• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.561-569
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• 2022

A study was conducted to determine whether there is a difference in the incidence of kidney cancer according to income level and the difference in delayed diagnosis. To this end, the incidence of kidney cancer in Korea was analyzed by income level and by stage. From 2015 to 2017, a national kidney cancer cohort was established by linking the KCCR(Korea Central Cancer Registry), NHISS(National health insurance sharing service), and the HIRA(Health insirance review and assessment service) database to calculate the kidney cancer incidence by stage and income level. During the study period, the incidence of kidney cancer in Korea increased in all income deciles, but decreased only in the medical aid population. The incidence of kidney cancer in Korea was 7.35 per 100,000 people, and 83.54% of them were locoregional kidney cancer. In the top 20% of the income decile, there was a high incidence of 21.46 cases per 100,000 people, among which 18.37 cases were locoregional kidney cancer. On the other hand, even after adjusting for risk factors related to kidney cancer, it was confirmed that the lower the income level, the higher the risk of being diagnosed with kidney cancer with distant metastasis (lowest income 20% adj.OR 1.807, 95% CI 1.411-2.222). In the insured population, the risk ratio of being diagnosed with unknown stage was 1.926 (95% CI 1.317, 2.816). The higher the income level, the higher the frequency of early cancer diagnosis, but the lower the income level, the higher the risk of being diagnosed with metastatic kidney cancer or an unknown stage, so health inequality according to income level was observed.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.167-175
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• 2023

In the context of COVID-19, this study was conducted to study how college students' earned grades and average grade point averages changed according to face-to-face and non-face-to-face classes. For this study, grade data was extracted using an access database. For the study, 152 students during the 3rd semester were compared and analyzed the grade point average, average grade point average, midterm exam, final exam, assignment score, and attendance score of students who participated in non-face-to-face and face-to-face classes. As an analysis method, independent sample t-test statistical processing was performed. It was concluded that the face-to-face class students had better grades and average GPA. As a result, the face-to-face class students showed 4.39 points higher than the non-face-to-face class students, and the average grade value was 0.6642 points higher. As a result of the comparative analysis, it was statistically significant, and the face-to-face class averaged 21.22 and the non-face-to-face class had 16.83 points. In conclusion, it was confirmed that face-to-face students' grades were generally higher than those of non-face-to-face students, and that face-to-face students showed higher participation in class.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1249-1258
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• 2022

Marine debris is defined as a substance that is intentionally or inadvertently left on the shore or is introduced or discharged into the ocean, which has or is likely to have a harmful effect on the marine environments. In this study, the detection of marine debris and the analysis of the amount of change on marine debris were performed using the object detection method for an efficient method of identifying the quantity of marine debris and analyzing the amount of change. The study area is Yuho Mongdol Beach in the northeastern part of Geoje Island, and the amount of change was analyzed through images collected at 15-minute intervals for 32 days from September 12 to October 14, 2022. Marine debris detection using YOLOv5x, a one-stage object detection model, derived the performance of plastic bottles mAP 0.869 and styrofoam buoys mAP 0.862. As a result, marine debris showed a large decrease at 8-day intervals, and it was found that the quantity of Styrofoam buoys was about three times larger and the range of change was also larger.

• PNF and Movement
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• v.20 no.3
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• pp.307-319
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• 2022

