• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.167-173
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• 2021

The purpose of this study is to discuss the types of algorithms and data categories in AI education for elementary school students. The study surveyed 11 pre-elementary teachers after providing education and practice on various data, artificial intelligence algorithm, and AI education platform for 15 weeks. The categories of data and algorithms considering the elementary school level, and educational tools were presented, and their suitability was analyzed. Through the questionnaire, it was concluded that it is most suitable for the teacher to select and preprocess data in advance according to the purpose of the class, and the classification and prediction algorithms are suitable for elementary AI education. In addition, it was confirmed that Entry is most suitable as an AI educational tool, and materials that explain mathematical knowledge are needed to educate the concept of learning of AI. This study is meaningful in that it specifically presents the categories of algorithms and data with in AI education for elementary school students, and analyzes the need for related mathematics education and appropriate AI educational tools.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.321-326
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• 2021

In this paper, the information education plan of the Ministry of Education and offices of education was analyzed in relation to the construction of the AI convergence education room. And based on this, an appropriate construction direction was suggested. For research, AI convergence education room construction plan, budget amount per school and etc were analyzed in the information education plan of the Ministry of Education, KERIS, KOFAC, and the plan of leading schools for AI education. Although the Ministry of Education is making various efforts to promote SW education and artificial intelligence convergence education, it is found that the number is far insufficient compared to the number of elementary, middle and high schools. Efforts to expand the information education room at the government level are continuously required, and in order to utilize the developed artificial intelligence convergence education room, it is necessary to provide abundant teaching and learning examples for the construction space model. In addition, school-level planning and execution must be followed so that the constructed space can be used appropriately, and management and maintenance plans for equipment are required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.52-54
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• 2021

With the advent of the era of artificial intelligence, robot education and teaching under its empowerment have been widely concerned and applied worldwide. The purpose of this study: systematically evaluate the application effect of AI educational robots in student education and teaching; the method of this study: use the computer to search for relevant education in the search tools such as "Web of Science", "CNKI", "ERIC", "IEEE" A comparative study of the effects of robot teaching and traditional teaching. The retrieval time is from January 2000 to January 2020. Comprehensive MetaAnalysis 2.0 was used for Meta analysis. The results of this study: A quantitative analysis of the 31 valid research literatures included, and an objective evaluation of the effect of the meta-analysis on AI educational robots. The analysis results show that the combined effect of AI educational robots on student learning effects is 0.465 This indicates that educational robots have a moderately positive effect on students 'learning effectiveness. The conclusion of this study: The application effect of AI educational robots in student education and teaching is better than traditional education methods, which can better promote student learning.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.533-535
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• 2021

The differences between this study and previous studies are as follows. First, by building a cloud-based system using IoT technology, the system was built to monitor the status of buildings in real time from anywhere with an internet connection. Second, a model for predicting the future was developed using artificial intelligence (LSTM) and statistical (ARIMA) methods for the measured time series sensor data, and the effectiveness of the proposed prediction model was experimentally verified using a scaled-down building model. Third, a method to analyze the condition of a building more three-dimensionally by visualizing the structural deformation of a building by convergence of multiple sensor data was proposed, and the effectiveness of the proposed method was demonstrated through the case of an actual earthquake-damaged building.

• Convergence Security Journal
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• v.22 no.5
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• pp.11-20
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• 2022

As the Internet and web technology develop around mobile devices, image data contains various types of sensitive information such as people, text, and space. In addition to these characteristics, as the use of SNS increases, the amount of damage caused by exposure and abuse of personal information online is increasing. However, research on de-identification technology based on multi-type object detection for personal information protection is insufficient. Therefore, this paper proposes an artificial intelligence model that detects and de-identifies multiple types of objects using existing single-type object detection models in parallel. Through cutmix, an image in which person and text objects exist together are created and composed of training data, and detection and de-identification of objects with different characteristics of person and text was performed. The proposed model achieves a precision of 0.724 and mAP@.5 of 0.745 when two objects are present at the same time. In addition, after de-identification, mAP@.5 was 0.224 for all objects, showing a decrease of 0.4 or more.

• The Journal of Bigdata
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• v.7 no.1
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• pp.37-47
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• 2022

Autonomous delivery operation data is the key to driving a paradigm shift for last-mile delivery in the Corona era. To bridge the technological gap between domestic autonomous delivery robots and overseas technology-leading countries, large-scale data collection and verification that can be used for artificial intelligence training is required as the top priority. Therefore, overseas technology-leading countries are contributing to verification and technological development by opening AI training data in public data that anyone can use. In this paper, 326 objects were collected to trainn autonomous delivery robots, and artificial intelligence models such as Mask r-CNN and Yolo v3 were trained and verified. In addition, the two models were compared based on comparison and the elements required for future autonomous delivery robot research were considered.

• Industry Promotion Research
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• v.8 no.1
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• pp.55-68
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• 2023

The purpose of this study is to investigate the factors that influence the intention to continuously use the RPA program used in the financial industry for those working in the financial industry. In particular, the purpose of this study is to understand the will to accept and the perception of acceptance conflict by considering the characteristics of individuals in the relationship between work and information technology. As a result of the study, it can be confirmed that the RPA system based on intelligent process automation including artificial intelligence should be further strengthened in the transformation of a digitalized enterprise rather than the RPA based on simple task automation that is currently most used. In general, the phenomenon of cognitive dissonance was prominent for the adoption of new technology, but the phenomenon of cognitive dissonance did not appear for the continued use of RPA in the financial industry. Able to know. In the future in the financial industry, it is thought that the change in the labor organization will be accelerated as the suitability of repetitive tasks and technologies is increased.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.205-211
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• 2023

In this study, we analyzed the potential application and validity of ChatGPT, an artificial intelligence technology currently gaining attention across various fields, for the creation of advertising photographs. To do this, we examined the relationship between the visual elements of advertising photographs and language, and investigated use cases of ChatGPT in advertising. Furthermore, we analyzed the elements of each stage in the advertising photograph creation process and conducted expert interviews to determine the validity of ChatGPT's application in these stages. The results revealed that, although somewhat limited, the feasibility of using ChatGPT was found to be high in the planning stage of advertising photographs, but lower in the actual shooting and post-production stages. Considering these findings, it is necessary to continuously monitor the progress of AI technology and strive to enhance the creativity and efficiency of advertising photograph production through collaboration between technology and humans.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.209-216
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• 2023

In this paper, artificial intelligence agents ChatGPT and Stable Diffusion were used to explore the possibility of educational use by implementing a program to recommend picture materials for teaching and learning according to the class topic entered by teachers. The average time spent recommending all picture materials is about 6 minutes. In general, pictures related to keywords were recommended, and the letters in the recommended pictures could only know the intention to represent the letters, and the letters could not be recognized and the meaning could not be known. However, further research seems to be needed on the fact that the type or content of the recommended picture depends entirely on the response of ChatGPT and that it is not possible to accurately recommend the picture for all keywords. In addition, it was concluded that it is true that the recommended picture is related to the keyword, but the evaluation of whether it has educational value is the subject of discussion that should be left to the judgment of human teachers.