• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.283-291
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• 2021

In this paper, we propose the development and application of a software education program for underachievers. The software education program for underachieving students was developed in consideration of the characteristics of learner's suffering from underachievement and the educational effects of software education, and is meaningful in that it proposes a plan to improve the learning gap in distance learning. Learners can acquire digital literacy and learning skills by solving structured tasks in the form of courseware, intelligent tutoring, debugging, and artificial intelligence learning models in educational programs. Based on the effects of software education, such as enhancing logical thinking ability and problem solving ability, this program provides opportunities to solve fusion tasks to underachievers. Based on this, it is expected that it can have a positive effect on the overall academic work.

• Human Ecology Research
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• v.59 no.1
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• pp.23-43
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• 2021

This study looked through the text mining analysis to check the status of the virtual assistant service, and explore the needs of consumers, and present consumer-oriented directions. Trendup 4.0 was used to analyze the keywords of AI services in Online News and social media from 2016 to 2020. The R program was used to collect consumer comment data and implement Topic Modeling analysis. According to the analysis, the number of mentions of AI services in mass media and social media has steadily increased. The Sentimental Analysis showed consumers were feeling positive about AI services in terms of useful and convenient functional and emotional aspects such as pleasure and interest. However, consumers were also experiencing complexity and difficulty with AI services and had concerns and fears about the use of AI services in the early stages of their introduction. The results of the consumer review analysis showed that there were topics(Technical Requirements) related to technology and the access process for the AI services to be provided, and topics (Consumer Request) expressed negative feelings about AI services, and topics(Consumer Life Support Area) about specific functions in the use of AI services. Text mining analysis enable this study to confirm consumer expectations or concerns about AI service, and to examine areas of service support that consumers experienced. The review data on each platform also revealed that the potential needs of consumers could be met by expanding the scope of support services and applying platform-specific strengths to provide differentiated services.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.65-70
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• 2024

As the interest in AI education grows, researchers have made efforts to implement AI education programs. However, research targeting pre-service teachers has been limited thus far. Therefore, this study was conducted to develop an AI literacy education program for preservice secondary teachers. The research results revealed that the weekly topics included the definition and applications of AI, analysis of intelligent agents, the importance of data, understanding machine learning, hands-on exercises on prediction and classification, hands-on exercises on clustering and classification, hands-on exercises on unstructured data, understanding deep learning, application of deep learning algorithms, fairness, transparency, accountability, safety, and social integration. Through this research, it is hoped that AI literacy education programs for preservice teachers will be expanded. In the future, it is anticipated that follow-up studies will be conducted to implement relevant education in teacher training institutions and analyze its effectiveness.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.1
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• pp.89-103
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• 2023

The purpose of this study was to measure bone age using an artificial intelligence program based on the Greulich-Pyle (GP) method to find out the bone age corresponding to each stage of cervical vertebral maturation (CVM) and the middle phalanx of the third finger (MP3). This study was conducted on 3,118 patients who visited pediatric dentistry at Kyung Hee University Dental Hospital and Pusan National University Dental Hospital from 2013 to 2021. The CVM stage was divided into 5 stages according to the classification by Baccetti, and the MP3 stage was divided into 5 stages according to the methods of Hägg and Taranger. Based on the GP method, bone age was evaluated using an artificial intelligence program. The pubertal growth spurt in the CVM stage was CVM II and III. The mean bone age in CVM II was 11.00 ± 1.81 years for males and 10.00 ± 1.49 years for females, and in CVM III, 13.00 ± 1.46 years for males and 12.00 ± 1.44 years for females (p < 0.0001). The pubertal growth spurt in the MP3 stage was MP3 - G stage. The bone age at the MP3 - G stage was 13.14 ± 1.07 years for males and 11.40 ± 1.09 years for females (p < 0.0001). Bone age evaluation using artificial intelligence is worth using in clinical practice, and it is expected that a faster and more accurate diagnosis will be possible.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.2
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• pp.37-43
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• 2007

Coordination of multiple UAVs is an essential technology for various applications in robotics, automation, and artificial intelligence. In general, it includes 1) waypoints assignment and 2) trajectory generation. In this paper, we propose a new method for this problem. First, we modify the concept of the standard visibility graph to greatly improve the optimality of the generated trajectories and reduce the computational complexity. Second, we propose an efficient stochastic approach using simulated annealing that assigns waypoints to each UAV from the constructed visibility graph. Third, we describe a method to detect collision between two UAVs. FinallY, we suggest an efficient method of controlling the velocity of UAVs using A* algorithm in order to avoid inter-UAV collision. We present simulation results from various environments that verify the effectiveness of our approach.

• Annual Conference of KIPS
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• 2023.11a
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• pp.481-482
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• 2023

양자 컴퓨팅 환경에서 빅데이터를 이용하는 Quantum Artificial Intelligence(QAI)는 빠른 계산 속도를 추구한다. 최근 금융, 물류, 교통 분야의 QAI 모델과 이미지 분류용 quantum convolutional neural network가 소개됐지만 아직 완벽한 성능은 달성하지 못했다. 본 논문은 성능 향상을 위한 모듈을 새로 제시하고, 이를 소형 양자 컴퓨터에 적용하며 하이브리드 모델 구성을 가능하게 한다. 실험 결과, 제안하는 방법은 기존 네트워크와 비교해 우수한 성능을 보였다.

• Korean Journal of Artificial Intelligence
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• v.1 no.1
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• pp.4-6
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• 2013

In the era of information society, IT industry has been developed very much. New technology has made appearance in citizens' lives. IOT (Internet of Things) has grown up the most rapidly in IT industry. Kevin Ashiton, MIT specialist, said, "Loading of FRIS and other sensors shall build Internet of things." Internet of things is said to let things have sensor and communication module and to exchange information and communicate each other. In this study, Internet of things has been applied to flowerpot to build automatic flowerpot control system that turns fan ON and supplies water depending upon temperature and moisture. Users are difficult to cognize temperature and humidity of flower pot correctly. In this study, an experiment obtained correct value of temperature and humidity to build control system. At the performance test of flower pot, commands turned ON depending upon temperature and humidity. Control system should be added to control water supply quantity and time objectively according to servo motor control. Purpose of further study was to control flower pot by remote system in connection with smart phone application. An application control can make not only temperature and humidity statistics but also server depending upon users' needs to turn fan ON and take actions and to control flower pot.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.268-270
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• 2019

In this paper, we analyzed the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers. As a result of analysis, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been utilizing the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.785-790
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• 2019

Convergence and advancement between technologies such as Artificial Intelligence, Big Data, and the Internet of Things have a significant impact on the regional flagship industry. All technical fields are used as a converged technology by connecting between technology and industry. In order to understanding the recent technical trend, it is possible to easily realized the technical trend research and analysis through keyword search using patent information. The purpose of this study is to identify patent trends applied to convergence technology in the 4th Industrial Revolution age in radiation technology development and to present patent trends and analysis for strengthening and utilizing radiation-related industrial technology competitiveness and to apply them to demand technology and forecast future promising technologies.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.