Purpose: This study aimed to investigate the perceptions of tele-physical therapy of health and non-health majors. It can provide basic research data for the provision of medical services in the future by identifying the level of awareness of the need for tele-physical therapy and the factors that affect it. Methods: The subjects were adults aged 20 to 29 in Korea, with 199 participants consisting of 83 health majors and 116 non-health majors. The survey was conducted over a period of 14 days. The survey comprised 19 questions, including 10 questions about general characteristics and 9 questions about tele-physical therapy recognition. The results were statistically analyzed using a statistical package program. Results: There was a significant difference between the two groups regarding the recognition of tele-physical therapy, with an average of 2.64 points in health majors and an average of 1.71 points in non-health majors, showing a low overall score. There was no significant difference in perception of the necessity of tele-physical therapy, with an average of 3.71 points in health majors and an average of 3.49 points in non-health majors, showing a high score, which was defined as a score of 3 or higher. Conclusion: Health and non-health majors showed low awareness of tele-physical therapy. A high level of perceived necessity for tele-physical therapy was shown. In the perception of tele-physical therapy in health majors, 'awareness', 'health improvement', and 'convenience' affect the perception of the necessity of tele-physical therapy. In non-health majors, 'knowledge and skills', 'health improvement', 'expected treatment satisfaction', and 'resolving restrictions on hospital visits' affect the perception of the necessity of tele-physical therapy.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.77-84
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• 2022

In this paper, we propose a system for monitoring air pollutants such as fine dust using an unmanned aerial vehicle capable of autonomous navigation. The existing air quality management system used a method of collecting information through a fixed sensor box or through a measurement sensor of a drone using a control device. This has disadvantages in that additional procedures for data collection and transmission must be performed in a limited space and for monitoring. In this paper, to overcome this problem, a GPS module for location information and a PMS7003 module for fine dust measurement are embedded in an unmanned aerial vehicle capable of autonomous navigation through flight information designation, and the collected information is stored in the SD module, and after the flight is completed, press the transmit button. It configures a system of one-stop structure that is stored in a remote database through a smartphone app connected via Bluetooth. In addition, an HTML5-based web monitoring page for real-time monitoring is configured and provided to interested users. The results of this study can be utilized in an environmental monitoring system through an unmanned aerial vehicle, and in the future, various pollutants measuring sensors such as sulfur dioxide and carbon dioxide will be added to develop it into a total environmental control system.

• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.119-137
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• 2021

This study aimed to develop a teaching-learning process plan for the 'adolescent nutrition and dietary behavior' unit of middle school technology and home economics through blended learning teaching method. "Analysis-Design-Development-Evaluation and Revision" model developed by Korea Institute of Curriculum and Evaluation(KICE) was applied to developing the teaching-learning process plan. The authors analyzed subject contents suitable for blended learning, and then designed a teaching-learning process plan by selecting the topics, developing the teaching strategies, and deciding on the media and evaluation tools for each class. Based on the plan for each week, the final version of the teaching-learning process plan, handouts for activities, and evaluation tools were developed. The teaching-learning process plan was revised and supplemented based on the expert verification results. The developed teaching-learning process plan which applied blended learning method was considered suitable for the current curriculum, and the group presentation activities implimented in the online classes were found to encourage learners' participation and interest. Also, the developed teaching-learning process plan could be used in the online only environment without any issues depending on the intention of the classes, by the appropriate use of distance learning tools such as Paddles or Thinkerbells. The developed teaching-learning course plan is expected to be effectively used in either online or blended learning environment, as a means of helping adolescent students improve their dietary life.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.325-340
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• 2022

Amid the unprecedented situation of COVID-19 around the world, online education has established itself as an essential element in the era of zero contact and the importance of various content and changes of the system that are appropriate for the era of the 4^th industrial revolution has increased. Although universities are making their efforts to combine ICT technologies and design and achieve new systems, the recognition and atmosphere for establishing the cloud computing system are falling short. The purpose of this research importance of success factors of "Building a cloud computing system of cyber university in Korea" by classifying the work characteristics and scale, and to derive and analyze the importance cloud rankings considering the organization and individual dimension. Therefore, this study has drawn 14 major factors in the previous researches and models through the survey on experts with knowledge related to the cloud computing. The analysis was conducted to see what differences there are in factors for the successful establishment of the cloud computing system using AHP. It is expected that the factors for success presented through this study would be used as systemic strategies and tools for the purpose of drawing factors for the success of establishing the private cloud computing system for the higher education institutions and public information systems.