• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• Journal of Korean Academy of Dental Administration
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• v.7 no.1
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• pp.21-28
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• 2019

The purpose of this study was to investigate the association between wrong postures and pain during scaling and encourage dental hygienists and students to exercise scaling in a good position. After obtaining informed consent, 107 students (3rd and 4th grade students) who had an experience with scaling practice were enrolled. The questionnaire included three general items, four items related to the posture during scaling, and nine items related to pain management (total 16 items), for which the five-point Likert scale was used. Through the questionnaire, we examined the preference of posture during scaling, posture education during scaling, pain in each part during scaling, pain management, and pain management method. In the scaling exercise, 86.3% of the subjects were instructed on the correct posture, and 87.9% of the subjects perceived the possibility of inducing musculoskeletal disorders based on the scaling posture. The percentage of subjects who responded that they performed scaling in the correct posture was 33.6% and that of subjects who answered that they bowed or turned their head by more than 15° was 64.4%. Further, 45.7% of the subjects answered that they bent their shoulders, and 29.9% of the subjects answered that their postures were not parallel to the floor. Pain during scaling was still higher when they bent their head, they bent their waist, and they bent their wrist (p<0.05). During scaling, pain was most frequent in the fingers and hands (15%), followed by the neck (14%), shoulders (11.2%), waist (9.3%), and feet and legs (2.8%). The percentage of subjects who performed regular exercise (or stretching) to prevent pain was 29.9% and that of subjects who managed pain after scaling was 12.1%. Further, exercise (24.6%) and self-massage (20.3%) were highly used as the pain management methods, and the school practice was preferred to education media for pain management (79.4%). In the scaling practice, there was a training on pain management, but the frequency of practicing in the wrong posture was high. Moreover, pain increased upon practicing in an incorrect posture. Therefore, more in-depth and systematic education on the necessity and method of musculoskeletal disease management during scaling is required.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.583-589
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• 2021

This study is a study on the development of virtual reality to prepare for the increase in disasters of households living with companion animals due to the increase of companion animals. The increase in single-person households and DINKs(Double Income, No Kid) along with the low birth rate and aging population is raising the risk of disasters caused by companion animals in particular. Among these disasters, there is an increase in the occurrence of fires primarily due to the raising of companion animals. Electric stove fires caused by pets are the most common fires. In particular, the frequency of electric stove fires caused by cats is the highest. Careful precautions by the owner are necessary to reduce fires caused by pets. Parenting of companion animals causes pet loss syndrome due to emotional exchange. There are injuries to pets in escalators and injuries to owners in elevators due to disasters caused by the owner's negligence. In order to reduce injuries on escalators and elevators, basic etiquette for using escalators and elevators with pets is required as basic etiquette. It is necessary to utilize virtual reality to reduce disasters caused by such companion animals. Virtual reality can be experienced without a physical space in advance training to overcome disasters, so real disaster cases can be experienced immersively. Therefore, learning how to reduce fires caused by companion animals, disasters caused by owner's negligence, and petloss syndrome through virtual reality will greatly contribute to disaster prevention and reduction of social costs.

• Journal of Science Education
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• v.45 no.3
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• pp.336-348
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• 2021

The goal of this study is to investigate the current status of implementation of convergence education in elementary, middle, and high schools. A survey was conducted on 871 in-service teachers nationwide, and frequency analysis was conducted by school level. Key findings are as follows: first, 449 (51.5%) are found to practice convergence education. Second, the reason for implementing convergence education is the voluntary effort of teachers and the educational necessity for the future society. Third, it was found that convergence education is being implemented centered on science, arts, and social studies as a link between subjects in regular curriculum hours. Fourth, 270 (64%) of teachers who implemented convergence education in response to COVID-19 performed online convergence education, and experienced difficulties in creating class materials and communicating with students. Fifth, the excessive work of teachers, insufficient support for teacher training and research group activities, and lack of various convergence education programs are suggested as reasons for not implementing convergence education. This study hopes to provide implications for policy and implementation for revitalizing convergence education.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.20-37
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• 2023

Although traffic signal installations are continuously expanding, the effect of preventing traffic accidents remains unverified. Totally, 7,045 traffic accident data (such as signal violations) registered with TCS were manually searched for a 7-year period from 2013 to 2019 for 1,602 traffic signals in Daejeon Metropolitan City. The top 20 traffic accident intersections were identified, the traffic accident investigation records and field maps were viewed to compare the driving direction and signal phase of the violated vehicle, and the cause of the traffic accident was divided into insufficient signal operation design (operation) and driver negligence (intentional). Results of the analysis revealed that 75% of traffic accidents occurred in thru-left-turn traffic signals and overlap; moreover, extending the yellow time or operating all red signals due to countermeasures against traffic accidents occurring in yellow signals resulted in reduced traffic accidents. Data indicated that Permissive Left Turn requires improvement with the signal operation. In addition, since The Korean National Police Agency is not computerized for traffic accident sites and signal-related data, the lack of manpower necessitates improvement and utilization of TCS when establishing traffic accident prevention measures. It is believed that it will contribute to signal operation by analyzing vast amounts of data collected in the field and presenting improvement measures.

