• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.91-99
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• 2011

Adult-learners of online education have been distant from studying for a long time and are rather familiar with the old massive education. As a result, despite the excellent ability in self-control learning that results in great academic performances and academic continuity, the learners give up on learning, feeling depressed for not being able to perform their advantage, the self-control learning ability. The study developed a self-control learning model and UDL design learning model in order to establish e-mentoring system. For the analysis on results of the experiment, the researcher extracted academic grade, the rate of re-register, and the amount of total studying time after dividing new students and transfer students of H cyber university into the control group and the experimental group. In conclusion, the results showed that more satisfied the control group is, the higher academic achievements and the higher academic continuity are accomplished.

• The Journal of Korean Medicine
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• v.42 no.3
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• pp.153-164
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• 2021

Objectives: The aim of this study is to investigate the effect of Heat-sensitive Moxibustion on Benign Prostatic Hyperplasia Methods: We searched articles from Academic Journals(CAJ) online databases, Oriental Medicine Advanced Searching Integrated System (OASIS), Searching key words were '前列腺增生', '熱敏灸' and '열민구', '전립선비대'. The search range included randomized controlled trials (RCTs). Among the articles published to 2020, 10 articles were found. After review the title, abstract and original, 3 articles were selected finally to rule out treatment combined with completely different treatments. Result: The Heat-sensitive moxibustion at acupoints in the treatment of Benign prostatic hyperplasia were significantly superior to control group after treatment in the symptoms of patients, IPSS, QOL, PVR and Qmax(P<0.05). The Heat-sensitive moxibustion can significantly reduce the incidence of temporary urinary incontinence after Transurethral resection of the prostate(TURP) and improve life quality and satisfaction of patients(P<0.05). The individualized desensitization saturated time and amount of Heat-sensitive moxibustion is superior effective to general amount and time of traditional moxibustion in the total effective rate, IPSS, Ru and Qmax(P<0.01) for Benign prostatic hyperplasia. Conclusion: Heat sensitive moxibustion directly transfer heat to the source of a disease. So it can be considered as a good treatment for Benign prostate hypertrophy. It was also shown a better effect on BPH compared to traditional moxibustion, According to the thermo principles of tumor, if the tumor cell's death temperature of 43℃ is reached, that can cause tumor degeneration. Therefore I think Heat sensitive moxibustion can be applied to various tumor disease. The results of this study could be applied to clinical treatment of BPH. However, additional large-scale clinical researches should be conducted.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.55-61
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• 2024

In response to the COVID-19 pandemic, the logistics industry in Korea has rapidly been expanding, with offline demand concentrating on online platforms owing to the development of digital infrastructure. This has increased the workload of courier drivers considerably, along with labor intensity. A delivery driver died recently from overwork due to the continuous increase in delivery volume, which raises social concerns. Delivery drivers work long hours, (over 12 hours) and are greatly affected by weather conditions, such as snow, rain, heat waves, and cold waves. In addition, they lack a fixed workplace; perform atypical work handling workpieces of various sizes, weights, and shapes; and spend a large amount of time driving as part of their work. This work involves a high level of tension and requires attention and concentration. Despite the frequency of industrial accidents in the courier industry, studies on safety and health to quantitatively analyze and systematize the work of courier workers are very scarce. Therefore, to define the work process necessary for investigating the harmful factors in delivery service and the work analysis, this study conducted interviews and on-site surveys to analyze the unit work of the delivery service by targeting delivery workers. In other words, a framework of unit work for work analysis was presented to enable research and analysis by considering the aforementioned characteristics of the courier industry. The process was broadly divided into work, transport, storage, delay, and inspection. Work was divided into loading, sorting, unloading, and door subcategories, and transportation was divided into vehicle, cart, and walking subcategories as well as 10 small processes. Moreover, 22 unit works were again drawn by conducting field surveys and interviews. The risk of unit work derived from this study was ergonomically evaluated, and the ergonomic analysis revealed that uploading and transportation were the most dangerous. The results of this study could be used as basic data for preventing industrial accidents among courier workers, whose work has increased with the logistics volume and the development of the logistics industry.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.769-779
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• 2017

