.1
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• pp.76-84
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• 2022

The study was conducted to analyze the impacts of COVID-19 on the number of dental trauma patients. Based on the data provided by the Health Insurance Review and Assessment Service's Healthcare Bigdata Hub, dental trauma which occurred from 2010 to 2020 was analyzed. Since the outbreak of COVID-19, the incidence of dental trauma per 100,000 decreased compared to the average Incidence between 2017 to 2019. By age, it decreased by 5.4% (p = 0.017) for 0 - 4 years old, 30.3% (p < 0.001) for 5 - 9 years old, 39.5% (p < 0.001) for 10 - 14 years old, 14.5% (p = 0.002) for 15 - 19 years old, 1.3% for 20 - 29 years old, 0.2% for 40 - 49 years old, 2.7% for 50 - 59 years old, 1.2% for 60 years old or older, but it increased by 2.5% for 30 - 39 years old. Compared monthly, before and after the outbreak of COVID-19 confirmed patients, the number of dental trauma patients dropped sharply. After the outbreak of COVID-19, the incidence of dental trauma decreased significantly for under 20 years old, but the decrease was not significant for 20 years old or older.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.451-463
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• 2022

Whenever a mineshaft accidentally collapses, speedy risk assessment is both required and crucial. But onsite safety diagnosis by humans is reportedly difficult considering the additional risk of collapse of the unstable mineshaft. Generally, drones equipped with high-speed lidar sensors can be used for such inspection. However, the drone technology is restrictively applicable at very shallow depth, failing in mineshafts with depths of hundreds of meters because of the limit of wireless communication and turbulence inside the mineshaft. In previous study, a three-dimensional (3D) scanning system with a single channel lidar was fabricated and operated using towed cable in a mineshaft to a depth of 200 m. The rotation and pendulum movement errors of the measuring unit were compensated for by applying the data of inertial measuring unit and comparing the similarity between the scan data of the adjacent depths (Kim et al., 2020). However, the errors grew with scan depth. In this paper, a multi-channel lidar sensor to obtain a continuous cross-sectional image of the mineshaft from a winch system pulled from bottom upward. In this new approach, within overlapped region viewed by the multi-channel lidar, rotation error was compensated for by comparing the similarity between the scan data at the same depth. The fabricated system was applied to scan 0-165 m depth of the mineshaft with 180 m depth. The reconstructed image was depicted in a 3D graph for interpretation.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.57-79
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• 2022

COVID-19, which started in December 2019, has had a great impact on our lives in general, including politics, economy, society, and culture, and activities in sports and arts have also been significantly reduced. In the case of sports, sports for all fields in which ordinary citizens participate were particularly affected, and cases of infection in places closely related to people's lives, such as gyms, table tennis, and badminton clubs, also amplified the social fear of the spread of COVID-19. Therefore, in this study, we analyzed news articles related to sports for all at the time when COVID-19 was first spread, and investigated what issues were emerging and being discussed in the sports for all field under the COVID-19 situation. Specifically, we collected news articles dealt with sports for all issues under the COVID-19 situation from Korea's leading portal news sites and identified key sports for all issues by performing topic modeling on these articles. Through the analysis, we found meaningful issues such as COVID-19 outbreak in sports facilities and support for sports activities. In addition, through wordcloud analysis of these major issues, we visually understood the issues and identified the changes in these issues over time.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• Korean Journal of Applied Biomechanics
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• v.12 no.2
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• pp.279-293
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• 2002

In the study the subjects who 10 university golfers act, and the kinetic factors were analyzed by the ground reaction system. the conclusion are as follows. 1) In the golf putting swing, the ground reaction factors of sagital plane in aspect are showen that the left and right foot sufficient difference, in the level of p <.05. 2) In the golf putting swing, the ground reaction factors of frontal plane in aspect is showen that the left foot has no significant difference in AD BS in the level of p < .05. In success, IP, FS. It can show significant difference. In addition, the right foot is shown the success, There is significant difference. 3) In the golf putting swing, the ground reaction factors of the vertical plane in aspect are shown that the left foot has no significant difference in BS, FS in the level p < .05. In success, AD, IP. It can show significant difference. In addition, the right foot is shown the success, There is significant difference. 4) In the golf putting swing, the ground reaction factors of torque in aspect are shown that the left foot had no significant difference in BS in the level p < .05. In success, AD, IP, FS. It can show significant difference. In addition, the right foot has no significant difference in IP in the level p < .05. AD, BS, FS. There is significant difference. The summarized conclusions are as follows. The first that the power of sagital plane needs the motion which can get the good power change in the stabilized pose. The second is that the small motion can make good putting in stabilized pose. The third is that the body weight move to the direction of the ball. The fourth is that the putting which looks perfect oscillation is good motion.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.