• The Korean Journal of Franchise Management
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• v.8 no.2
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• pp.17-24
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• 2017

Purpose - In recent years, research has been conducted on the conflict resolution strategies of the franchise headquarters and the franchisees, but there is a lack of research on how the power structure and cultural factors play a role in resolving conflicts. From this perspective, this study is to examine the structural relationship between franchisors' cultural orientation and relationship power, and conflict resolution strategies, relationship trust, and relationship performance using. The findings of this study suggest how franchise headquarters should establish long-term relationship with franchisees and share information. Research design, data, methodology - The data were collected from April 1 to April 15, 2013. Because this study examined franchise industries from the franchisee perspective, we contacted franchisee store owner and managers located in Seoul and Gyeonggi Province. Interviewers trained contacted a total of 200 franchisees, and 196 franchisees responded. Out of 196 respondents, 13 respondents were deleted due to missing information. Thus, a total of 183 franchisee were used for this study. he data were analyzed using frequency analysis, confirmatory factor analysis, correlation analysis, and structural equational modeling with SPSS 24.0 and Amos 23.0 statistical program. Results - The results showed that cooperation orientation and relational power of franchisor had significant effects on conflict resolution strategies. Cooperating, obliging, and compromising strategies of conflict resolution strategy had significant effects on relationship trust. Also, relationship trust had significant effect on relationship performance. Conclusions - This study shows that the franchise headquarters and the franchisees share necessary information for common purposes and that continuous two-way communications play an important role in resolving conflicts. In other words, the result of this study suggests that if the franchise headquarters and the franchisee actively consider the position of the other party and strive to achieve the goal, conflict resolution may be more successful. In order to do this, the franchise headquarters will have to consider how to build and maintain continuous communication with the franchisees, and continuous education is also needed so that employees can have a cooperative attitude. However, since the culture of these organizations is not made up of simple staff training and is not formed within a short time, the CEO of the franchisee headquarters should take the lead in establishing a cooperative culture with the merchants over the long term.

• The Korean Journal of Franchise Management
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• v.5 no.1
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• pp.143-160
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• 2014

This study focuses that the business starters can refer to this study, select better franchise headquarter and make the franchise member agreement. The most concerned part for the people who want to open franchise shop is what brand is reliable and safe to them. I have analyzed disclosure report that contains overall information of franchise headquarters and researched 300 franchise shops as sample. I drew the conclusion of the decision factors of franchise member agreement, overlooked demographical status by frequency analysis with SPSS 18.0 and performed disperse analysis to examine the decision factors of franchise member agreement and the difference between sex, service type, shop size and income level. In conclusion, the most concerned factor for the franchise agreement is sales management. Sex, shop size and income level are not meaningful factors, but the cost and training management factors are considered differently based on the service type. I hope 1) this study can be utilized for the franchise business starters judge and refer information level provided by the headquarters and make a successful franchise shop business. 2) this study can make solid relationship between franchise members and present a long term vision to them. Finally, this study can be a foundation to promote franchise field through making and supplementing the law of promoting proficient and good franchise headquarters and fairness of franchise transaction and franchise encouragement.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.319-326
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• 2023

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has increased the workload of healthcare workers (HCWs), impacting their health. This study aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and identify factors associated with poor sleep among HCWs in Vietnam during the COVID-19 pandemic. Methods: In this cross-sectional study, 1000 frontline HCWs were recruited from various healthcare facilities in Vietnam between October 2021 and November 2021. Data were collected using a 3-part self-administered questionnaire, which covered demographics, sleep quality, and factors related to poor sleep. Poor sleep quality was defined as a total PSQI score of 5 or higher. Results: Participants' mean age was 33.20±6.81 years (range, 20.0-61.0), and 63.0% were women. The median work experience was 8.54±6.30 years. Approximately 6.3% had chronic comorbidities, such as hypertension and diabetes mellitus. About 59.5% were directly responsible for patient care and treatment, while 7.1% worked in tracing and sampling. A total of 73.8% reported poor sleep quality. Multivariate logistic regression revealed significant associations between poor sleep quality and the presence of chronic comorbidities (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.17 to 5.24), being a frontline HCW directly involved in patient care and treatment (OR, 1.59; 95% CI, 1.16 to 2.16), increased working hours (OR, 1.84; 95% CI,1.37 to 2.48), and a higher frequency of encountering critically ill and dying patients (OR, 1.42; 95% CI, 1.03 to 1.95). Conclusions: The high prevalence of poor sleep among HCWs in Vietnam during the COVID-19 pandemic was similar to that in other countries. Working conditions should be adjusted to improve sleep quality among this population.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.