Nutrient (i.e., nitrogen and phosphorus) budgets are required under a 'Livestock Excreta Survey'. A nutrient budget is one of the agri-environmental indicators that calculates the difference between the inputs and outputs of the amount of nutrients within a certain boundary and for a certain time period (e.g., 1 year). In this study, a nutrients budget model was developed to effectively determine the surplus of nutrients within a region in Korea. The C# program language was used in order to facilitate the deployment of a graphical user interface (GUI) and to enhance compatibility. Also, the model was developed on Windows OS, which is the commonly used operating system in Korea. The model was based on the OECD/Eurostat nutrient budget method, and it was modified to consider manure composting procedures as well. There are key features of the nutrient budget model, including directly use of the original data sets from various input and output sources, and a collectively exchange of the address in different formats. The model can quickly show the results of various spatial and temporal resolutions with the same data, as well as perform a sensitivity analysis with coefficients and easily compareresults using tables and graphs. Further, it would be necessary to study the extension of the scope of utilization, such as the application of various nutrient budget methods. It would also be helpful to investigate both pre and postprocessing information such as linking input data through online systems.

• Nutrition Research and Practice
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• v.15 no.sup1
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• pp.53-69
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• 2021

BACKGROUND/OBJECTIVES: To determine the weight change trend among the adult Turkish population after 1 yr of the coronavirus disease 2019 (COVID-19) pandemic and factors associated with weight change. MATERIALS/METHODS: This cross-sectional study was conducted between 26 February and 6 March 2021 using an online questionnaire that included questions for sociodemographic variables, eating habits, stress level, and the Three-Factor Eating Questionnaire-R18. Those who weighed themselves 1-2 weeks before the pandemic was declared in Turkey and remembered their weight were invited to participate in the study. Trends in weight and body mass index (BMI) change were calculated. The variables associated with a 1% change in BMI were assessed using hierarchical regression analysis. RESULTS: The study was conducted with 1,630 adults (70.25% female) with a mean age of 32.09 (11.62) yrs. The trend of weight change was found to increase by an average of 1.15 ± 6.10 kg (female +0.72 ± 5.51, male +2.16 ± 7.22 kg) for the first year of the COVID-19 pandemic. The rate of participants with a normal BMI (18.50-24.99 kg/m²) decreased to 51.91% from 55.75%. Consuming an "Increased amount of food compared to before the pandemic" was found to be the independent variable that had the strongest association with a 1% increase in BMI (β = 0.23 P < 0.001). The average change in the BMI was higher in older individuals than in those who were younger. A high stress level was associated with a decrease in BMI (β = -0.04 P = 0.048). CONCLUSIONS: In this study, the factors associated with weight change after 1 yr of the pandemic in the Turkish population was reported for the first time. A high stress level and increased weight gain trend still occur in Turkey after 1 yr of the pandemic.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.250-257
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• 2018

Today, the integration of the rapid development of ICT and the smart devices moves our lives quickly into an online community environment through not only quick and easy information search but also various social media. Accordingly, individual activities in the smart media environment are pouring out vast quantities of data in many fields, accumulating a tremendous amount of data. The everyday data of individuals is reproducing different values from the previous ones, while suggesting new customized services that utilize them in various fields. Recently, big data utilization has attracted a great attention in the field of healthcare. Especially, development of healthcare service linked with mobile is expected to bring a new paradigm in this field. In this study, creation of a 3D avatar character model as a means to transfer information to individuals more efficiently is proposed in the development of mobile customized service for health promotion and growth prediction of children and adolescents, at the same time, an effective visual expression method to have a sense of immersion and unity is searched.

• Information Systems Review
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• v.26 no.2
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• pp.185-203
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• 2024

This study empirically explores the influence of gamification elements and privacy concerns on users' intention to use fitness apps, based on the technology acceptance model (TAM). This research classifies gamification in fitness apps into three categories: achievement-related elements, social-related elements, and immersion-related elements. Although previous research investigated the gamification of fitness apps, few studies combined the impact of gamification with privacy concerns. Considering the significant amount of sensitive personal data collected by fitness apps, we recognize the importance of data privacy and aim to address this gap in research. To achieve this goal, we collected and analyzed data from 187 Korean fitness app users through an online questionnaire. The results confirm the highly significant influence of perceived ease of use, perceived usefulness, and achievement-related gamification elements. Social-related gamification elements, immersion-related gamification elements, and privacy concerns however show insignificant results for the intention to use fitness apps in the Korean market. Location and time limit the generalizability of this study; however, the findings of this study nonetheless offer valuable insights for practitioners and developers to enhance the design and development of their applications.